Analysis of small sample size studies using nonparametric bootstrap test with pooled resampling method.

PubMed

Dwivedi, Alok Kumar; Mallawaarachchi, Indika; Alvarado, Luis A

2017-06-30

Experimental studies in biomedical research frequently pose analytical problems related to small sample size. In such studies, there are conflicting findings regarding the choice of parametric and nonparametric analysis, especially with non-normal data. In such instances, some methodologists questioned the validity of parametric tests and suggested nonparametric tests. In contrast, other methodologists found nonparametric tests to be too conservative and less powerful and thus preferred using parametric tests. Some researchers have recommended using a bootstrap test; however, this method also has small sample size limitation. We used a pooled method in nonparametric bootstrap test that may overcome the problem related with small samples in hypothesis testing. The present study compared nonparametric bootstrap test with pooled resampling method corresponding to parametric, nonparametric, and permutation tests through extensive simulations under various conditions and using real data examples. The nonparametric pooled bootstrap t-test provided equal or greater power for comparing two means as compared with unpaired t-test, Welch t-test, Wilcoxon rank sum test, and permutation test while maintaining type I error probability for any conditions except for Cauchy and extreme variable lognormal distributions. In such cases, we suggest using an exact Wilcoxon rank sum test. Nonparametric bootstrap paired t-test also provided better performance than other alternatives. Nonparametric bootstrap test provided benefit over exact Kruskal-Wallis test. We suggest using nonparametric bootstrap test with pooled resampling method for comparing paired or unpaired means and for validating the one way analysis of variance test results for non-normal data in small sample size studies. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

How Many Subjects are Needed for a Visual Field Normative Database? A Comparison of Ground Truth and Bootstrapped Statistics.

PubMed

Phu, Jack; Bui, Bang V; Kalloniatis, Michael; Khuu, Sieu K

2018-03-01

The number of subjects needed to establish the normative limits for visual field (VF) testing is not known. Using bootstrap resampling, we determined whether the ground truth mean, distribution limits, and standard deviation (SD) could be approximated using different set size ( x ) levels, in order to provide guidance for the number of healthy subjects required to obtain robust VF normative data. We analyzed the 500 Humphrey Field Analyzer (HFA) SITA-Standard results of 116 healthy subjects and 100 HFA full threshold results of 100 psychophysically experienced healthy subjects. These VFs were resampled (bootstrapped) to determine mean sensitivity, distribution limits (5th and 95th percentiles), and SD for different ' x ' and numbers of resamples. We also used the VF results of 122 glaucoma patients to determine the performance of ground truth and bootstrapped results in identifying and quantifying VF defects. An x of 150 (for SITA-Standard) and 60 (for full threshold) produced bootstrapped descriptive statistics that were no longer different to the original distribution limits and SD. Removing outliers produced similar results. Differences between original and bootstrapped limits in detecting glaucomatous defects were minimized at x = 250. Ground truth statistics of VF sensitivities could be approximated using set sizes that are significantly smaller than the original cohort. Outlier removal facilitates the use of Gaussian statistics and does not significantly affect the distribution limits. We provide guidance for choosing the cohort size for different levels of error when performing normative comparisons with glaucoma patients.

Confidence limit calculation for antidotal potency ratio derived from lethal dose 50

PubMed Central

Manage, Ananda; Petrikovics, Ilona

2013-01-01

AIM: To describe confidence interval calculation for antidotal potency ratios using bootstrap method. METHODS: We can easily adapt the nonparametric bootstrap method which was invented by Efron to construct confidence intervals in such situations like this. The bootstrap method is a resampling method in which the bootstrap samples are obtained by resampling from the original sample. RESULTS: The described confidence interval calculation using bootstrap method does not require the sampling distribution antidotal potency ratio. This can serve as a substantial help for toxicologists, who are directed to employ the Dixon up-and-down method with the application of lower number of animals to determine lethal dose 50 values for characterizing the investigated toxic molecules and eventually for characterizing the antidotal protections by the test antidotal systems. CONCLUSION: The described method can serve as a useful tool in various other applications. Simplicity of the method makes it easier to do the calculation using most of the programming software packages. PMID:25237618

Epistemic uncertainty in the location and magnitude of earthquakes in Italy from Macroseismic data

USGS Publications Warehouse

Bakun, W.H.; Gomez, Capera A.; Stucchi, M.

2011-01-01

Three independent techniques (Bakun and Wentworth, 1997; Boxer from Gasperini et al., 1999; and Macroseismic Estimation of Earthquake Parameters [MEEP; see Data and Resources section, deliverable D3] from R.M.W. Musson and M.J. Jimenez) have been proposed for estimating an earthquake location and magnitude from intensity data alone. The locations and magnitudes obtained for a given set of intensity data are almost always different, and no one technique is consistently best at matching instrumental locations and magnitudes of recent well-recorded earthquakes in Italy. Rather than attempting to select one of the three solutions as best, we use all three techniques to estimate the location and the magnitude and the epistemic uncertainties among them. The estimates are calculated using bootstrap resampled data sets with Monte Carlo sampling of a decision tree. The decision-tree branch weights are based on goodness-of-fit measures of location and magnitude for recent earthquakes. The location estimates are based on the spatial distribution of locations calculated from the bootstrap resampled data. The preferred source location is the locus of the maximum bootstrap location spatial density. The location uncertainty is obtained from contours of the bootstrap spatial density: 68% of the bootstrap locations are within the 68% confidence region, and so on. For large earthquakes, our preferred location is not associated with the epicenter but with a location on the extended rupture surface. For small earthquakes, the epicenters are generally consistent with the location uncertainties inferred from the intensity data if an epicenter inaccuracy of 2-3 km is allowed. The preferred magnitude is the median of the distribution of bootstrap magnitudes. As with location uncertainties, the uncertainties in magnitude are obtained from the distribution of bootstrap magnitudes: the bounds of the 68% uncertainty range enclose 68% of the bootstrap magnitudes, and so on. The instrumental magnitudes for large and small earthquakes are generally consistent with the confidence intervals inferred from the distribution of bootstrap resampled magnitudes.

Classifier performance prediction for computer-aided diagnosis using a limited dataset.

PubMed

Sahiner, Berkman; Chan, Heang-Ping; Hadjiiski, Lubomir

2008-04-01

In a practical classifier design problem, the true population is generally unknown and the available sample is finite-sized. A common approach is to use a resampling technique to estimate the performance of the classifier that will be trained with the available sample. We conducted a Monte Carlo simulation study to compare the ability of the different resampling techniques in training the classifier and predicting its performance under the constraint of a finite-sized sample. The true population for the two classes was assumed to be multivariate normal distributions with known covariance matrices. Finite sets of sample vectors were drawn from the population. The true performance of the classifier is defined as the area under the receiver operating characteristic curve (AUC) when the classifier designed with the specific sample is applied to the true population. We investigated methods based on the Fukunaga-Hayes and the leave-one-out techniques, as well as three different types of bootstrap methods, namely, the ordinary, 0.632, and 0.632+ bootstrap. The Fisher's linear discriminant analysis was used as the classifier. The dimensionality of the feature space was varied from 3 to 15. The sample size n2 from the positive class was varied between 25 and 60, while the number of cases from the negative class was either equal to n2 or 3n2. Each experiment was performed with an independent dataset randomly drawn from the true population. Using a total of 1000 experiments for each simulation condition, we compared the bias, the variance, and the root-mean-squared error (RMSE) of the AUC estimated using the different resampling techniques relative to the true AUC (obtained from training on a finite dataset and testing on the population). Our results indicated that, under the study conditions, there can be a large difference in the RMSE obtained using different resampling methods, especially when the feature space dimensionality is relatively large and the sample size is small. Under this type of conditions, the 0.632 and 0.632+ bootstrap methods have the lowest RMSE, indicating that the difference between the estimated and the true performances obtained using the 0.632 and 0.632+ bootstrap will be statistically smaller than those obtained using the other three resampling methods. Of the three bootstrap methods, the 0.632+ bootstrap provides the lowest bias. Although this investigation is performed under some specific conditions, it reveals important trends for the problem of classifier performance prediction under the constraint of a limited dataset.

Determination of Time Dependent Virus Inactivation Rates

NASA Astrophysics Data System (ADS)

Chrysikopoulos, C. V.; Vogler, E. T.

2003-12-01

A methodology is developed for estimating temporally variable virus inactivation rate coefficients from experimental virus inactivation data. The methodology consists of a technique for slope estimation of normalized virus inactivation data in conjunction with a resampling parameter estimation procedure. The slope estimation technique is based on a relatively flexible geostatistical method known as universal kriging. Drift coefficients are obtained by nonlinear fitting of bootstrap samples and the corresponding confidence intervals are obtained by bootstrap percentiles. The proposed methodology yields more accurate time dependent virus inactivation rate coefficients than those estimated by fitting virus inactivation data to a first-order inactivation model. The methodology is successfully applied to a set of poliovirus batch inactivation data. Furthermore, the importance of accurate inactivation rate coefficient determination on virus transport in water saturated porous media is demonstrated with model simulations.

What Teachers Should Know About the Bootstrap: Resampling in the Undergraduate Statistics Curriculum

PubMed Central

Hesterberg, Tim C.

2015-01-01

Bootstrapping has enormous potential in statistics education and practice, but there are subtle issues and ways to go wrong. For example, the common combination of nonparametric bootstrapping and bootstrap percentile confidence intervals is less accurate than using t-intervals for small samples, though more accurate for larger samples. My goals in this article are to provide a deeper understanding of bootstrap methods—how they work, when they work or not, and which methods work better—and to highlight pedagogical issues. Supplementary materials for this article are available online. [Received December 2014. Revised August 2015] PMID:27019512

Exploring the Replicability of a Study's Results: Bootstrap Statistics for the Multivariate Case.

ERIC Educational Resources Information Center

Thompson, Bruce

Conventional statistical significance tests do not inform the researcher regarding the likelihood that results will replicate. One strategy for evaluating result replication is to use a "bootstrap" resampling of a study's data so that the stability of results across numerous configurations of the subjects can be explored. This paper…

More accurate, calibrated bootstrap confidence intervals for correlating two autocorrelated climate time series

NASA Astrophysics Data System (ADS)

Olafsdottir, Kristin B.; Mudelsee, Manfred

2013-04-01

Estimation of the Pearson's correlation coefficient between two time series to evaluate the influences of one time depended variable on another is one of the most often used statistical method in climate sciences. Various methods are used to estimate confidence interval to support the correlation point estimate. Many of them make strong mathematical assumptions regarding distributional shape and serial correlation, which are rarely met. More robust statistical methods are needed to increase the accuracy of the confidence intervals. Bootstrap confidence intervals are estimated in the Fortran 90 program PearsonT (Mudelsee, 2003), where the main intention was to get an accurate confidence interval for correlation coefficient between two time series by taking the serial dependence of the process that generated the data into account. However, Monte Carlo experiments show that the coverage accuracy for smaller data sizes can be improved. Here we adapt the PearsonT program into a new version called PearsonT3, by calibrating the confidence interval to increase the coverage accuracy. Calibration is a bootstrap resampling technique, which basically performs a second bootstrap loop or resamples from the bootstrap resamples. It offers, like the non-calibrated bootstrap confidence intervals, robustness against the data distribution. Pairwise moving block bootstrap is used to preserve the serial correlation of both time series. The calibration is applied to standard error based bootstrap Student's t confidence intervals. The performances of the calibrated confidence intervals are examined with Monte Carlo simulations, and compared with the performances of confidence intervals without calibration, that is, PearsonT. The coverage accuracy is evidently better for the calibrated confidence intervals where the coverage error is acceptably small (i.e., within a few percentage points) already for data sizes as small as 20. One form of climate time series is output from numerical models which simulate the climate system. The method is applied to model data from the high resolution ocean model, INALT01 where the relationship between the Agulhas Leakage and the North Brazil Current is evaluated. Preliminary results show significant correlation between the two variables when there is 10 year lag between them, which is more or less the time that takes the Agulhas Leakage water to reach the North Brazil Current. Mudelsee, M., 2003. Estimating Pearson's correlation coefficient with bootstrap confidence interval from serially dependent time series. Mathematical Geology 35, 651-665.

The Bootstrap, the Jackknife, and the Randomization Test: A Sampling Taxonomy.

PubMed

Rodgers, J L

1999-10-01

A simple sampling taxonomy is defined that shows the differences between and relationships among the bootstrap, the jackknife, and the randomization test. Each method has as its goal the creation of an empirical sampling distribution that can be used to test statistical hypotheses, estimate standard errors, and/or create confidence intervals. Distinctions between the methods can be made based on the sampling approach (with replacement versus without replacement) and the sample size (replacing the whole original sample versus replacing a subset of the original sample). The taxonomy is useful for teaching the goals and purposes of resampling schemes. An extension of the taxonomy implies other possible resampling approaches that have not previously been considered. Univariate and multivariate examples are presented.

Prediction of resource volumes at untested locations using simple local prediction models

USGS Publications Warehouse

Attanasi, E.D.; Coburn, T.C.; Freeman, P.A.

2006-01-01

This paper shows how local spatial nonparametric prediction models can be applied to estimate volumes of recoverable gas resources at individual undrilled sites, at multiple sites on a regional scale, and to compute confidence bounds for regional volumes based on the distribution of those estimates. An approach that combines cross-validation, the jackknife, and bootstrap procedures is used to accomplish this task. Simulation experiments show that cross-validation can be applied beneficially to select an appropriate prediction model. The cross-validation procedure worked well for a wide range of different states of nature and levels of information. Jackknife procedures are used to compute individual prediction estimation errors at undrilled locations. The jackknife replicates also are used with a bootstrap resampling procedure to compute confidence bounds for the total volume. The method was applied to data (partitioned into a training set and target set) from the Devonian Antrim Shale continuous-type gas play in the Michigan Basin in Otsego County, Michigan. The analysis showed that the model estimate of total recoverable volumes at prediction sites is within 4 percent of the total observed volume. The model predictions also provide frequency distributions of the cell volumes at the production unit scale. Such distributions are the basis for subsequent economic analyses. ?? Springer Science+Business Media, LLC 2007.

Assessing Uncertainties in Surface Water Security: A Probabilistic Multi-model Resampling approach

NASA Astrophysics Data System (ADS)

Rodrigues, D. B. B.

2015-12-01

Various uncertainties are involved in the representation of processes that characterize interactions between societal needs, ecosystem functioning, and hydrological conditions. Here, we develop an empirical uncertainty assessment of water security indicators that characterize scarcity and vulnerability, based on a multi-model and resampling framework. We consider several uncertainty sources including those related to: i) observed streamflow data; ii) hydrological model structure; iii) residual analysis; iv) the definition of Environmental Flow Requirement method; v) the definition of critical conditions for water provision; and vi) the critical demand imposed by human activities. We estimate the overall uncertainty coming from the hydrological model by means of a residual bootstrap resampling approach, and by uncertainty propagation through different methodological arrangements applied to a 291 km² agricultural basin within the Cantareira water supply system in Brazil. Together, the two-component hydrograph residual analysis and the block bootstrap resampling approach result in a more accurate and precise estimate of the uncertainty (95% confidence intervals) in the simulated time series. We then compare the uncertainty estimates associated with water security indicators using a multi-model framework and provided by each model uncertainty estimation approach. The method is general and can be easily extended forming the basis for meaningful support to end-users facing water resource challenges by enabling them to incorporate a viable uncertainty analysis into a robust decision making process.

Assessing uncertainties in superficial water provision by different bootstrap-based techniques

NASA Astrophysics Data System (ADS)

Rodrigues, Dulce B. B.; Gupta, Hoshin V.; Mendiondo, Eduardo Mario

2014-05-01

An assessment of water security can incorporate several water-related concepts, characterizing the interactions between societal needs, ecosystem functioning, and hydro-climatic conditions. The superficial freshwater provision level depends on the methods chosen for 'Environmental Flow Requirement' estimations, which integrate the sources of uncertainty in the understanding of how water-related threats to aquatic ecosystem security arise. Here, we develop an uncertainty assessment of superficial freshwater provision based on different bootstrap techniques (non-parametric resampling with replacement). To illustrate this approach, we use an agricultural basin (291 km2) within the Cantareira water supply system in Brazil monitored by one daily streamflow gage (24-year period). The original streamflow time series has been randomly resampled for different times or sample sizes (N = 500; ...; 1000), then applied to the conventional bootstrap approach and variations of this method, such as: 'nearest neighbor bootstrap'; and 'moving blocks bootstrap'. We have analyzed the impact of the sampling uncertainty on five Environmental Flow Requirement methods, based on: flow duration curves or probability of exceedance (Q90%, Q75% and Q50%); 7-day 10-year low-flow statistic (Q7,10); and presumptive standard (80% of the natural monthly mean ?ow). The bootstrap technique has been also used to compare those 'Environmental Flow Requirement' (EFR) methods among themselves, considering the difference between the bootstrap estimates and the "true" EFR characteristic, which has been computed averaging the EFR values of the five methods and using the entire streamflow record at monitoring station. This study evaluates the bootstrapping strategies, the representativeness of streamflow series for EFR estimates and their confidence intervals, in addition to overview of the performance differences between the EFR methods. The uncertainties arisen during EFR methods assessment will be propagated through water security indicators referring to water scarcity and vulnerability, seeking to provide meaningful support to end-users and water managers facing the incorporation of uncertainties in the decision making process.

Forecasting drought risks for a water supply storage system using bootstrap position analysis

USGS Publications Warehouse

Tasker, Gary; Dunne, Paul

1997-01-01

Forecasting the likelihood of drought conditions is an integral part of managing a water supply storage and delivery system. Position analysis uses a large number of possible flow sequences as inputs to a simulation of a water supply storage and delivery system. For a given set of operating rules and water use requirements, water managers can use such a model to forecast the likelihood of specified outcomes such as reservoir levels falling below a specified level or streamflows falling below statutory passing flows a few months ahead conditioned on the current reservoir levels and streamflows. The large number of possible flow sequences are generated using a stochastic streamflow model with a random resampling of innovations. The advantages of this resampling scheme, called bootstrap position analysis, are that it does not rely on the unverifiable assumption of normality and it allows incorporation of long-range weather forecasts into the analysis.

Bootstrap position analysis for forecasting low flow frequency

USGS Publications Warehouse

Tasker, Gary D.; Dunne, P.

1997-01-01

A method of random resampling of residuals from stochastic models is used to generate a large number of 12-month-long traces of natural monthly runoff to be used in a position analysis model for a water-supply storage and delivery system. Position analysis uses the traces to forecast the likelihood of specified outcomes such as reservoir levels falling below a specified level or streamflows falling below statutory passing flows conditioned on the current reservoir levels and streamflows. The advantages of this resampling scheme, called bootstrap position analysis, are that it does not rely on the unverifiable assumption of normality, fewer parameters need to be estimated directly from the data, and accounting for parameter uncertainty is easily done. For a given set of operating rules and water-use requirements for a system, water managers can use such a model as a decision-making tool to evaluate different operating rules. ?? ASCE,.

The efficiency of average linkage hierarchical clustering algorithm associated multi-scale bootstrap resampling in identifying homogeneous precipitation catchments

NASA Astrophysics Data System (ADS)

Chuan, Zun Liang; Ismail, Noriszura; Shinyie, Wendy Ling; Lit Ken, Tan; Fam, Soo-Fen; Senawi, Azlyna; Yusoff, Wan Nur Syahidah Wan

2018-04-01

Due to the limited of historical precipitation records, agglomerative hierarchical clustering algorithms widely used to extrapolate information from gauged to ungauged precipitation catchments in yielding a more reliable projection of extreme hydro-meteorological events such as extreme precipitation events. However, identifying the optimum number of homogeneous precipitation catchments accurately based on the dendrogram resulted using agglomerative hierarchical algorithms are very subjective. The main objective of this study is to propose an efficient regionalized algorithm to identify the homogeneous precipitation catchments for non-stationary precipitation time series. The homogeneous precipitation catchments are identified using average linkage hierarchical clustering algorithm associated multi-scale bootstrap resampling, while uncentered correlation coefficient as the similarity measure. The regionalized homogeneous precipitation is consolidated using K-sample Anderson Darling non-parametric test. The analysis result shows the proposed regionalized algorithm performed more better compared to the proposed agglomerative hierarchical clustering algorithm in previous studies.

Efficient bootstrap estimates for tail statistics

NASA Astrophysics Data System (ADS)

Breivik, Øyvind; Aarnes, Ole Johan

2017-03-01

Bootstrap resamples can be used to investigate the tail of empirical distributions as well as return value estimates from the extremal behaviour of the sample. Specifically, the confidence intervals on return value estimates or bounds on in-sample tail statistics can be obtained using bootstrap techniques. However, non-parametric bootstrapping from the entire sample is expensive. It is shown here that it suffices to bootstrap from a small subset consisting of the highest entries in the sequence to make estimates that are essentially identical to bootstraps from the entire sample. Similarly, bootstrap estimates of confidence intervals of threshold return estimates are found to be well approximated by using a subset consisting of the highest entries. This has practical consequences in fields such as meteorology, oceanography and hydrology where return values are calculated from very large gridded model integrations spanning decades at high temporal resolution or from large ensembles of independent and identically distributed model fields. In such cases the computational savings are substantial.

A resampling strategy based on bootstrap to reduce the effect of large blunders in GPS absolute positioning

NASA Astrophysics Data System (ADS)

Angrisano, Antonio; Maratea, Antonio; Gaglione, Salvatore

2018-01-01

In the absence of obstacles, a GPS device is generally able to provide continuous and accurate estimates of position, while in urban scenarios buildings can generate multipath and echo-only phenomena that severely affect the continuity and the accuracy of the provided estimates. Receiver autonomous integrity monitoring (RAIM) techniques are able to reduce the negative consequences of large blunders in urban scenarios, but require both a good redundancy and a low contamination to be effective. In this paper a resampling strategy based on bootstrap is proposed as an alternative to RAIM, in order to estimate accurately position in case of low redundancy and multiple blunders: starting with the pseudorange measurement model, at each epoch the available measurements are bootstrapped—that is random sampled with replacement—and the generated a posteriori empirical distribution is exploited to derive the final position. Compared to standard bootstrap, in this paper the sampling probabilities are not uniform, but vary according to an indicator of the measurement quality. The proposed method has been compared with two different RAIM techniques on a data set collected in critical conditions, resulting in a clear improvement on all considered figures of merit.

The Role of Mediators in the Indirect Effects of Religiosity on Therapeutic Compliance in African Migrant HIV-Positive Patients.

PubMed

Mambet Doue, Constance; Roussiau, Nicolas

2016-12-01

This research investigates the indirect effects of religiosity (practice and belief) on therapeutic compliance in 81 HIV-positive patients who are migrants from sub-Saharan Africa (23 men and 58 women). Using analyses of mediation and standard multiple regression, including a resampling procedure by bootstrapping, the role of these mediators (magical-religious beliefs and nonuse of toxic substances) was tested. The results show that, through magical-religious beliefs, religiosity has a negative indirect effect, while with the nonuse of toxic substances, religious practice has a positive indirect effect. Beyond religiosity, the role of mediators is highlighted in the interaction with therapeutic compliance.

Pick a Sample.

ERIC Educational Resources Information Center

Peterson, Ivars

1991-01-01

A method that enables people to obtain the benefits of statistics and probability theory without the shortcomings of conventional methods because it is free of mathematical formulas and is easy to understand and use is described. A resampling technique called the "bootstrap" is discussed in terms of application and development. (KR)

Bias-Corrected Estimation of Noncentrality Parameters of Covariance Structure Models

ERIC Educational Resources Information Center

Raykov, Tenko

2005-01-01

A bias-corrected estimator of noncentrality parameters of covariance structure models is discussed. The approach represents an application of the bootstrap methodology for purposes of bias correction, and utilizes the relation between average of resample conventional noncentrality parameter estimates and their sample counterpart. The…

Linear regression in astronomy. II

NASA Technical Reports Server (NTRS)

Feigelson, Eric D.; Babu, Gutti J.

1992-01-01

A wide variety of least-squares linear regression procedures used in observational astronomy, particularly investigations of the cosmic distance scale, are presented and discussed. The classes of linear models considered are (1) unweighted regression lines, with bootstrap and jackknife resampling; (2) regression solutions when measurement error, in one or both variables, dominates the scatter; (3) methods to apply a calibration line to new data; (4) truncated regression models, which apply to flux-limited data sets; and (5) censored regression models, which apply when nondetections are present. For the calibration problem we develop two new procedures: a formula for the intercept offset between two parallel data sets, which propagates slope errors from one regression to the other; and a generalization of the Working-Hotelling confidence bands to nonstandard least-squares lines. They can provide improved error analysis for Faber-Jackson, Tully-Fisher, and similar cosmic distance scale relations.

A Statistical Analysis of Brain Morphology Using Wild Bootstrapping

PubMed Central

Ibrahim, Joseph G.; Tang, Niansheng; Rowe, Daniel B.; Hao, Xuejun; Bansal, Ravi; Peterson, Bradley S.

2008-01-01

Methods for the analysis of brain morphology, including voxel-based morphology and surface-based morphometries, have been used to detect associations between brain structure and covariates of interest, such as diagnosis, severity of disease, age, IQ, and genotype. The statistical analysis of morphometric measures usually involves two statistical procedures: 1) invoking a statistical model at each voxel (or point) on the surface of the brain or brain subregion, followed by mapping test statistics (e.g., t test) or their associated p values at each of those voxels; 2) correction for the multiple statistical tests conducted across all voxels on the surface of the brain region under investigation. We propose the use of new statistical methods for each of these procedures. We first use a heteroscedastic linear model to test the associations between the morphological measures at each voxel on the surface of the specified subregion (e.g., cortical or subcortical surfaces) and the covariates of interest. Moreover, we develop a robust test procedure that is based on a resampling method, called wild bootstrapping. This procedure assesses the statistical significance of the associations between a measure of given brain structure and the covariates of interest. The value of this robust test procedure lies in its computationally simplicity and in its applicability to a wide range of imaging data, including data from both anatomical and functional magnetic resonance imaging (fMRI). Simulation studies demonstrate that this robust test procedure can accurately control the family-wise error rate. We demonstrate the application of this robust test procedure to the detection of statistically significant differences in the morphology of the hippocampus over time across gender groups in a large sample of healthy subjects. PMID:17649909

Small sample mediation testing: misplaced confidence in bootstrapped confidence intervals.

PubMed

Koopman, Joel; Howe, Michael; Hollenbeck, John R; Sin, Hock-Peng

2015-01-01

Bootstrapping is an analytical tool commonly used in psychology to test the statistical significance of the indirect effect in mediation models. Bootstrapping proponents have particularly advocated for its use for samples of 20-80 cases. This advocacy has been heeded, especially in the Journal of Applied Psychology, as researchers are increasingly utilizing bootstrapping to test mediation with samples in this range. We discuss reasons to be concerned with this escalation, and in a simulation study focused specifically on this range of sample sizes, we demonstrate not only that bootstrapping has insufficient statistical power to provide a rigorous hypothesis test in most conditions but also that bootstrapping has a tendency to exhibit an inflated Type I error rate. We then extend our simulations to investigate an alternative empirical resampling method as well as a Bayesian approach and demonstrate that they exhibit comparable statistical power to bootstrapping in small samples without the associated inflated Type I error. Implications for researchers testing mediation hypotheses in small samples are presented. For researchers wishing to use these methods in their own research, we have provided R syntax in the online supplemental materials. (c) 2015 APA, all rights reserved.

Tests for informative cluster size using a novel balanced bootstrap scheme.

PubMed

Nevalainen, Jaakko; Oja, Hannu; Datta, Somnath

2017-07-20

Clustered data are often encountered in biomedical studies, and to date, a number of approaches have been proposed to analyze such data. However, the phenomenon of informative cluster size (ICS) is a challenging problem, and its presence has an impact on the choice of a correct analysis methodology. For example, Dutta and Datta (2015, Biometrics) presented a number of marginal distributions that could be tested. Depending on the nature and degree of informativeness of the cluster size, these marginal distributions may differ, as do the choices of the appropriate test. In particular, they applied their new test to a periodontal data set where the plausibility of the informativeness was mentioned, but no formal test for the same was conducted. We propose bootstrap tests for testing the presence of ICS. A balanced bootstrap method is developed to successfully estimate the null distribution by merging the re-sampled observations with closely matching counterparts. Relying on the assumption of exchangeability within clusters, the proposed procedure performs well in simulations even with a small number of clusters, at different distributions and against different alternative hypotheses, thus making it an omnibus test. We also explain how to extend the ICS test to a regression setting and thereby enhancing its practical utility. The methodologies are illustrated using the periodontal data set mentioned earlier. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Applying Bootstrap Resampling to Compute Confidence Intervals for Various Statistics with R

ERIC Educational Resources Information Center

Dogan, C. Deha

2017-01-01

Background: Most of the studies in academic journals use p values to represent statistical significance. However, this is not a good indicator of practical significance. Although confidence intervals provide information about the precision of point estimation, they are, unfortunately, rarely used. The infrequent use of confidence intervals might…

Explanation of Two Anomalous Results in Statistical Mediation Analysis

ERIC Educational Resources Information Center

Fritz, Matthew S.; Taylor, Aaron B.; MacKinnon, David P.

2012-01-01

Previous studies of different methods of testing mediation models have consistently found two anomalous results. The first result is elevated Type I error rates for the bias-corrected and accelerated bias-corrected bootstrap tests not found in nonresampling tests or in resampling tests that did not include a bias correction. This is of special…

Confidence Intervals for Effect Sizes: Applying Bootstrap Resampling

ERIC Educational Resources Information Center

Banjanovic, Erin S.; Osborne, Jason W.

2016-01-01

Confidence intervals for effect sizes (CIES) provide readers with an estimate of the strength of a reported statistic as well as the relative precision of the point estimate. These statistics offer more information and context than null hypothesis statistic testing. Although confidence intervals have been recommended by scholars for many years,…

Speeding Up Non-Parametric Bootstrap Computations for Statistics Based on Sample Moments in Small/Moderate Sample Size Applications

PubMed Central

Chaibub Neto, Elias

2015-01-01

In this paper we propose a vectorized implementation of the non-parametric bootstrap for statistics based on sample moments. Basically, we adopt the multinomial sampling formulation of the non-parametric bootstrap, and compute bootstrap replications of sample moment statistics by simply weighting the observed data according to multinomial counts instead of evaluating the statistic on a resampled version of the observed data. Using this formulation we can generate a matrix of bootstrap weights and compute the entire vector of bootstrap replications with a few matrix multiplications. Vectorization is particularly important for matrix-oriented programming languages such as R, where matrix/vector calculations tend to be faster than scalar operations implemented in a loop. We illustrate the application of the vectorized implementation in real and simulated data sets, when bootstrapping Pearson’s sample correlation coefficient, and compared its performance against two state-of-the-art R implementations of the non-parametric bootstrap, as well as a straightforward one based on a for loop. Our investigations spanned varying sample sizes and number of bootstrap replications. The vectorized bootstrap compared favorably against the state-of-the-art implementations in all cases tested, and was remarkably/considerably faster for small/moderate sample sizes. The same results were observed in the comparison with the straightforward implementation, except for large sample sizes, where the vectorized bootstrap was slightly slower than the straightforward implementation due to increased time expenditures in the generation of weight matrices via multinomial sampling. PMID:26125965

permGPU: Using graphics processing units in RNA microarray association studies.

PubMed

Shterev, Ivo D; Jung, Sin-Ho; George, Stephen L; Owzar, Kouros

2010-06-16

Many analyses of microarray association studies involve permutation, bootstrap resampling and cross-validation, that are ideally formulated as embarrassingly parallel computing problems. Given that these analyses are computationally intensive, scalable approaches that can take advantage of multi-core processor systems need to be developed. We have developed a CUDA based implementation, permGPU, that employs graphics processing units in microarray association studies. We illustrate the performance and applicability of permGPU within the context of permutation resampling for a number of test statistics. An extensive simulation study demonstrates a dramatic increase in performance when using permGPU on an NVIDIA GTX 280 card compared to an optimized C/C++ solution running on a conventional Linux server. permGPU is available as an open-source stand-alone application and as an extension package for the R statistical environment. It provides a dramatic increase in performance for permutation resampling analysis in the context of microarray association studies. The current version offers six test statistics for carrying out permutation resampling analyses for binary, quantitative and censored time-to-event traits.

Assessing uncertainties in surface water security: An empirical multimodel approach

NASA Astrophysics Data System (ADS)

Rodrigues, Dulce B. B.; Gupta, Hoshin V.; Mendiondo, Eduardo M.; Oliveira, Paulo Tarso S.

2015-11-01

Various uncertainties are involved in the representation of processes that characterize interactions among societal needs, ecosystem functioning, and hydrological conditions. Here we develop an empirical uncertainty assessment of water security indicators that characterize scarcity and vulnerability, based on a multimodel and resampling framework. We consider several uncertainty sources including those related to (i) observed streamflow data; (ii) hydrological model structure; (iii) residual analysis; (iv) the method for defining Environmental Flow Requirement; (v) the definition of critical conditions for water provision; and (vi) the critical demand imposed by human activities. We estimate the overall hydrological model uncertainty by means of a residual bootstrap resampling approach, and by uncertainty propagation through different methodological arrangements applied to a 291 km2 agricultural basin within the Cantareira water supply system in Brazil. Together, the two-component hydrograph residual analysis and the block bootstrap resampling approach result in a more accurate and precise estimate of the uncertainty (95% confidence intervals) in the simulated time series. We then compare the uncertainty estimates associated with water security indicators using a multimodel framework and the uncertainty estimates provided by each model uncertainty estimation approach. The range of values obtained for the water security indicators suggests that the models/methods are robust and performs well in a range of plausible situations. The method is general and can be easily extended, thereby forming the basis for meaningful support to end-users facing water resource challenges by enabling them to incorporate a viable uncertainty analysis into a robust decision-making process.

Mist net effort required to inventory a forest bat species assemblage.

Treesearch

Theodore J. Weller; Danny C. Lee

2007-01-01

Little quantitative information exists about the survey effort necessary to inventory temperate bat species assemblages. We used a bootstrap resampling lgorithm to estimate the number of mist net surveys required to capture individuals from 9 species at both study area and site levels using data collected in a forested watershed in northwestern California, USA, during...

POLYMAT-C: a comprehensive SPSS program for computing the polychoric correlation matrix.

PubMed

Lorenzo-Seva, Urbano; Ferrando, Pere J

2015-09-01

We provide a free noncommercial SPSS program that implements procedures for (a) obtaining the polychoric correlation matrix between a set of ordered categorical measures, so that it can be used as input for the SPSS factor analysis (FA) program; (b) testing the null hypothesis of zero population correlation for each element of the matrix by using appropriate simulation procedures; (c) obtaining valid and accurate confidence intervals via bootstrap resampling for those correlations found to be significant; and (d) performing, if necessary, a smoothing procedure that makes the matrix amenable to any FA estimation procedure. For the main purpose (a), the program uses a robust unified procedure that allows four different types of estimates to be obtained at the user's choice. Overall, we hope the program will be a very useful tool for the applied researcher, not only because it provides an appropriate input matrix for FA, but also because it allows the researcher to carefully check the appropriateness of the matrix for this purpose. The SPSS syntax, a short manual, and data files related to this article are available as Supplemental materials that are available for download with this article.

Examination of the reliability of the crash modification factors using empirical Bayes method with resampling technique.

PubMed

Wang, Jung-Han; Abdel-Aty, Mohamed; Wang, Ling

2017-07-01

There have been plenty of studies intended to use different methods, for example, empirical Bayes before-after methods, to get accurate estimation of CMFs. All of them have different assumptions toward crash count if there was no treatment. Additionally, another major assumption is that multiple sites share the same true CMF. Under this assumption, the CMF at an individual intersection is randomly drawn from a normally distributed population of CMFs at all intersections. Since CMFs are non-zero values, the population of all CMFs might not follow normal distributions, and even if it does, the true mean of CMFs at some intersections may be different from that at others. Therefore, a bootstrap method based on before-after empirical Bayes theory was proposed to estimate CMFs, but it did not make distributional assumptions. This bootstrap procedure has the added benefit of producing a measure of CMF stability. Furthermore, based on the bootstrapped CMF, a new CMF precision rating method was proposed to evaluate the reliability of CMFs. This study chose 29 urban four-legged intersections as treated sites, and their controls were changed from stop-controlled to signal-controlled. Meanwhile, 124 urban four-legged stop-controlled intersections were selected as reference sites. At first, different safety performance functions (SPFs) were applied to five crash categories, and it was found that each crash category had different optimal SPF form. Then, the CMFs of these five crash categories were estimated using the bootstrap empirical Bayes method. The results of the bootstrapped method showed that signalization significantly decreased Angle+Left-Turn crashes, and its CMF had the highest precision. While, the CMF for Rear-End crashes was unreliable. For KABCO, KABC, and KAB crashes, their CMFs were proved to be reliable for the majority of intersections, but the estimated effect of signalization may be not accurate at some sites. Copyright © 2017 Elsevier Ltd. All rights reserved.

Grain Size and Parameter Recovery with TIMSS and the General Diagnostic Model

ERIC Educational Resources Information Center

Skaggs, Gary; Wilkins, Jesse L. M.; Hein, Serge F.

2016-01-01

The purpose of this study was to explore the degree of grain size of the attributes and the sample sizes that can support accurate parameter recovery with the General Diagnostic Model (GDM) for a large-scale international assessment. In this resampling study, bootstrap samples were obtained from the 2003 Grade 8 TIMSS in Mathematics at varying…

swot: Super W Of Theta

NASA Astrophysics Data System (ADS)

Coupon, Jean; Leauthaud, Alexie; Kilbinger, Martin; Medezinski, Elinor

2017-07-01

SWOT (Super W Of Theta) computes two-point statistics for very large data sets, based on “divide and conquer” algorithms, mainly, but not limited to data storage in binary trees, approximation at large scale, parellelization (open MPI), and bootstrap and jackknife resampling methods “on the fly”. It currently supports projected and 3D galaxy auto and cross correlations, galaxy-galaxy lensing, and weighted histograms.

Comparison of Efficiency of Jackknife and Variance Component Estimators of Standard Errors. Program Statistics Research. Technical Report.

ERIC Educational Resources Information Center

Longford, Nicholas T.

Large scale surveys usually employ a complex sampling design and as a consequence, no standard methods for estimation of the standard errors associated with the estimates of population means are available. Resampling methods, such as jackknife or bootstrap, are often used, with reference to their properties of robustness and reduction of bias. A…

Confidence limits for contribution plots in multivariate statistical process control using bootstrap estimates.

PubMed

Babamoradi, Hamid; van den Berg, Frans; Rinnan, Åsmund

2016-02-18

In Multivariate Statistical Process Control, when a fault is expected or detected in the process, contribution plots are essential for operators and optimization engineers in identifying those process variables that were affected by or might be the cause of the fault. The traditional way of interpreting a contribution plot is to examine the largest contributing process variables as the most probable faulty ones. This might result in false readings purely due to the differences in natural variation, measurement uncertainties, etc. It is more reasonable to compare variable contributions for new process runs with historical results achieved under Normal Operating Conditions, where confidence limits for contribution plots estimated from training data are used to judge new production runs. Asymptotic methods cannot provide confidence limits for contribution plots, leaving re-sampling methods as the only option. We suggest bootstrap re-sampling to build confidence limits for all contribution plots in online PCA-based MSPC. The new strategy to estimate CLs is compared to the previously reported CLs for contribution plots. An industrial batch process dataset was used to illustrate the concepts. Copyright © 2016 Elsevier B.V. All rights reserved.

Trends and Correlation Estimation in Climate Sciences: Effects of Timescale Errors

NASA Astrophysics Data System (ADS)

Mudelsee, M.; Bermejo, M. A.; Bickert, T.; Chirila, D.; Fohlmeister, J.; Köhler, P.; Lohmann, G.; Olafsdottir, K.; Scholz, D.

2012-12-01

Trend describes time-dependence in the first moment of a stochastic process, and correlation measures the linear relation between two random variables. Accurately estimating the trend and correlation, including uncertainties, from climate time series data in the uni- and bivariate domain, respectively, allows first-order insights into the geophysical process that generated the data. Timescale errors, ubiquitious in paleoclimatology, where archives are sampled for proxy measurements and dated, poses a problem to the estimation. Statistical science and the various applied research fields, including geophysics, have almost completely ignored this problem due to its theoretical almost-intractability. However, computational adaptations or replacements of traditional error formulas have become technically feasible. This contribution gives a short overview of such an adaptation package, bootstrap resampling combined with parametric timescale simulation. We study linear regression, parametric change-point models and nonparametric smoothing for trend estimation. We introduce pairwise-moving block bootstrap resampling for correlation estimation. Both methods share robustness against autocorrelation and non-Gaussian distributional shape. We shortly touch computing-intensive calibration of bootstrap confidence intervals and consider options to parallelize the related computer code. Following examples serve not only to illustrate the methods but tell own climate stories: (1) the search for climate drivers of the Agulhas Current on recent timescales, (2) the comparison of three stalagmite-based proxy series of regional, western German climate over the later part of the Holocene, and (3) trends and transitions in benthic oxygen isotope time series from the Cenozoic. Financial support by Deutsche Forschungsgemeinschaft (FOR 668, FOR 1070, MU 1595/4-1) and the European Commission (MC ITN 238512, MC ITN 289447) is acknowledged.

Uncertainty quantification of CO₂ saturation estimated from electrical resistance tomography data at the Cranfield site

DOE PAGES

Yang, Xianjin; Chen, Xiao; Carrigan, Charles R.; ...

2014-06-03

A parametric bootstrap approach is presented for uncertainty quantification (UQ) of CO₂ saturation derived from electrical resistance tomography (ERT) data collected at the Cranfield, Mississippi (USA) carbon sequestration site. There are many sources of uncertainty in ERT-derived CO₂ saturation, but we focus on how the ERT observation errors propagate to the estimated CO₂ saturation in a nonlinear inversion process. Our UQ approach consists of three steps. We first estimated the observational errors from a large number of reciprocal ERT measurements. The second step was to invert the pre-injection baseline data and the resulting resistivity tomograph was used as the priormore » information for nonlinear inversion of time-lapse data. We assigned a 3% random noise to the baseline model. Finally, we used a parametric bootstrap method to obtain bootstrap CO₂ saturation samples by deterministically solving a nonlinear inverse problem many times with resampled data and resampled baseline models. Then the mean and standard deviation of CO₂ saturation were calculated from the bootstrap samples. We found that the maximum standard deviation of CO₂ saturation was around 6% with a corresponding maximum saturation of 30% for a data set collected 100 days after injection began. There was no apparent spatial correlation between the mean and standard deviation of CO₂ saturation but the standard deviation values increased with time as the saturation increased. The uncertainty in CO₂ saturation also depends on the ERT reciprocal error threshold used to identify and remove noisy data and inversion constraints such as temporal roughness. Five hundred realizations requiring 3.5 h on a single 12-core node were needed for the nonlinear Monte Carlo inversion to arrive at stationary variances while the Markov Chain Monte Carlo (MCMC) stochastic inverse approach may expend days for a global search. This indicates that UQ of 2D or 3D ERT inverse problems can be performed on a laptop or desktop PC.« less

SABRE: a method for assessing the stability of gene modules in complex tissues and subject populations.

PubMed

Shannon, Casey P; Chen, Virginia; Takhar, Mandeep; Hollander, Zsuzsanna; Balshaw, Robert; McManus, Bruce M; Tebbutt, Scott J; Sin, Don D; Ng, Raymond T

2016-11-14

Gene network inference (GNI) algorithms can be used to identify sets of coordinately expressed genes, termed network modules from whole transcriptome gene expression data. The identification of such modules has become a popular approach to systems biology, with important applications in translational research. Although diverse computational and statistical approaches have been devised to identify such modules, their performance behavior is still not fully understood, particularly in complex human tissues. Given human heterogeneity, one important question is how the outputs of these computational methods are sensitive to the input sample set, or stability. A related question is how this sensitivity depends on the size of the sample set. We describe here the SABRE (Similarity Across Bootstrap RE-sampling) procedure for assessing the stability of gene network modules using a re-sampling strategy, introduce a novel criterion for identifying stable modules, and demonstrate the utility of this approach in a clinically-relevant cohort, using two different gene network module discovery algorithms. The stability of modules increased as sample size increased and stable modules were more likely to be replicated in larger sets of samples. Random modules derived from permutated gene expression data were consistently unstable, as assessed by SABRE, and provide a useful baseline value for our proposed stability criterion. Gene module sets identified by different algorithms varied with respect to their stability, as assessed by SABRE. Finally, stable modules were more readily annotated in various curated gene set databases. The SABRE procedure and proposed stability criterion may provide guidance when designing systems biology studies in complex human disease and tissues.

Contribution of Morphological Awareness and Lexical Inferencing Ability to L2 Vocabulary Knowledge and Reading Comprehension among Advanced EFL Learners: Testing Direct and Indirect Effects

ERIC Educational Resources Information Center

Zhang, Dongbo; Koda, Keiko

2012-01-01

Within the Structural Equation Modeling framework, this study tested the direct and indirect effects of morphological awareness and lexical inferencing ability on L2 vocabulary knowledge and reading comprehension among advanced Chinese EFL readers in a university in China. Using both regular z-test and the bootstrapping (data-based resampling)…

Uncertainty Quantification in High Throughput Screening ...

EPA Pesticide Factsheets

Using uncertainty quantification, we aim to improve the quality of modeling data from high throughput screening assays for use in risk assessment. ToxCast is a large-scale screening program that analyzes thousands of chemicals using over 800 assays representing hundreds of biochemical and cellular processes, including endocrine disruption, cytotoxicity, and zebrafish development. Over 2.6 million concentration response curves are fit to models to extract parameters related to potency and efficacy. Models built on ToxCast results are being used to rank and prioritize the toxicological risk of tested chemicals and to predict the toxicity of tens of thousands of chemicals not yet tested in vivo. However, the data size also presents challenges. When fitting the data, the choice of models, model selection strategy, and hit call criteria must reflect the need for computational efficiency and robustness, requiring hard and somewhat arbitrary cutoffs. When coupled with unavoidable noise in the experimental concentration response data, these hard cutoffs cause uncertainty in model parameters and the hit call itself. The uncertainty will then propagate through all of the models built on the data. Left unquantified, this uncertainty makes it difficult to fully interpret the data for risk assessment. We used bootstrap resampling methods to quantify the uncertainty in fitting models to the concentration response data. Bootstrap resampling determines confidence intervals for

Fast Computation of the Two-Point Correlation Function in the Age of Big Data

NASA Astrophysics Data System (ADS)

Pellegrino, Andrew; Timlin, John

2018-01-01

We present a new code which quickly computes the two-point correlation function for large sets of astronomical data. This code combines the ease of use of Python with the speed of parallel shared libraries written in C. We include the capability to compute the auto- and cross-correlation statistics, and allow the user to calculate the three-dimensional and angular correlation functions. Additionally, the code automatically divides the user-provided sky masks into contiguous subsamples of similar size, using the HEALPix pixelization scheme, for the purpose of resampling. Errors are computed using jackknife and bootstrap resampling in a way that adds negligible extra runtime, even with many subsamples. We demonstrate comparable speed with other clustering codes, and code accuracy compared to known and analytic results.

Estimation of urban runoff and water quality using remote sensing and artificial intelligence.

PubMed

Ha, S R; Park, S Y; Park, D H

2003-01-01

Water quality and quantity of runoff are strongly dependent on the landuse and landcover (LULC) criteria. In this study, we developed a more improved parameter estimation procedure for the environmental model using remote sensing (RS) and artificial intelligence (AI) techniques. Landsat TM multi-band (7bands) and Korea Multi-Purpose Satellite (KOMPSAT) panchromatic data were selected for input data processing. We employed two kinds of artificial intelligence techniques, RBF-NN (radial-basis-function neural network) and ANN (artificial neural network), to classify LULC of the study area. A bootstrap resampling method, a statistical technique, was employed to generate the confidence intervals and distribution of the unit load. SWMM was used to simulate the urban runoff and water quality and applied to the study watershed. The condition of urban flow and non-point contaminations was simulated with rainfall-runoff and measured water quality data. The estimated total runoff, peak time, and pollutant generation varied considerably according to the classification accuracy and percentile unit load applied. The proposed procedure would efficiently be applied to water quality and runoff simulation in a rapidly changing urban area.

Estimating uncertainty in respondent-driven sampling using a tree bootstrap method.

PubMed

Baraff, Aaron J; McCormick, Tyler H; Raftery, Adrian E

2016-12-20

Respondent-driven sampling (RDS) is a network-based form of chain-referral sampling used to estimate attributes of populations that are difficult to access using standard survey tools. Although it has grown quickly in popularity since its introduction, the statistical properties of RDS estimates remain elusive. In particular, the sampling variability of these estimates has been shown to be much higher than previously acknowledged, and even methods designed to account for RDS result in misleadingly narrow confidence intervals. In this paper, we introduce a tree bootstrap method for estimating uncertainty in RDS estimates based on resampling recruitment trees. We use simulations from known social networks to show that the tree bootstrap method not only outperforms existing methods but also captures the high variability of RDS, even in extreme cases with high design effects. We also apply the method to data from injecting drug users in Ukraine. Unlike other methods, the tree bootstrap depends only on the structure of the sampled recruitment trees, not on the attributes being measured on the respondents, so correlations between attributes can be estimated as well as variability. Our results suggest that it is possible to accurately assess the high level of uncertainty inherent in RDS.

A bootstrap estimation scheme for chemical compositional data with nondetects

USGS Publications Warehouse

Palarea-Albaladejo, J; Martín-Fernández, J.A; Olea, Ricardo A.

2014-01-01

The bootstrap method is commonly used to estimate the distribution of estimators and their associated uncertainty when explicit analytic expressions are not available or are difficult to obtain. It has been widely applied in environmental and geochemical studies, where the data generated often represent parts of whole, typically chemical concentrations. This kind of constrained data is generically called compositional data, and they require specialised statistical methods to properly account for their particular covariance structure. On the other hand, it is not unusual in practice that those data contain labels denoting nondetects, that is, concentrations falling below detection limits. Nondetects impede the implementation of the bootstrap and represent an additional source of uncertainty that must be taken into account. In this work, a bootstrap scheme is devised that handles nondetects by adding an imputation step within the resampling process and conveniently propagates their associated uncertainly. In doing so, it considers the constrained relationships between chemical concentrations originated from their compositional nature. Bootstrap estimates using a range of imputation methods, including new stochastic proposals, are compared across scenarios of increasing difficulty. They are formulated to meet compositional principles following the log-ratio approach, and an adjustment is introduced in the multivariate case to deal with nonclosed samples. Results suggest that nondetect bootstrap based on model-based imputation is generally preferable. A robust approach based on isometric log-ratio transformations appears to be particularly suited in this context. Computer routines in the R statistical programming language are provided. 

Resampling to Address the Winner's Curse in Genetic Association Analysis of Time to Event

PubMed Central

Poirier, Julia G.; Faye, Laura L.; Dimitromanolakis, Apostolos; Paterson, Andrew D.; Sun, Lei

2015-01-01

ABSTRACT The “winner's curse” is a subtle and difficult problem in interpretation of genetic association, in which association estimates from large‐scale gene detection studies are larger in magnitude than those from subsequent replication studies. This is practically important because use of a biased estimate from the original study will yield an underestimate of sample size requirements for replication, leaving the investigators with an underpowered study. Motivated by investigation of the genetics of type 1 diabetes complications in a longitudinal cohort of participants in the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) Genetics Study, we apply a bootstrap resampling method in analysis of time to nephropathy under a Cox proportional hazards model, examining 1,213 single‐nucleotide polymorphisms (SNPs) in 201 candidate genes custom genotyped in 1,361 white probands. Among 15 top‐ranked SNPs, bias reduction in log hazard ratio estimates ranges from 43.1% to 80.5%. In simulation studies based on the observed DCCT/EDIC genotype data, genome‐wide bootstrap estimates for false‐positive SNPs and for true‐positive SNPs with low‐to‐moderate power are closer to the true values than uncorrected naïve estimates, but tend to overcorrect SNPs with high power. This bias‐reduction technique is generally applicable for complex trait studies including quantitative, binary, and time‐to‐event traits. PMID:26411674

Incorporating external evidence in trial-based cost-effectiveness analyses: the use of resampling methods

PubMed Central

2014-01-01

Background Cost-effectiveness analyses (CEAs) that use patient-specific data from a randomized controlled trial (RCT) are popular, yet such CEAs are criticized because they neglect to incorporate evidence external to the trial. A popular method for quantifying uncertainty in a RCT-based CEA is the bootstrap. The objective of the present study was to further expand the bootstrap method of RCT-based CEA for the incorporation of external evidence. Methods We utilize the Bayesian interpretation of the bootstrap and derive the distribution for the cost and effectiveness outcomes after observing the current RCT data and the external evidence. We propose simple modifications of the bootstrap for sampling from such posterior distributions. Results In a proof-of-concept case study, we use data from a clinical trial and incorporate external evidence on the effect size of treatments to illustrate the method in action. Compared to the parametric models of evidence synthesis, the proposed approach requires fewer distributional assumptions, does not require explicit modeling of the relation between external evidence and outcomes of interest, and is generally easier to implement. A drawback of this approach is potential computational inefficiency compared to the parametric Bayesian methods. Conclusions The bootstrap method of RCT-based CEA can be extended to incorporate external evidence, while preserving its appealing features such as no requirement for parametric modeling of cost and effectiveness outcomes. PMID:24888356

Incorporating external evidence in trial-based cost-effectiveness analyses: the use of resampling methods.

PubMed

Sadatsafavi, Mohsen; Marra, Carlo; Aaron, Shawn; Bryan, Stirling

2014-06-03

Cost-effectiveness analyses (CEAs) that use patient-specific data from a randomized controlled trial (RCT) are popular, yet such CEAs are criticized because they neglect to incorporate evidence external to the trial. A popular method for quantifying uncertainty in a RCT-based CEA is the bootstrap. The objective of the present study was to further expand the bootstrap method of RCT-based CEA for the incorporation of external evidence. We utilize the Bayesian interpretation of the bootstrap and derive the distribution for the cost and effectiveness outcomes after observing the current RCT data and the external evidence. We propose simple modifications of the bootstrap for sampling from such posterior distributions. In a proof-of-concept case study, we use data from a clinical trial and incorporate external evidence on the effect size of treatments to illustrate the method in action. Compared to the parametric models of evidence synthesis, the proposed approach requires fewer distributional assumptions, does not require explicit modeling of the relation between external evidence and outcomes of interest, and is generally easier to implement. A drawback of this approach is potential computational inefficiency compared to the parametric Bayesian methods. The bootstrap method of RCT-based CEA can be extended to incorporate external evidence, while preserving its appealing features such as no requirement for parametric modeling of cost and effectiveness outcomes.

Resampling probability values for weighted kappa with multiple raters.

PubMed

Mielke, Paul W; Berry, Kenneth J; Johnston, Janis E

2008-04-01

A new procedure to compute weighted kappa with multiple raters is described. A resampling procedure to compute approximate probability values for weighted kappa with multiple raters is presented. Applications of weighted kappa are illustrated with an example analysis of classifications by three independent raters.

Simulation and statistics: Like rhythm and song

NASA Astrophysics Data System (ADS)

Othman, Abdul Rahman

2013-04-01

Simulation has been introduced to solve problems in the form of systems. By using this technique the following two problems can be overcome. First, a problem that has an analytical solution but the cost of running an experiment to solve is high in terms of money and lives. Second, a problem exists but has no analytical solution. In the field of statistical inference the second problem is often encountered. With the advent of high-speed computing devices, a statistician can now use resampling techniques such as the bootstrap and permutations to form pseudo sampling distribution that will lead to the solution of the problem that cannot be solved analytically. This paper discusses how a Monte Carlo simulation was and still being used to verify the analytical solution in inference. This paper also discusses the resampling techniques as simulation techniques. The misunderstandings about these two techniques are examined. The successful usages of both techniques are also explained.

Testing the significance of a correlation with nonnormal data: comparison of Pearson, Spearman, transformation, and resampling approaches.

PubMed

Bishara, Anthony J; Hittner, James B

2012-09-01

It is well known that when data are nonnormally distributed, a test of the significance of Pearson's r may inflate Type I error rates and reduce power. Statistics textbooks and the simulation literature provide several alternatives to Pearson's correlation. However, the relative performance of these alternatives has been unclear. Two simulation studies were conducted to compare 12 methods, including Pearson, Spearman's rank-order, transformation, and resampling approaches. With most sample sizes (n ≥ 20), Type I and Type II error rates were minimized by transforming the data to a normal shape prior to assessing the Pearson correlation. Among transformation approaches, a general purpose rank-based inverse normal transformation (i.e., transformation to rankit scores) was most beneficial. However, when samples were both small (n ≤ 10) and extremely nonnormal, the permutation test often outperformed other alternatives, including various bootstrap tests.

Abstract: Inference and Interval Estimation for Indirect Effects With Latent Variable Models.

PubMed

Falk, Carl F; Biesanz, Jeremy C

2011-11-30

Models specifying indirect effects (or mediation) and structural equation modeling are both popular in the social sciences. Yet relatively little research has compared methods that test for indirect effects among latent variables and provided precise estimates of the effectiveness of different methods. This simulation study provides an extensive comparison of methods for constructing confidence intervals and for making inferences about indirect effects with latent variables. We compared the percentile (PC) bootstrap, bias-corrected (BC) bootstrap, bias-corrected accelerated (BC a ) bootstrap, likelihood-based confidence intervals (Neale & Miller, 1997), partial posterior predictive (Biesanz, Falk, and Savalei, 2010), and joint significance tests based on Wald tests or likelihood ratio tests. All models included three reflective latent variables representing the independent, dependent, and mediating variables. The design included the following fully crossed conditions: (a) sample size: 100, 200, and 500; (b) number of indicators per latent variable: 3 versus 5; (c) reliability per set of indicators: .7 versus .9; (d) and 16 different path combinations for the indirect effect (α = 0, .14, .39, or .59; and β = 0, .14, .39, or .59). Simulations were performed using a WestGrid cluster of 1680 3.06GHz Intel Xeon processors running R and OpenMx. Results based on 1,000 replications per cell and 2,000 resamples per bootstrap method indicated that the BC and BC a bootstrap methods have inflated Type I error rates. Likelihood-based confidence intervals and the PC bootstrap emerged as methods that adequately control Type I error and have good coverage rates.

A neurogenetics approach to defining differential susceptibility to institutional care

PubMed Central

Brett, Zoe H.; Sheridan, Margaret; Humphreys, Kate; Smyke, Anna; Gleason, Mary Margaret; Fox, Nathan; Zeanah, Charles; Nelson, Charles; Drury, Stacy

2014-01-01

An individual's neurodevelopmental and cognitive sequelae to negative early experiences may, in part, be explained by genetic susceptibility. We examined whether extreme differences in the early caregiving environment, defined as exposure to severe psychosocial deprivation associated with institutional care compared to normative rearing, interacted with a biologically informed genoset comprising BDNF (rs6265), COMT (rs4680), and SIRT1 (rs3758391) to predict distinct outcomes of neurodevelopment at age 8 (N = 193, 97 males and 96 females). Ethnicity was categorized as Romanian (71%), Roma (21%), unknown (7%), or other (1%). We identified a significant interaction between early caregiving environment (i.e., institutionalized versus never institutionalized children) and the a priori defined genoset for full-scale IQ, two spatial working memory tasks, and prefrontal cortex gray matter volume. Model validation was performed using a bootstrap resampling procedure. Although we hypothesized that the effect of this genoset would operate in a manner consistent with differential susceptibility, our results demonstrate a complex interaction where vantage susceptibility, diathesis stress, and differential susceptibility are implicated. PMID:25663728

A neurogenetics approach to defining differential susceptibility to institutional care.

PubMed

Brett, Zoe H; Sheridan, Margaret; Humphreys, Kate; Smyke, Anna; Gleason, Mary Margaret; Fox, Nathan; Zeanah, Charles; Nelson, Charles; Drury, Stacy

2015-03-01

An individual's neurodevelopmental and cognitive sequelae to negative early experiences may, in part, be explained by genetic susceptibility. We examined whether extreme differences in the early caregiving environment, defined as exposure to severe psychosocial deprivation associated with institutional care compared to normative rearing, interacted with a biologically informed genoset comprising BDNF (rs6265), COMT (rs4680), and SIRT1 (rs3758391) to predict distinct outcomes of neurodevelopment at age 8 ( N = 193, 97 males and 96 females). Ethnicity was categorized as Romanian (71%), Roma (21%), unknown (7%), or other (1%). We identified a significant interaction between early caregiving environment (i.e., institutionalized versus never institutionalized children) and the a priori defined genoset for full-scale IQ, two spatial working memory tasks, and prefrontal cortex gray matter volume. Model validation was performed using a bootstrap resampling procedure. Although we hypothesized that the effect of this genoset would operate in a manner consistent with differential susceptibility, our results demonstrate a complex interaction where vantage susceptibility, diathesis stress, and differential susceptibility are implicated.

Reference Levels for Patient Radiation Doses in Interventional Radiology: Proposed Initial Values for U.S. Practice1

PubMed Central

Miller, Donald L.; Kwon, Deukwoo; Bonavia, Grant H.

2009-01-01

Purpose: To propose initial values for patient reference levels for fluoroscopically guided procedures in the United States. Materials and Methods: This secondary analysis of data from the Radiation Doses in Interventional Radiology Procedures (RAD-IR) study was conducted under a protocol approved by the institutional review board and was HIPAA compliant. Dose distributions (percentiles) were calculated for each type of procedure in the RAD-IR study where there were data from at least 30 cases. Confidence intervals for the dose distributions were determined by using bootstrap resampling. Weight banding and size correction methods for normalizing dose to patient body habitus were tested. Results: The different methods for normalizing patient radiation dose according to patient weight gave results that were not significantly different (P > .05). The 75th percentile patient radiation doses normalized with weight banding were not significantly different from those that were uncorrected for body habitus. Proposed initial reference levels for various interventional procedures are provided for reference air kerma, kerma-area product, fluoroscopy time, and number of images. Conclusion: Sufficient data exist to permit an initial proposal of values for reference levels for interventional radiologic procedures in the United States. For ease of use, reference levels without correction for body habitus are recommended. A national registry of radiation-dose data for interventional radiologic procedures is a necessary next step to refine these reference levels. © RSNA, 2009 Supplemental material: http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.2533090354/-/DC1 PMID:19789226

Generalized Bootstrap Method for Assessment of Uncertainty in Semivariogram Inference

USGS Publications Warehouse

Olea, R.A.; Pardo-Iguzquiza, E.

2011-01-01

The semivariogram and its related function, the covariance, play a central role in classical geostatistics for modeling the average continuity of spatially correlated attributes. Whereas all methods are formulated in terms of the true semivariogram, in practice what can be used are estimated semivariograms and models based on samples. A generalized form of the bootstrap method to properly model spatially correlated data is used to advance knowledge about the reliability of empirical semivariograms and semivariogram models based on a single sample. Among several methods available to generate spatially correlated resamples, we selected a method based on the LU decomposition and used several examples to illustrate the approach. The first one is a synthetic, isotropic, exhaustive sample following a normal distribution, the second example is also a synthetic but following a non-Gaussian random field, and a third empirical sample consists of actual raingauge measurements. Results show wider confidence intervals than those found previously by others with inadequate application of the bootstrap. Also, even for the Gaussian example, distributions for estimated semivariogram values and model parameters are positively skewed. In this sense, bootstrap percentile confidence intervals, which are not centered around the empirical semivariogram and do not require distributional assumptions for its construction, provide an achieved coverage similar to the nominal coverage. The latter cannot be achieved by symmetrical confidence intervals based on the standard error, regardless if the standard error is estimated from a parametric equation or from bootstrap. ?? 2010 International Association for Mathematical Geosciences.

Kolmogorov-Smirnov test for spatially correlated data

USGS Publications Warehouse

Olea, R.A.; Pawlowsky-Glahn, V.

2009-01-01

The Kolmogorov-Smirnov test is a convenient method for investigating whether two underlying univariate probability distributions can be regarded as undistinguishable from each other or whether an underlying probability distribution differs from a hypothesized distribution. Application of the test requires that the sample be unbiased and the outcomes be independent and identically distributed, conditions that are violated in several degrees by spatially continuous attributes, such as topographical elevation. A generalized form of the bootstrap method is used here for the purpose of modeling the distribution of the statistic D of the Kolmogorov-Smirnov test. The innovation is in the resampling, which in the traditional formulation of bootstrap is done by drawing from the empirical sample with replacement presuming independence. The generalization consists of preparing resamplings with the same spatial correlation as the empirical sample. This is accomplished by reading the value of unconditional stochastic realizations at the sampling locations, realizations that are generated by simulated annealing. The new approach was tested by two empirical samples taken from an exhaustive sample closely following a lognormal distribution. One sample was a regular, unbiased sample while the other one was a clustered, preferential sample that had to be preprocessed. Our results show that the p-value for the spatially correlated case is always larger that the p-value of the statistic in the absence of spatial correlation, which is in agreement with the fact that the information content of an uncorrelated sample is larger than the one for a spatially correlated sample of the same size. ?? Springer-Verlag 2008.

A Web-based nomogram predicting para-aortic nodal metastasis in incompletely staged patients with endometrial cancer: a Korean Multicenter Study.

PubMed

Kang, Sokbom; Lee, Jong-Min; Lee, Jae-Kwan; Kim, Jae-Weon; Cho, Chi-Heum; Kim, Seok-Mo; Park, Sang-Yoon; Park, Chan-Yong; Kim, Ki-Tae

2014-03-01

The purpose of this study is to develop a Web-based nomogram for predicting the individualized risk of para-aortic nodal metastasis in incompletely staged patients with endometrial cancer. From 8 institutions, the medical records of 397 patients who underwent pelvic and para-aortic lymphadenectomy as a surgical staging procedure were retrospectively reviewed. A multivariate logistic regression model was created and internally validated by rigorous bootstrap resampling methods. Finally, the model was transformed into a user-friendly Web-based nomogram (http://http://www.kgog.org/nomogram/empa001.html). The rate of para-aortic nodal metastasis was 14.4% (57/397 patients). Using a stepwise variable selection, 4 variables including deep myometrial invasion, non-endometrioid subtype, lymphovascular space invasion, and log-transformed CA-125 levels were finally adopted. After 1000 repetitions of bootstrapping, all of these 4 variables retained a significant association with para-aortic nodal metastasis in the multivariate analysis-deep myometrial invasion (P = 0.001), non-endometrioid histologic subtype (P = 0.034), lymphovascular space invasion (P = 0.003), and log-transformed serum CA-125 levels (P = 0.004). The model showed good discrimination (C statistics = 0.87; 95% confidence interval, 0.82-0.92) and accurate calibration (Hosmer-Lemeshow P = 0.74). This nomogram showed good performance in predicting para-aortic metastasis in patients with endometrial cancer. The tool may be useful in determining the extent of lymphadenectomy after incomplete surgery.

Inadequacy of Conventional Grab Sampling for Remediation Decision-Making for Metal Contamination at Small-Arms Ranges.

PubMed

Clausen, J L; Georgian, T; Gardner, K H; Douglas, T A

2018-01-01

Research shows grab sampling is inadequate for evaluating military ranges contaminated with energetics because of their highly heterogeneous distribution. Similar studies assessing the heterogeneous distribution of metals at small-arms ranges (SAR) are lacking. To address this we evaluated whether grab sampling provides appropriate data for performing risk analysis at metal-contaminated SARs characterized with 30-48 grab samples. We evaluated the extractable metal content of Cu, Pb, Sb, and Zn of the field data using a Monte Carlo random resampling with replacement (bootstrapping) simulation approach. Results indicate the 95% confidence interval of the mean for Pb (432 mg/kg) at one site was 200-700 mg/kg with a data range of 5-4500 mg/kg. Considering the U.S. Environmental Protection Agency screening level for lead is 400 mg/kg, the necessity of cleanup at this site is unclear. Resampling based on populations of 7 and 15 samples, a sample size more realistic for the area yielded high false negative rates.

Uncertainties in the cluster-cluster correlation function

NASA Astrophysics Data System (ADS)

Ling, E. N.; Frenk, C. S.; Barrow, J. D.

1986-12-01

The bootstrap resampling technique is applied to estimate sampling errors and significance levels of the two-point correlation functions determined for a subset of the CfA redshift survey of galaxies and a redshift sample of 104 Abell clusters. The angular correlation function for a sample of 1664 Abell clusters is also calculated. The standard errors in xi(r) for the Abell data are found to be considerably larger than quoted 'Poisson errors'. The best estimate for the ratio of the correlation length of Abell clusters (richness class R greater than or equal to 1, distance class D less than or equal to 4) to that of CfA galaxies is 4.2 + 1.4 or - 1.0 (68 percentile error). The enhancement of cluster clustering over galaxy clustering is statistically significant in the presence of resampling errors. The uncertainties found do not include the effects of possible systematic biases in the galaxy and cluster catalogs and could be regarded as lower bounds on the true uncertainty range.

Unbiased Estimates of Variance Components with Bootstrap Procedures

ERIC Educational Resources Information Center

Brennan, Robert L.

2007-01-01

This article provides general procedures for obtaining unbiased estimates of variance components for any random-model balanced design under any bootstrap sampling plan, with the focus on designs of the type typically used in generalizability theory. The results reported here are particularly helpful when the bootstrap is used to estimate standard…

Frequency Analysis Using Bootstrap Method and SIR Algorithm for Prevention of Natural Disasters

NASA Astrophysics Data System (ADS)

Kim, T.; Kim, Y. S.

2017-12-01

The frequency analysis of hydrometeorological data is one of the most important factors in response to natural disaster damage, and design standards for a disaster prevention facilities. In case of frequency analysis of hydrometeorological data, it assumes that observation data have statistical stationarity, and a parametric method considering the parameter of probability distribution is applied. For a parametric method, it is necessary to sufficiently collect reliable data; however, snowfall observations are needed to compensate for insufficient data in Korea, because of reducing the number of days for snowfall observations and mean maximum daily snowfall depth due to climate change. In this study, we conducted the frequency analysis for snowfall using the Bootstrap method and SIR algorithm which are the resampling methods that can overcome the problems of insufficient data. For the 58 meteorological stations distributed evenly in Korea, the probability of snowfall depth was estimated by non-parametric frequency analysis using the maximum daily snowfall depth data. The results show that probabilistic daily snowfall depth by frequency analysis is decreased at most stations, and most stations representing the rate of change were found to be consistent in both parametric and non-parametric frequency analysis. This study shows that the resampling methods can do the frequency analysis of the snowfall depth that has insufficient observed samples, which can be applied to interpretation of other natural disasters such as summer typhoons with seasonal characteristics. Acknowledgment.This research was supported by a grant(MPSS-NH-2015-79) from Disaster Prediction and Mitigation Technology Development Program funded by Korean Ministry of Public Safety and Security(MPSS).

Positive Traits Linked to Less Pain through Lower Pain Catastrophizing

PubMed Central

Hood, Anna; Pulvers, Kim; Carrillo, Janet; Merchant, Gina; Thomas, Marie

2011-01-01

The present study examined the association between positive traits, pain catastrophizing, and pain perceptions. We hypothesized that pain catastrophizing would mediate the relationship between positive traits and pain. First, participants (n = 114) completed the Trait Hope Scale, the Life Orientation Test- Revised, and the Pain Catastrophizing Scale. Participants then completed the experimental pain stimulus, a cold pressor task, by submerging their hand in a circulating water bath (0º Celsius) for as long as tolerable. Immediately following the task, participants completed the Short-Form McGill Pain Questionnaire (MPQ-SF). Pearson correlation found associations between hope and pain catastrophizing (r = −.41, p < .01) and MPQ-SF scores (r = −.20, p < .05). Optimism was significantly associated with pain catastrophizing (r = −.44, p < .01) and MPQ-SF scores (r = −.19, p < .05). Bootstrapping, a non-parametric resampling procedure, tested for mediation and supported our hypothesis that pain catastrophizing mediated the relationship between positive traits and MPQ-SF pain report. To our knowledge, this investigation is the first to establish that the protective link between positive traits and experimental pain operates through lower pain catastrophizing. PMID:22199416

"You better not leave me shaming!": Conditional indirect effect analyses of anti-fat attitudes, body shame, and fat talk as a function of self-compassion in college women.

PubMed

Webb, Jennifer B; Fiery, Mallory F; Jafari, Nadia

2016-09-01

The present investigation provided a theoretically-driven analysis testing whether body shame helped account for the predicted positive associations between explicit weight bias in the form of possessing anti-fat attitudes (i.e., dislike, fear of fat, and willpower beliefs) and engaging in fat talk among 309 weight-diverse college women. We also evaluated whether self-compassion served as a protective factor in these relationships. Robust non-parametric bootstrap resampling procedures adjusted for body mass index (BMI) revealed stronger indirect and conditional indirect effects for dislike and fear of fat attitudes and weaker, marginal effects for the models inclusive of willpower beliefs. In general, the indirect effect of anti-fat attitudes on fat talk via body shame declined with increasing levels of self-compassion. Our preliminary findings may point to useful process variables to target in mitigating the impact of endorsing anti-fat prejudice on fat talk in college women and may help clarify who is at higher risk. Copyright © 2016 Elsevier Ltd. All rights reserved.

Image restoration techniques as applied to Landsat MSS and TM data

USGS Publications Warehouse

Meyer, David

1987-01-01

Two factors are primarily responsible for the loss of image sharpness in processing digital Landsat images. The first factor is inherent in the data because the sensor's optics and electronics, along with other sensor elements, blur and smear the data. Digital image restoration can be used to reduce this degradation. The second factor, which further degrades by blurring or aliasing, is the resampling performed during geometric correction. An image restoration procedure, when used in place of typical resampled techniques, reduces sensor degradation without introducing the artifacts associated with resampling. The EROS Data Center (EDC) has implemented the restoration proceed for Landsat multispectral scanner (MSS) and thematic mapper (TM) data. This capability, developed at the University of Arizona by Dr. Robert Schowengerdt and Lynette Wood, combines restoration and resampling in a single step to produce geometrically corrected MSS and TM imagery. As with resampling, restoration demands a tradeoff be made between aliasing, which occurs when attempting to extract maximum sharpness from an image, and blurring, which reduces the aliasing problem but sacrifices image sharpness. The restoration procedure used at EDC minimizes these artifacts by being adaptive, tailoring the tradeoff to be optimal for individual images.

Using Landsat Surface Reflectance Data as a Reference Target for Multiswath Hyperspectral Data Collected Over Mixed Agricultural Rangeland Areas

DOE Office of Scientific and Technical Information (OSTI.GOV)

McCann, Cooper; Repasky, Kevin S.; Morin, Mikindra

Low-cost flight-based hyperspectral imaging systems have the potential to provide important information for ecosystem and environmental studies as well as aide in land management. To realize this potential, methods must be developed to provide large-area surface reflectance data allowing for temporal data sets at the mesoscale. This paper describes a bootstrap method of producing a large-area, radiometrically referenced hyperspectral data set using the Landsat surface reflectance (LaSRC) data product as a reference target. The bootstrap method uses standard hyperspectral processing techniques that are extended to remove uneven illumination conditions between flight passes, allowing for radiometrically self-consistent data after mosaicking. Throughmore » selective spectral and spatial resampling, LaSRC data are used as a radiometric reference target. Advantages of the bootstrap method include the need for minimal site access, no ancillary instrumentation, and automated data processing. Data from two hyperspectral flights over the same managed agricultural and unmanaged range land covering approximately 5.8 km 2 acquired on June 21, 2014 and June 24, 2015 are presented. As a result, data from a flight over agricultural land collected on June 6, 2016 are compared with concurrently collected ground-based reflectance spectra as a means of validation.« less

Using Landsat Surface Reflectance Data as a Reference Target for Multiswath Hyperspectral Data Collected Over Mixed Agricultural Rangeland Areas

DOE PAGES

McCann, Cooper; Repasky, Kevin S.; Morin, Mikindra; ...

2017-07-25

Low-cost flight-based hyperspectral imaging systems have the potential to provide important information for ecosystem and environmental studies as well as aide in land management. To realize this potential, methods must be developed to provide large-area surface reflectance data allowing for temporal data sets at the mesoscale. This paper describes a bootstrap method of producing a large-area, radiometrically referenced hyperspectral data set using the Landsat surface reflectance (LaSRC) data product as a reference target. The bootstrap method uses standard hyperspectral processing techniques that are extended to remove uneven illumination conditions between flight passes, allowing for radiometrically self-consistent data after mosaicking. Throughmore » selective spectral and spatial resampling, LaSRC data are used as a radiometric reference target. Advantages of the bootstrap method include the need for minimal site access, no ancillary instrumentation, and automated data processing. Data from two hyperspectral flights over the same managed agricultural and unmanaged range land covering approximately 5.8 km 2 acquired on June 21, 2014 and June 24, 2015 are presented. As a result, data from a flight over agricultural land collected on June 6, 2016 are compared with concurrently collected ground-based reflectance spectra as a means of validation.« less

A non-parametric peak calling algorithm for DamID-Seq.

PubMed

Li, Renhua; Hempel, Leonie U; Jiang, Tingbo

2015-01-01

Protein-DNA interactions play a significant role in gene regulation and expression. In order to identify transcription factor binding sites (TFBS) of double sex (DSX)-an important transcription factor in sex determination, we applied the DNA adenine methylation identification (DamID) technology to the fat body tissue of Drosophila, followed by deep sequencing (DamID-Seq). One feature of DamID-Seq data is that induced adenine methylation signals are not assured to be symmetrically distributed at TFBS, which renders the existing peak calling algorithms for ChIP-Seq, including SPP and MACS, inappropriate for DamID-Seq data. This challenged us to develop a new algorithm for peak calling. A challenge in peaking calling based on sequence data is estimating the averaged behavior of background signals. We applied a bootstrap resampling method to short sequence reads in the control (Dam only). After data quality check and mapping reads to a reference genome, the peaking calling procedure compromises the following steps: 1) reads resampling; 2) reads scaling (normalization) and computing signal-to-noise fold changes; 3) filtering; 4) Calling peaks based on a statistically significant threshold. This is a non-parametric method for peak calling (NPPC). We also used irreproducible discovery rate (IDR) analysis, as well as ChIP-Seq data to compare the peaks called by the NPPC. We identified approximately 6,000 peaks for DSX, which point to 1,225 genes related to the fat body tissue difference between female and male Drosophila. Statistical evidence from IDR analysis indicated that these peaks are reproducible across biological replicates. In addition, these peaks are comparable to those identified by use of ChIP-Seq on S2 cells, in terms of peak number, location, and peaks width.

Predicting pain relief: Use of pre-surgical trigeminal nerve diffusion metrics in trigeminal neuralgia.

PubMed

Hung, Peter S-P; Chen, David Q; Davis, Karen D; Zhong, Jidan; Hodaie, Mojgan

2017-01-01

Trigeminal neuralgia (TN) is a chronic neuropathic facial pain disorder that commonly responds to surgery. A proportion of patients, however, do not benefit and suffer ongoing pain. There are currently no imaging tools that permit the prediction of treatment response. To address this paucity, we used diffusion tensor imaging (DTI) to determine whether pre-surgical trigeminal nerve microstructural diffusivities can prognosticate response to TN treatment. In 31 TN patients and 16 healthy controls, multi-tensor tractography was used to extract DTI-derived metrics-axial (AD), radial (RD), mean diffusivity (MD), and fractional anisotropy (FA)-from the cisternal segment, root entry zone and pontine segment of trigeminal nerves for false discovery rate-corrected Student's t -tests. Ipsilateral diffusivities were bootstrap resampled to visualize group-level diffusivity thresholds of long-term response. To obtain an individual-level statistical classifier of surgical response, we conducted discriminant function analysis (DFA) with the type of surgery chosen alongside ipsilateral measurements and ipsilateral/contralateral ratios of AD and RD from all regions of interest as prediction variables. Abnormal diffusivity in the trigeminal pontine fibers, demonstrated by increased AD, highlighted non-responders (n = 14) compared to controls. Bootstrap resampling revealed three ipsilateral diffusivity thresholds of response-pontine AD, MD, cisternal FA-separating 85% of non-responders from responders. DFA produced an 83.9% (71.0% using leave-one-out-cross-validation) accurate prognosticator of response that successfully identified 12/14 non-responders. Our study demonstrates that pre-surgical DTI metrics can serve as a highly predictive, individualized tool to prognosticate surgical response. We further highlight abnormal pontine segment diffusivities as key features of treatment non-response and confirm the axiom that central pain does not commonly benefit from peripheral treatments.

Estimation and correction of visibility bias in aerial surveys of wintering ducks

USGS Publications Warehouse

Pearse, A.T.; Gerard, P.D.; Dinsmore, S.J.; Kaminski, R.M.; Reinecke, K.J.

2008-01-01

Incomplete detection of all individuals leading to negative bias in abundance estimates is a pervasive source of error in aerial surveys of wildlife, and correcting that bias is a critical step in improving surveys. We conducted experiments using duck decoys as surrogates for live ducks to estimate bias associated with surveys of wintering ducks in Mississippi, USA. We found detection of decoy groups was related to wetland cover type (open vs. forested), group size (1?100 decoys), and interaction of these variables. Observers who detected decoy groups reported counts that averaged 78% of the decoys actually present, and this counting bias was not influenced by either covariate cited above. We integrated this sightability model into estimation procedures for our sample surveys with weight adjustments derived from probabilities of group detection (estimated by logistic regression) and count bias. To estimate variances of abundance estimates, we used bootstrap resampling of transects included in aerial surveys and data from the bias-correction experiment. When we implemented bias correction procedures on data from a field survey conducted in January 2004, we found bias-corrected estimates of abundance increased 36?42%, and associated standard errors increased 38?55%, depending on species or group estimated. We deemed our method successful for integrating correction of visibility bias in an existing sample survey design for wintering ducks in Mississippi, and we believe this procedure could be implemented in a variety of sampling problems for other locations and species.

Correcting Evaluation Bias of Relational Classifiers with Network Cross Validation

DTIC Science & Technology

2010-01-01

classi- fication algorithms: simple random resampling (RRS), equal-instance random resampling (ERS), and network cross-validation ( NCV ). The first two... NCV procedure that eliminates overlap between test sets altogether. The procedure samples for k disjoint test sets that will be used for evaluation...propLabeled ∗ S) nodes from train Pool in f erenceSet =network − trainSet F = F ∪ < trainSet, test Set, in f erenceSet > end for output: F NCV addresses

Patient radiation doses in interventional cardiology in the U.S.: Advisory data sets and possible initial values for U.S. reference levels

DOE Office of Scientific and Technical Information (OSTI.GOV)

Miller, Donald L.; Hilohi, C. Michael; Spelic, David C.

2012-10-15

Purpose: To determine patient radiation doses from interventional cardiology procedures in the U.S and to suggest possible initial values for U.S. benchmarks for patient radiation dose from selected interventional cardiology procedures [fluoroscopically guided diagnostic cardiac catheterization and percutaneous coronary intervention (PCI)]. Methods: Patient radiation dose metrics were derived from analysis of data from the 2008 to 2009 Nationwide Evaluation of X-ray Trends (NEXT) survey of cardiac catheterization. This analysis used deidentified data and did not require review by an IRB. Data from 171 facilities in 30 states were analyzed. The distributions (percentiles) of radiation dose metrics were determined for diagnosticmore » cardiac catheterizations, PCI, and combined diagnostic and PCI procedures. Confidence intervals for these dose distributions were determined using bootstrap resampling. Results: Percentile distributions (advisory data sets) and possible preliminary U.S. reference levels (based on the 75th percentile of the dose distributions) are provided for cumulative air kerma at the reference point (K{sub a,r}), cumulative air kerma-area product (P{sub KA}), fluoroscopy time, and number of cine runs. Dose distributions are sufficiently detailed to permit dose audits as described in National Council on Radiation Protection and Measurements Report No. 168. Fluoroscopy times are consistent with those observed in European studies, but P{sub KA} is higher in the U.S. Conclusions: Sufficient data exist to suggest possible initial benchmarks for patient radiation dose for certain interventional cardiology procedures in the U.S. Our data suggest that patient radiation dose in these procedures is not optimized in U.S. practice.« less

The threshold bootstrap clustering: a new approach to find families or transmission clusters within molecular quasispecies.

PubMed

Prosperi, Mattia C F; De Luca, Andrea; Di Giambenedetto, Simona; Bracciale, Laura; Fabbiani, Massimiliano; Cauda, Roberto; Salemi, Marco

2010-10-25

Phylogenetic methods produce hierarchies of molecular species, inferring knowledge about taxonomy and evolution. However, there is not yet a consensus methodology that provides a crisp partition of taxa, desirable when considering the problem of intra/inter-patient quasispecies classification or infection transmission event identification. We introduce the threshold bootstrap clustering (TBC), a new methodology for partitioning molecular sequences, that does not require a phylogenetic tree estimation. The TBC is an incremental partition algorithm, inspired by the stochastic Chinese restaurant process, and takes advantage of resampling techniques and models of sequence evolution. TBC uses as input a multiple alignment of molecular sequences and its output is a crisp partition of the taxa into an automatically determined number of clusters. By varying initial conditions, the algorithm can produce different partitions. We describe a procedure that selects a prime partition among a set of candidate ones and calculates a measure of cluster reliability. TBC was successfully tested for the identification of type-1 human immunodeficiency and hepatitis C virus subtypes, and compared with previously established methodologies. It was also evaluated in the problem of HIV-1 intra-patient quasispecies clustering, and for transmission cluster identification, using a set of sequences from patients with known transmission event histories. TBC has been shown to be effective for the subtyping of HIV and HCV, and for identifying intra-patient quasispecies. To some extent, the algorithm was able also to infer clusters corresponding to events of infection transmission. The computational complexity of TBC is quadratic in the number of taxa, lower than other established methods; in addition, TBC has been enhanced with a measure of cluster reliability. The TBC can be useful to characterise molecular quasipecies in a broad context.

Trends and variability in the hydrological regime of the Mackenzie River Basin

NASA Astrophysics Data System (ADS)

Abdul Aziz, Omar I.; Burn, Donald H.

2006-03-01

Trends and variability in the hydrological regime were analyzed for the Mackenzie River Basin in northern Canada. The procedure utilized the Mann-Kendall non-parametric test to detect trends, the Trend Free Pre-Whitening (TFPW) approach for correcting time-series data for autocorrelation and a bootstrap resampling method to account for the cross-correlation structure of the data. A total of 19 hydrological and six meteorological variables were selected for the study. Analysis was conducted on hydrological data from a network of 54 hydrometric stations and meteorological data from a network of 10 stations. The results indicated that several hydrological variables exhibit a greater number of significant trends than are expected to occur by chance. Noteworthy were strong increasing trends over the winter month flows of December to April as well as in the annual minimum flow and weak decreasing trends in the early summer and late fall flows as well as in the annual mean flow. An earlier onset of the spring freshet is noted over the basin. The results are expected to assist water resources managers and policy makers in making better planning decisions in the Mackenzie River Basin.

Racial disparities in stage-specific colorectal cancer mortality: 1960-2005.

PubMed

Soneji, Samir; Iyer, Shally Shalini; Armstrong, Katrina; Asch, David A

2010-10-01

We examined whether racial disparities in stage-specific colorectal cancer survival changed between 1960 and 2005. We used US Mortality Multiple-Cause-of-Death Data Files and intercensal estimates to calculate standardized mortality rates by gender and race from 1960 to 2005. We used Surveillance, Epidemiology, and End Results (SEER) data to estimate stage-specific colorectal cancer survival. To account for SEER sampling uncertainty, we used a bootstrap resampling procedure and fit a Cox proportional hazards model. Between 1960-2005, patterns of decline in mortality rate as a result of colorectal cancer differed greatly by gender and race: 54% reduction for White women, 14% reduction for Black women, 39% reduction for White men, and 28% increase for Black men. Blacks consistently experienced worse rates of stage-specific survival and life expectancy than did Whites for both genders, across all age groups, and for localized, regional, and distant stages of the disease. The rates of stage-specific colorectal cancer survival differed among Blacks when compared with Whites during the 4-decade study period. Differences in stage-specific life expectancy were the result of differences in access to care or quality of care. More attention should be given to racial disparities in colorectal cancer management.

Bootstrapping under constraint for the assessment of group behavior in human contact networks

NASA Astrophysics Data System (ADS)

Tremblay, Nicolas; Barrat, Alain; Forest, Cary; Nornberg, Mark; Pinton, Jean-François; Borgnat, Pierre

2013-11-01

The increasing availability of time- and space-resolved data describing human activities and interactions gives insights into both static and dynamic properties of human behavior. In practice, nevertheless, real-world data sets can often be considered as only one realization of a particular event. This highlights a key issue in social network analysis: the statistical significance of estimated properties. In this context, we focus here on the assessment of quantitative features of specific subset of nodes in empirical networks. We present a method of statistical resampling based on bootstrapping groups of nodes under constraints within the empirical network. The method enables us to define acceptance intervals for various null hypotheses concerning relevant properties of the subset of nodes under consideration in order to characterize by a statistical test its behavior as “normal” or not. We apply this method to a high-resolution data set describing the face-to-face proximity of individuals during two colocated scientific conferences. As a case study, we show how to probe whether colocating the two conferences succeeded in bringing together the two corresponding groups of scientists.

Explanation of Two Anomalous Results in Statistical Mediation Analysis.

PubMed

Fritz, Matthew S; Taylor, Aaron B; Mackinnon, David P

2012-01-01

Previous studies of different methods of testing mediation models have consistently found two anomalous results. The first result is elevated Type I error rates for the bias-corrected and accelerated bias-corrected bootstrap tests not found in nonresampling tests or in resampling tests that did not include a bias correction. This is of special concern as the bias-corrected bootstrap is often recommended and used due to its higher statistical power compared with other tests. The second result is statistical power reaching an asymptote far below 1.0 and in some conditions even declining slightly as the size of the relationship between X and M , a , increased. Two computer simulations were conducted to examine these findings in greater detail. Results from the first simulation found that the increased Type I error rates for the bias-corrected and accelerated bias-corrected bootstrap are a function of an interaction between the size of the individual paths making up the mediated effect and the sample size, such that elevated Type I error rates occur when the sample size is small and the effect size of the nonzero path is medium or larger. Results from the second simulation found that stagnation and decreases in statistical power as a function of the effect size of the a path occurred primarily when the path between M and Y , b , was small. Two empirical mediation examples are provided using data from a steroid prevention and health promotion program aimed at high school football players (Athletes Training and Learning to Avoid Steroids; Goldberg et al., 1996), one to illustrate a possible Type I error for the bias-corrected bootstrap test and a second to illustrate a loss in power related to the size of a . Implications of these findings are discussed.

Exact and Monte carlo resampling procedures for the Wilcoxon-Mann-Whitney and Kruskal-Wallis tests.

PubMed

Berry, K J; Mielke, P W

2000-12-01

Exact and Monte Carlo resampling FORTRAN programs are described for the Wilcoxon-Mann-Whitney rank sum test and the Kruskal-Wallis one-way analysis of variance for ranks test. The program algorithms compensate for tied values and do not depend on asymptotic approximations for probability values, unlike most algorithms contained in PC-based statistical software packages.

Problems with Multivariate Normality: Can the Multivariate Bootstrap Help?

ERIC Educational Resources Information Center

Thompson, Bruce

Multivariate normality is required for some statistical tests. This paper explores the implications of violating the assumption of multivariate normality and illustrates a graphical procedure for evaluating multivariate normality. The logic for using the multivariate bootstrap is presented. The multivariate bootstrap can be used when distribution…

Learning predictive models that use pattern discovery--a bootstrap evaluative approach applied in organ functioning sequences.

PubMed

Toma, Tudor; Bosman, Robert-Jan; Siebes, Arno; Peek, Niels; Abu-Hanna, Ameen

2010-08-01

An important problem in the Intensive Care is how to predict on a given day of stay the eventual hospital mortality for a specific patient. A recent approach to solve this problem suggested the use of frequent temporal sequences (FTSs) as predictors. Methods following this approach were evaluated in the past by inducing a model from a training set and validating the prognostic performance on an independent test set. Although this evaluative approach addresses the validity of the specific models induced in an experiment, it falls short of evaluating the inductive method itself. To achieve this, one must account for the inherent sources of variation in the experimental design. The main aim of this work is to demonstrate a procedure based on bootstrapping, specifically the .632 bootstrap procedure, for evaluating inductive methods that discover patterns, such as FTSs. A second aim is to apply this approach to find out whether a recently suggested inductive method that discovers FTSs of organ functioning status is superior over a traditional method that does not use temporal sequences when compared on each successive day of stay at the Intensive Care Unit. The use of bootstrapping with logistic regression using pre-specified covariates is known in the statistical literature. Using inductive methods of prognostic models based on temporal sequence discovery within the bootstrap procedure is however novel at least in predictive models in the Intensive Care. Our results of applying the bootstrap-based evaluative procedure demonstrate the superiority of the FTS-based inductive method over the traditional method in terms of discrimination as well as accuracy. In addition we illustrate the insights gained by the analyst into the discovered FTSs from the bootstrap samples. Copyright 2010 Elsevier Inc. All rights reserved.

Quantifying the risk of extreme aviation accidents

NASA Astrophysics Data System (ADS)

Das, Kumer Pial; Dey, Asim Kumer

2016-12-01

Air travel is considered a safe means of transportation. But when aviation accidents do occur they often result in fatalities. Fortunately, the most extreme accidents occur rarely. However, 2014 was the deadliest year in the past decade causing 111 plane crashes, and among them worst four crashes cause 298, 239, 162 and 116 deaths. In this study, we want to assess the risk of the catastrophic aviation accidents by studying historical aviation accidents. Applying a generalized Pareto model we predict the maximum fatalities from an aviation accident in future. The fitted model is compared with some of its competitive models. The uncertainty in the inferences are quantified using simulated aviation accident series, generated by bootstrap resampling and Monte Carlo simulations.

Statistical inference, the bootstrap, and neural-network modeling with application to foreign exchange rates.

PubMed

White, H; Racine, J

2001-01-01

We propose tests for individual and joint irrelevance of network inputs. Such tests can be used to determine whether an input or group of inputs "belong" in a particular model, thus permitting valid statistical inference based on estimated feedforward neural-network models. The approaches employ well-known statistical resampling techniques. We conduct a small Monte Carlo experiment showing that our tests have reasonable level and power behavior, and we apply our methods to examine whether there are predictable regularities in foreign exchange rates. We find that exchange rates do appear to contain information that is exploitable for enhanced point prediction, but the nature of the predictive relations evolves through time.

Bootstrapping Confidence Intervals for Robust Measures of Association.

ERIC Educational Resources Information Center

King, Jason E.

A Monte Carlo simulation study was conducted to determine the bootstrap correction formula yielding the most accurate confidence intervals for robust measures of association. Confidence intervals were generated via the percentile, adjusted, BC, and BC(a) bootstrap procedures and applied to the Winsorized, percentage bend, and Pearson correlation…

Nonparametric bootstrap analysis with applications to demographic effects in demand functions.

PubMed

Gozalo, P L

1997-12-01

"A new bootstrap proposal, labeled smooth conditional moment (SCM) bootstrap, is introduced for independent but not necessarily identically distributed data, where the classical bootstrap procedure fails.... A good example of the benefits of using nonparametric and bootstrap methods is the area of empirical demand analysis. In particular, we will be concerned with their application to the study of two important topics: what are the most relevant effects of household demographic variables on demand behavior, and to what extent present parametric specifications capture these effects." excerpt

Introducing Statistical Inference to Biology Students through Bootstrapping and Randomization

ERIC Educational Resources Information Center

Lock, Robin H.; Lock, Patti Frazer

2008-01-01

Bootstrap methods and randomization tests are increasingly being used as alternatives to standard statistical procedures in biology. They also serve as an effective introduction to the key ideas of statistical inference in introductory courses for biology students. We discuss the use of such simulation based procedures in an integrated curriculum…

Shape analysis of H II regions - I. Statistical clustering

NASA Astrophysics Data System (ADS)

Campbell-White, Justyn; Froebrich, Dirk; Kume, Alfred

2018-07-01

We present here our shape analysis method for a sample of 76 Galactic H II regions from MAGPIS 1.4 GHz data. The main goal is to determine whether physical properties and initial conditions of massive star cluster formation are linked to the shape of the regions. We outline a systematic procedure for extracting region shapes and perform hierarchical clustering on the shape data. We identified six groups that categorize H II regions by common morphologies. We confirmed the validity of these groupings by bootstrap re-sampling and the ordinance technique multidimensional scaling. We then investigated associations between physical parameters and the assigned groups. Location is mostly independent of group, with a small preference for regions of similar longitudes to share common morphologies. The shapes are homogeneously distributed across Galactocentric distance and latitude. One group contains regions that are all younger than 0.5 Myr and ionized by low- to intermediate-mass sources. Those in another group are all driven by intermediate- to high-mass sources. One group was distinctly separated from the other five and contained regions at the surface brightness detection limit for the survey. We find that our hierarchical procedure is most sensitive to the spatial sampling resolution used, which is determined for each region from its distance. We discuss how these errors can be further quantified and reduced in future work by utilizing synthetic observations from numerical simulations of H II regions. We also outline how this shape analysis has further applications to other diffuse astronomical objects.

Shape Analysis of HII Regions - I. Statistical Clustering

NASA Astrophysics Data System (ADS)

Campbell-White, Justyn; Froebrich, Dirk; Kume, Alfred

2018-04-01

We present here our shape analysis method for a sample of 76 Galactic HII regions from MAGPIS 1.4 GHz data. The main goal is to determine whether physical properties and initial conditions of massive star cluster formation is linked to the shape of the regions. We outline a systematic procedure for extracting region shapes and perform hierarchical clustering on the shape data. We identified six groups that categorise HII regions by common morphologies. We confirmed the validity of these groupings by bootstrap re-sampling and the ordinance technique multidimensional scaling. We then investigated associations between physical parameters and the assigned groups. Location is mostly independent of group, with a small preference for regions of similar longitudes to share common morphologies. The shapes are homogeneously distributed across Galactocentric distance and latitude. One group contains regions that are all younger than 0.5 Myr and ionised by low- to intermediate-mass sources. Those in another group are all driven by intermediate- to high-mass sources. One group was distinctly separated from the other five and contained regions at the surface brightness detection limit for the survey. We find that our hierarchical procedure is most sensitive to the spatial sampling resolution used, which is determined for each region from its distance. We discuss how these errors can be further quantified and reduced in future work by utilising synthetic observations from numerical simulations of HII regions. We also outline how this shape analysis has further applications to other diffuse astronomical objects.

A Primer on Bootstrap Factor Analysis as Applied to Health Studies Research

ERIC Educational Resources Information Center

Lu, Wenhua; Miao, Jingang; McKyer, E. Lisako J.

2014-01-01

Objectives: To demonstrate how the bootstrap method could be conducted in exploratory factor analysis (EFA) with a syntax written in SPSS. Methods: The data obtained from the Texas Childhood Obesity Prevention Policy Evaluation project (T-COPPE project) were used for illustration. A 5-step procedure to conduct bootstrap factor analysis (BFA) was…

Peace of Mind, Academic Motivation, and Academic Achievement in Filipino High School Students.

PubMed

Datu, Jesus Alfonso D

2017-04-09

Recent literature has recognized the advantageous role of low-arousal positive affect such as feelings of peacefulness and internal harmony in collectivist cultures. However, limited research has explored the benefits of low-arousal affective states in the educational setting. The current study examined the link of peace of mind (PoM) to academic motivation (i.e., amotivation, controlled motivation, and autonomous motivation) and academic achievement among 525 Filipino high school students. Findings revealed that PoM was positively associated with academic achievement β = .16, p < .05, autonomous motivation β = .48, p < .001, and controlled motivation β = .25, p < .01. As expected, PoM was negatively related to amotivation β = -.19, p < .05, and autonomous motivation was positively associated with academic achievement β = .52, p < .01. Furthermore, the results of bias-corrected bootstrap analyses at 95% confidence interval based on 5,000 bootstrapped resamples demonstrated that peace of mind had an indirect influence on academic achievement through the mediating effects of autonomous motivation. In terms of the effect sizes, the findings showed that PoM explained about 1% to 18% of the variance in academic achievement and motivation. The theoretical and practical implications of the results are elucidated.

Confidence intervals and hypothesis testing for the Permutation Entropy with an application to epilepsy

NASA Astrophysics Data System (ADS)

Traversaro, Francisco; O. Redelico, Francisco

2018-04-01

In nonlinear dynamics, and to a lesser extent in other fields, a widely used measure of complexity is the Permutation Entropy. But there is still no known method to determine the accuracy of this measure. There has been little research on the statistical properties of this quantity that characterize time series. The literature describes some resampling methods of quantities used in nonlinear dynamics - as the largest Lyapunov exponent - but these seems to fail. In this contribution, we propose a parametric bootstrap methodology using a symbolic representation of the time series to obtain the distribution of the Permutation Entropy estimator. We perform several time series simulations given by well-known stochastic processes: the 1/fα noise family, and show in each case that the proposed accuracy measure is as efficient as the one obtained by the frequentist approach of repeating the experiment. The complexity of brain electrical activity, measured by the Permutation Entropy, has been extensively used in epilepsy research for detection in dynamical changes in electroencephalogram (EEG) signal with no consideration of the variability of this complexity measure. An application of the parametric bootstrap methodology is used to compare normal and pre-ictal EEG signals.

A Satellite Mortality Study to Support Space Systems Lifetime Prediction

NASA Technical Reports Server (NTRS)

Fox, George; Salazar, Ronald; Habib-Agahi, Hamid; Dubos, Gregory

2013-01-01

Estimating the operational lifetime of satellites and spacecraft is a complex process. Operational lifetime can differ from mission design lifetime for a variety of reasons. Unexpected mortality can occur due to human errors in design and fabrication, to human errors in launch and operations, to random anomalies of hardware and software or even satellite function degradation or technology change, leading to unrealized economic or mission return. This study focuses on data collection of public information using, for the first time, a large, publically available dataset, and preliminary analysis of satellite lifetimes, both operational lifetime and design lifetime. The objective of this study is the illustration of the relationship of design life to actual lifetime for some representative classes of satellites and spacecraft. First, a Weibull and Exponential lifetime analysis comparison is performed on the ratio of mission operating lifetime to design life, accounting for terminated and ongoing missions. Next a Kaplan-Meier survivor function, standard practice for clinical trials analysis, is estimated from operating lifetime. Bootstrap resampling is used to provide uncertainty estimates of selected survival probabilities. This study highlights the need for more detailed databases and engineering reliability models of satellite lifetime that include satellite systems and subsystems, operations procedures and environmental characteristics to support the design of complex, multi-generation, long-lived space systems in Earth orbit.

Comparison of Sample Size by Bootstrap and by Formulas Based on Normal Distribution Assumption.

PubMed

Wang, Zuozhen

2018-01-01

Bootstrapping technique is distribution-independent, which provides an indirect way to estimate the sample size for a clinical trial based on a relatively smaller sample. In this paper, sample size estimation to compare two parallel-design arms for continuous data by bootstrap procedure are presented for various test types (inequality, non-inferiority, superiority, and equivalence), respectively. Meanwhile, sample size calculation by mathematical formulas (normal distribution assumption) for the identical data are also carried out. Consequently, power difference between the two calculation methods is acceptably small for all the test types. It shows that the bootstrap procedure is a credible technique for sample size estimation. After that, we compared the powers determined using the two methods based on data that violate the normal distribution assumption. To accommodate the feature of the data, the nonparametric statistical method of Wilcoxon test was applied to compare the two groups in the data during the process of bootstrap power estimation. As a result, the power estimated by normal distribution-based formula is far larger than that by bootstrap for each specific sample size per group. Hence, for this type of data, it is preferable that the bootstrap method be applied for sample size calculation at the beginning, and that the same statistical method as used in the subsequent statistical analysis is employed for each bootstrap sample during the course of bootstrap sample size estimation, provided there is historical true data available that can be well representative of the population to which the proposed trial is planning to extrapolate.

Simulating ensembles of source water quality using a K-nearest neighbor resampling approach.

PubMed

Towler, Erin; Rajagopalan, Balaji; Seidel, Chad; Summers, R Scott

2009-03-01

Climatological, geological, and water management factors can cause significant variability in surface water quality. As drinking water quality standards become more stringent, the ability to quantify the variability of source water quality becomes more important for decision-making and planning in water treatment for regulatory compliance. However, paucity of long-term water quality data makes it challenging to apply traditional simulation techniques. To overcome this limitation, we have developed and applied a robust nonparametric K-nearest neighbor (K-nn) bootstrap approach utilizing the United States Environmental Protection Agency's Information Collection Rule (ICR) data. In this technique, first an appropriate "feature vector" is formed from the best available explanatory variables. The nearest neighbors to the feature vector are identified from the ICR data and are resampled using a weight function. Repetition of this results in water quality ensembles, and consequently the distribution and the quantification of the variability. The main strengths of the approach are its flexibility, simplicity, and the ability to use a large amount of spatial data with limited temporal extent to provide water quality ensembles for any given location. We demonstrate this approach by applying it to simulate monthly ensembles of total organic carbon for two utilities in the U.S. with very different watersheds and to alkalinity and bromide at two other U.S. utilities.

Weighted recalibration of the Rosetta pedotransfer model with improved estimates of hydraulic parameter distributions and summary statistics (Rosetta3)

NASA Astrophysics Data System (ADS)

Zhang, Yonggen; Schaap, Marcel G.

2017-04-01

Pedotransfer functions (PTFs) have been widely used to predict soil hydraulic parameters in favor of expensive laboratory or field measurements. Rosetta (Schaap et al., 2001, denoted as Rosetta1) is one of many PTFs and is based on artificial neural network (ANN) analysis coupled with the bootstrap re-sampling method which allows the estimation of van Genuchten water retention parameters (van Genuchten, 1980, abbreviated here as VG), saturated hydraulic conductivity (Ks), and their uncertainties. In this study, we present an improved set of hierarchical pedotransfer functions (Rosetta3) that unify the water retention and Ks submodels into one. Parameter uncertainty of the fit of the VG curve to the original retention data is used in the ANN calibration procedure to reduce bias of parameters predicted by the new PTF. One thousand bootstrap replicas were used to calibrate the new models compared to 60 or 100 in Rosetta1, thus allowing the uni-variate and bi-variate probability distributions of predicted parameters to be quantified in greater detail. We determined the optimal weights for VG parameters and Ks, the optimal number of hidden nodes in ANN, and the number of bootstrap replicas required for statistically stable estimates. Results show that matric potential-dependent bias was reduced significantly while root mean square error (RMSE) for water content were reduced modestly; RMSE for Ks was increased by 0.9% (H3w) to 3.3% (H5w) in the new models on log scale of Ks compared with the Rosetta1 model. It was found that estimated distributions of parameters were mildly non-Gaussian and could instead be described rather well with heavy-tailed α-stable distributions. On the other hand, arithmetic means had only a small estimation bias for most textures when compared with the mean-like "shift" parameter of the α-stable distributions. Arithmetic means and (co-)variances are therefore still recommended as summary statistics of the estimated distributions. However, it may be necessary to parameterize the distributions in different ways if the new estimates are used in stochastic analyses of vadose zone flow and transport. Rosetta1 and Posetta3 were implemented in the python programming language, and the source code as well as additional documentation is available at: http://www.cals.arizona.edu/research/rosettav3.html.

Resampling-Based Empirical Bayes Multiple Testing Procedures for Controlling Generalized Tail Probability and Expected Value Error Rates: Focus on the False Discovery Rate and Simulation Study

PubMed Central

Dudoit, Sandrine; Gilbert, Houston N.; van der Laan, Mark J.

2014-01-01

Summary This article proposes resampling-based empirical Bayes multiple testing procedures for controlling a broad class of Type I error rates, defined as generalized tail probability (gTP) error rates, gTP(q, g) = Pr(g(Vn, Sn) > q), and generalized expected value (gEV) error rates, gEV(g) = E[g(Vn, Sn)], for arbitrary functions g(Vn, Sn) of the numbers of false positives Vn and true positives Sn. Of particular interest are error rates based on the proportion g(Vn, Sn) = Vn/(Vn + Sn) of Type I errors among the rejected hypotheses, such as the false discovery rate (FDR), FDR = E[Vn/(Vn + Sn)]. The proposed procedures offer several advantages over existing methods. They provide Type I error control for general data generating distributions, with arbitrary dependence structures among variables. Gains in power are achieved by deriving rejection regions based on guessed sets of true null hypotheses and null test statistics randomly sampled from joint distributions that account for the dependence structure of the data. The Type I error and power properties of an FDR-controlling version of the resampling-based empirical Bayes approach are investigated and compared to those of widely-used FDR-controlling linear step-up procedures in a simulation study. The Type I error and power trade-off achieved by the empirical Bayes procedures under a variety of testing scenarios allows this approach to be competitive with or outperform the Storey and Tibshirani (2003) linear step-up procedure, as an alternative to the classical Benjamini and Hochberg (1995) procedure. PMID:18932138

Accounting for Uncertainty in Decision Analytic Models Using Rank Preserving Structural Failure Time Modeling: Application to Parametric Survival Models.

PubMed

Bennett, Iain; Paracha, Noman; Abrams, Keith; Ray, Joshua

2018-01-01

Rank Preserving Structural Failure Time models are one of the most commonly used statistical methods to adjust for treatment switching in oncology clinical trials. The method is often applied in a decision analytic model without appropriately accounting for additional uncertainty when determining the allocation of health care resources. The aim of the study is to describe novel approaches to adequately account for uncertainty when using a Rank Preserving Structural Failure Time model in a decision analytic model. Using two examples, we tested and compared the performance of the novel Test-based method with the resampling bootstrap method and with the conventional approach of no adjustment. In the first example, we simulated life expectancy using a simple decision analytic model based on a hypothetical oncology trial with treatment switching. In the second example, we applied the adjustment method on published data when no individual patient data were available. Mean estimates of overall and incremental life expectancy were similar across methods. However, the bootstrapped and test-based estimates consistently produced greater estimates of uncertainty compared with the estimate without any adjustment applied. Similar results were observed when using the test based approach on a published data showing that failing to adjust for uncertainty led to smaller confidence intervals. Both the bootstrapping and test-based approaches provide a solution to appropriately incorporate uncertainty, with the benefit that the latter can implemented by researchers in the absence of individual patient data. Copyright © 2018 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Modeling of correlated data with informative cluster sizes: An evaluation of joint modeling and within-cluster resampling approaches.

PubMed

Zhang, Bo; Liu, Wei; Zhang, Zhiwei; Qu, Yanping; Chen, Zhen; Albert, Paul S

2017-08-01

Joint modeling and within-cluster resampling are two approaches that are used for analyzing correlated data with informative cluster sizes. Motivated by a developmental toxicity study, we examined the performances and validity of these two approaches in testing covariate effects in generalized linear mixed-effects models. We show that the joint modeling approach is robust to the misspecification of cluster size models in terms of Type I and Type II errors when the corresponding covariates are not included in the random effects structure; otherwise, statistical tests may be affected. We also evaluate the performance of the within-cluster resampling procedure and thoroughly investigate the validity of it in modeling correlated data with informative cluster sizes. We show that within-cluster resampling is a valid alternative to joint modeling for cluster-specific covariates, but it is invalid for time-dependent covariates. The two methods are applied to a developmental toxicity study that investigated the effect of exposure to diethylene glycol dimethyl ether.

Using Cluster Bootstrapping to Analyze Nested Data With a Few Clusters.

PubMed

Huang, Francis L

2018-04-01

Cluster randomized trials involving participants nested within intact treatment and control groups are commonly performed in various educational, psychological, and biomedical studies. However, recruiting and retaining intact groups present various practical, financial, and logistical challenges to evaluators and often, cluster randomized trials are performed with a low number of clusters (~20 groups). Although multilevel models are often used to analyze nested data, researchers may be concerned of potentially biased results due to having only a few groups under study. Cluster bootstrapping has been suggested as an alternative procedure when analyzing clustered data though it has seen very little use in educational and psychological studies. Using a Monte Carlo simulation that varied the number of clusters, average cluster size, and intraclass correlations, we compared standard errors using cluster bootstrapping with those derived using ordinary least squares regression and multilevel models. Results indicate that cluster bootstrapping, though more computationally demanding, can be used as an alternative procedure for the analysis of clustered data when treatment effects at the group level are of primary interest. Supplementary material showing how to perform cluster bootstrapped regressions using R is also provided.

Topics in Statistical Calibration

DTIC Science & Technology

2014-03-27

on a parametric bootstrap where, instead of sampling directly from the residuals , samples are drawn from a normal distribution. This procedure will...addition to centering them (Davison and Hinkley, 1997). When there are outliers in the residuals , the bootstrap distribution of x̂0 can become skewed or...based and inversion methods using the linear mixed-effects model. Then, a simple parametric bootstrap algorithm is proposed that can be used to either

Terrestrial laser scanning used to detect asymmetries in boat hulls

NASA Astrophysics Data System (ADS)

Roca-Pardiñas, Javier; López-Alvarez, Francisco; Ordóñez, Celestino; Menéndez, Agustín; Bernardo-Sánchez, Antonio

2012-01-01

We describe a methodology for identifying asymmetries in boat hull sections reconstructed from point clouds captured using a terrestrial laser scanner (TLS). A surface was first fit to the point cloud using a nonparametric regression method that permitted the construction of a continuous smooth surface. Asymmetries in cross-sections of the surface were identified using a bootstrap resampling technique that took into account uncertainty in the coordinates of the scanned points. Each reconstructed section was analyzed to check, for a given level of significance, that it was within the confidence interval for the theoretical symmetrical section. The method was applied to the study of asymmetries in a medium-sized yacht. Identified were differences of up to 5 cm between the real and theoretical sections in some parts of the hull.

Nomogram Prediction of Overall Survival After Curative Irradiation for Uterine Cervical Cancer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Seo, YoungSeok; Yoo, Seong Yul; Kim, Mi-Sook

Purpose: The purpose of this study was to develop a nomogram capable of predicting the probability of 5-year survival after radical radiotherapy (RT) without chemotherapy for uterine cervical cancer. Methods and Materials: We retrospectively analyzed 549 patients that underwent radical RT for uterine cervical cancer between March 1994 and April 2002 at our institution. Multivariate analysis using Cox proportional hazards regression was performed and this Cox model was used as the basis for the devised nomogram. The model was internally validated for discrimination and calibration by bootstrap resampling. Results: By multivariate regression analysis, the model showed that age, hemoglobin levelmore » before RT, Federation Internationale de Gynecologie Obstetrique (FIGO) stage, maximal tumor diameter, lymph node status, and RT dose at Point A significantly predicted overall survival. The survival prediction model demonstrated good calibration and discrimination. The bootstrap-corrected concordance index was 0.67. The predictive ability of the nomogram proved to be superior to FIGO stage (p = 0.01). Conclusions: The devised nomogram offers a significantly better level of discrimination than the FIGO staging system. In particular, it improves predictions of survival probability and could be useful for counseling patients, choosing treatment modalities and schedules, and designing clinical trials. However, before this nomogram is used clinically, it should be externally validated.« less

Automated modal parameter estimation using correlation analysis and bootstrap sampling

NASA Astrophysics Data System (ADS)

Yaghoubi, Vahid; Vakilzadeh, Majid K.; Abrahamsson, Thomas J. S.

2018-02-01

The estimation of modal parameters from a set of noisy measured data is a highly judgmental task, with user expertise playing a significant role in distinguishing between estimated physical and noise modes of a test-piece. Various methods have been developed to automate this procedure. The common approach is to identify models with different orders and cluster similar modes together. However, most proposed methods based on this approach suffer from high-dimensional optimization problems in either the estimation or clustering step. To overcome this problem, this study presents an algorithm for autonomous modal parameter estimation in which the only required optimization is performed in a three-dimensional space. To this end, a subspace-based identification method is employed for the estimation and a non-iterative correlation-based method is used for the clustering. This clustering is at the heart of the paper. The keys to success are correlation metrics that are able to treat the problems of spatial eigenvector aliasing and nonunique eigenvectors of coalescent modes simultaneously. The algorithm commences by the identification of an excessively high-order model from frequency response function test data. The high number of modes of this model provides bases for two subspaces: one for likely physical modes of the tested system and one for its complement dubbed the subspace of noise modes. By employing the bootstrap resampling technique, several subsets are generated from the same basic dataset and for each of them a model is identified to form a set of models. Then, by correlation analysis with the two aforementioned subspaces, highly correlated modes of these models which appear repeatedly are clustered together and the noise modes are collected in a so-called Trashbox cluster. Stray noise modes attracted to the mode clusters are trimmed away in a second step by correlation analysis. The final step of the algorithm is a fuzzy c-means clustering procedure applied to a three-dimensional feature space to assign a degree of physicalness to each cluster. The proposed algorithm is applied to two case studies: one with synthetic data and one with real test data obtained from a hammer impact test. The results indicate that the algorithm successfully clusters similar modes and gives a reasonable quantification of the extent to which each cluster is physical.

Fast, Exact Bootstrap Principal Component Analysis for p > 1 million

PubMed Central

Fisher, Aaron; Caffo, Brian; Schwartz, Brian; Zipunnikov, Vadim

2015-01-01

Many have suggested a bootstrap procedure for estimating the sampling variability of principal component analysis (PCA) results. However, when the number of measurements per subject (p) is much larger than the number of subjects (n), calculating and storing the leading principal components from each bootstrap sample can be computationally infeasible. To address this, we outline methods for fast, exact calculation of bootstrap principal components, eigenvalues, and scores. Our methods leverage the fact that all bootstrap samples occupy the same n-dimensional subspace as the original sample. As a result, all bootstrap principal components are limited to the same n-dimensional subspace and can be efficiently represented by their low dimensional coordinates in that subspace. Several uncertainty metrics can be computed solely based on the bootstrap distribution of these low dimensional coordinates, without calculating or storing the p-dimensional bootstrap components. Fast bootstrap PCA is applied to a dataset of sleep electroencephalogram recordings (p = 900, n = 392), and to a dataset of brain magnetic resonance images (MRIs) (p ≈ 3 million, n = 352). For the MRI dataset, our method allows for standard errors for the first 3 principal components based on 1000 bootstrap samples to be calculated on a standard laptop in 47 minutes, as opposed to approximately 4 days with standard methods. PMID:27616801

Predicting survival of men with recurrent prostate cancer after radical prostatectomy.

PubMed

Dell'Oglio, Paolo; Suardi, Nazareno; Boorjian, Stephen A; Fossati, Nicola; Gandaglia, Giorgio; Tian, Zhe; Moschini, Marco; Capitanio, Umberto; Karakiewicz, Pierre I; Montorsi, Francesco; Karnes, R Jeffrey; Briganti, Alberto

2016-02-01

To develop and externally validate a novel nomogram aimed at predicting cancer-specific mortality (CSM) after biochemical recurrence (BCR) among prostate cancer (PCa) patients treated with radical prostatectomy (RP) with or without adjuvant external beam radiotherapy (aRT) and/or hormonal therapy (aHT). The development cohort included 689 consecutive PCa patients treated with RP between 1987 and 2011 with subsequent BCR, defined as two subsequent prostate-specific antigen values >0.2 ng/ml. Multivariable competing-risks regression analyses tested the predictors of CSM after BCR for the purpose of 5-year CSM nomogram development. Validation (2000 bootstrap resamples) was internally tested. External validation was performed into a population of 6734 PCa patients with BCR after treatment with RP at the Mayo Clinic from 1987 to 2011. The predictive accuracy (PA) was quantified using the receiver operating characteristic-derived area under the curve and the calibration plot method. The 5-year CSM-free survival rate was 83.6% (confidence interval [CI]: 79.6-87.2). In multivariable analyses, pathologic stage T3b or more (hazard ratio [HR]: 7.42; p = 0.008), pathologic Gleason score 8-10 (HR: 2.19; p = 0.003), lymph node invasion (HR: 3.57; p = 0.001), time to BCR (HR: 0.99; p = 0.03) and age at BCR (HR: 1.04; p = 0.04), were each significantly associated with the risk of CSM after BCR. The bootstrap-corrected PA was 87.4% (bootstrap 95% CI: 82.0-91.7%). External validation of our nomogram showed a good PA at 83.2%. We developed and externally validated the first nomogram predicting 5-year CSM applicable to contemporary patients with BCR after RP with or without adjuvant treatment. Copyright © 2015 Elsevier Ltd. All rights reserved.

A Bootstrap Procedure of Propensity Score Estimation

ERIC Educational Resources Information Center

Bai, Haiyan

2013-01-01

Propensity score estimation plays a fundamental role in propensity score matching for reducing group selection bias in observational data. To increase the accuracy of propensity score estimation, the author developed a bootstrap propensity score. The commonly used propensity score matching methods: nearest neighbor matching, caliper matching, and…

Bootstrap Prediction Intervals in Non-Parametric Regression with Applications to Anomaly Detection

NASA Technical Reports Server (NTRS)

Kumar, Sricharan; Srivistava, Ashok N.

2012-01-01

Prediction intervals provide a measure of the probable interval in which the outputs of a regression model can be expected to occur. Subsequently, these prediction intervals can be used to determine if the observed output is anomalous or not, conditioned on the input. In this paper, a procedure for determining prediction intervals for outputs of nonparametric regression models using bootstrap methods is proposed. Bootstrap methods allow for a non-parametric approach to computing prediction intervals with no specific assumptions about the sampling distribution of the noise or the data. The asymptotic fidelity of the proposed prediction intervals is theoretically proved. Subsequently, the validity of the bootstrap based prediction intervals is illustrated via simulations. Finally, the bootstrap prediction intervals are applied to the problem of anomaly detection on aviation data.

Bootstrap Estimation and Testing for Variance Equality.

ERIC Educational Resources Information Center

Olejnik, Stephen; Algina, James

The purpose of this study was to develop a single procedure for comparing population variances which could be used for distribution forms. Bootstrap methodology was used to estimate the variability of the sample variance statistic when the population distribution was normal, platykurtic and leptokurtic. The data for the study were generated and…

Conditional Monthly Weather Resampling Procedure for Operational Seasonal Water Resources Forecasting

NASA Astrophysics Data System (ADS)

Beckers, J.; Weerts, A.; Tijdeman, E.; Welles, E.; McManamon, A.

2013-12-01

To provide reliable and accurate seasonal streamflow forecasts for water resources management several operational hydrologic agencies and hydropower companies around the world use the Extended Streamflow Prediction (ESP) procedure. The ESP in its original implementation does not accommodate for any additional information that the forecaster may have about expected deviations from climatology in the near future. Several attempts have been conducted to improve the skill of the ESP forecast, especially for areas which are affected by teleconnetions (e,g. ENSO, PDO) via selection (Hamlet and Lettenmaier, 1999) or weighting schemes (Werner et al., 2004; Wood and Lettenmaier, 2006; Najafi et al., 2012). A disadvantage of such schemes is that they lead to a reduction of the signal to noise ratio of the probabilistic forecast. To overcome this, we propose a resampling method conditional on climate indices to generate meteorological time series to be used in the ESP. The method can be used to generate a large number of meteorological ensemble members in order to improve the statistical properties of the ensemble. The effectiveness of the method was demonstrated in a real-time operational hydrologic seasonal forecasts system for the Columbia River basin operated by the Bonneville Power Administration. The forecast skill of the k-nn resampler was tested against the original ESP for three basins at the long-range seasonal time scale. The BSS and CRPSS were used to compare the results to those of the original ESP method. Positive forecast skill scores were found for the resampler method conditioned on different indices for the prediction of spring peak flows in the Dworshak and Hungry Horse basin. For the Libby Dam basin however, no improvement of skill was found. The proposed resampling method is a promising practical approach that can add skill to ESP forecasts at the seasonal time scale. Further improvement is possible by fine tuning the method and selecting the most informative climate indices for the region of interest.

The Effect of Social Problem Solving Skills in the Relationship between Traumatic Stress and Moral Disengagement among Inner-City African American High School Students

PubMed Central

Coker, Kendell L.; Ikpe, Uduakobong N.; Brooks, Jeannie S.; Page, Brian; Sobell, Mark B.

2014-01-01

This study examined the relationship between traumatic stress, social problem solving, and moral disengagement among African American inner-city high school students. Participants consisted of 45 (25 males and 20 females) African American students enrolled in grades 10 through 12. Mediation was assessed by testing for the indirect effect using the confidence interval derived from 10,000 bootstrapped resamples. The results revealed that social problem-solving skills have an indirect effect on the relationship between traumatic stress and moral disengagement. The findings suggest that African American youth that are negatively impacted by trauma evidence deficits in their social problem solving skills and are likely to be at an increased risk to morally disengage. Implications for culturally sensitive and trauma-based intervention programs are also provided. PMID:25071874

Effect of non-normality on test statistics for one-way independent groups designs.

PubMed

Cribbie, Robert A; Fiksenbaum, Lisa; Keselman, H J; Wilcox, Rand R

2012-02-01

The data obtained from one-way independent groups designs is typically non-normal in form and rarely equally variable across treatment populations (i.e., population variances are heterogeneous). Consequently, the classical test statistic that is used to assess statistical significance (i.e., the analysis of variance F test) typically provides invalid results (e.g., too many Type I errors, reduced power). For this reason, there has been considerable interest in finding a test statistic that is appropriate under conditions of non-normality and variance heterogeneity. Previously recommended procedures for analysing such data include the James test, the Welch test applied either to the usual least squares estimators of central tendency and variability, or the Welch test with robust estimators (i.e., trimmed means and Winsorized variances). A new statistic proposed by Krishnamoorthy, Lu, and Mathew, intended to deal with heterogeneous variances, though not non-normality, uses a parametric bootstrap procedure. In their investigation of the parametric bootstrap test, the authors examined its operating characteristics under limited conditions and did not compare it to the Welch test based on robust estimators. Thus, we investigated how the parametric bootstrap procedure and a modified parametric bootstrap procedure based on trimmed means perform relative to previously recommended procedures when data are non-normal and heterogeneous. The results indicated that the tests based on trimmed means offer the best Type I error control and power when variances are unequal and at least some of the distribution shapes are non-normal. © 2011 The British Psychological Society.

Community level patterns in diverse systems: A case study of litter fauna in a Mexican pine-oak forest using higher taxa surrogates and re-sampling methods

NASA Astrophysics Data System (ADS)

Moreno, Claudia E.; Guevara, Roger; Sánchez-Rojas, Gerardo; Téllez, Dianeis; Verdú, José R.

2008-01-01

Environmental assessment at the community level in highly diverse ecosystems is limited by taxonomic constraints and statistical methods requiring true replicates. Our objective was to show how diverse systems can be studied at the community level using higher taxa as biodiversity surrogates, and re-sampling methods to allow comparisons. To illustrate this we compared the abundance, richness, evenness and diversity of the litter fauna in a pine-oak forest in central Mexico among seasons, sites and collecting methods. We also assessed changes in the abundance of trophic guilds and evaluated the relationships between community parameters and litter attributes. With the direct search method we observed differences in the rate of taxa accumulation between sites. Bootstrap analysis showed that abundance varied significantly between seasons and sampling methods, but not between sites. In contrast, diversity and evenness were significantly higher at the managed than at the non-managed site. Tree regression models show that abundance varied mainly between seasons, whereas taxa richness was affected by litter attributes (composition and moisture content). The abundance of trophic guilds varied among methods and seasons, but overall we found that parasitoids, predators and detrivores decreased under management. Therefore, although our results suggest that management has positive effects on the richness and diversity of litter fauna, the analysis of trophic guilds revealed a contrasting story. Our results indicate that functional groups and re-sampling methods may be used as tools for describing community patterns in highly diverse systems. Also, the higher taxa surrogacy could be seen as a preliminary approach when it is not possible to identify the specimens at a low taxonomic level in a reasonable period of time and in a context of limited financial resources, but further studies are needed to test whether the results are specific to a system or whether they are general with regards to land management.

The intrinsic dependence structure of peak, volume, duration, and average intensity of hyetographs and hydrographs

NASA Astrophysics Data System (ADS)

Serinaldi, Francesco; Kilsby, Chris G.

2013-06-01

The information contained in hyetographs and hydrographs is often synthesized by using key properties such as the peak or maximum value Xp, volume V, duration D, and average intensity I. These variables play a fundamental role in hydrologic engineering as they are used, for instance, to define design hyetographs and hydrographs as well as to model and simulate the rainfall and streamflow processes. Given their inherent variability and the empirical evidence of the presence of a significant degree of association, such quantities have been studied as correlated random variables suitable to be modeled by multivariate joint distribution functions. The advent of copulas in geosciences simplified the inference procedures allowing for splitting the analysis of the marginal distributions and the study of the so-called dependence structure or copula. However, the attention paid to the modeling task has overlooked a more thorough study of the true nature and origin of the relationships that link Xp,V,D, and I. In this study, we apply a set of ad hoc bootstrap algorithms to investigate these aspects by analyzing the hyetographs and hydrographs extracted from 282 daily rainfall series from central eastern Europe, three 5 min rainfall series from central Italy, 80 daily streamflow series from the continental United States, and two sets of 200 simulated universal multifractal time series. Our results show that all the pairwise dependence structures between Xp,V,D, and I exhibit some key properties that can be reproduced by simple bootstrap algorithms that rely on a standard univariate resampling without resort to multivariate techniques. Therefore, the strong similarities between the observed dependence structures and the agreement between the observed and bootstrap samples suggest the existence of a numerical generating mechanism based on the superposition of the effects of sampling data at finite time steps and the process of summing realizations of independent random variables over random durations. We also show that the pairwise dependence structures are weakly dependent on the internal patterns of the hyetographs and hydrographs, meaning that the temporal evolution of the rainfall and runoff events marginally influences the mutual relationships of Xp,V,D, and I. Finally, our findings point out that subtle and often overlooked deterministic relationships between the properties of the event hyetographs and hydrographs exist. Confusing these relationships with genuine stochastic relationships can lead to an incorrect application of multivariate distributions and copulas and to misleading results.

Pearson-type goodness-of-fit test with bootstrap maximum likelihood estimation.

PubMed

Yin, Guosheng; Ma, Yanyuan

2013-01-01

The Pearson test statistic is constructed by partitioning the data into bins and computing the difference between the observed and expected counts in these bins. If the maximum likelihood estimator (MLE) of the original data is used, the statistic generally does not follow a chi-squared distribution or any explicit distribution. We propose a bootstrap-based modification of the Pearson test statistic to recover the chi-squared distribution. We compute the observed and expected counts in the partitioned bins by using the MLE obtained from a bootstrap sample. This bootstrap-sample MLE adjusts exactly the right amount of randomness to the test statistic, and recovers the chi-squared distribution. The bootstrap chi-squared test is easy to implement, as it only requires fitting exactly the same model to the bootstrap data to obtain the corresponding MLE, and then constructs the bin counts based on the original data. We examine the test size and power of the new model diagnostic procedure using simulation studies and illustrate it with a real data set.

Estimation of daily interfractional larynx residual setup error after isocentric alignment for head and neck radiotherapy: Quality-assurance implications for target volume and organ-at-risk margination using daily CT-on-rails imaging

PubMed Central

Baron, Charles A.; Awan, Musaddiq J.; Mohamed, Abdallah S. R.; Akel, Imad; Rosenthal, David I.; Gunn, G. Brandon; Garden, Adam S.; Dyer, Brandon A.; Court, Laurence; Sevak, Parag R; Kocak-Uzel, Esengul; Fuller, Clifton D.

2016-01-01

Larynx may alternatively serve as a target or organ-at-risk (OAR) in head and neck cancer (HNC) image-guided radiotherapy (IGRT). The objective of this study was to estimate IGRT parameters required for larynx positional error independent of isocentric alignment and suggest population–based compensatory margins. Ten HNC patients receiving radiotherapy (RT) with daily CT-on-rails imaging were assessed. Seven landmark points were placed on each daily scan. Taking the most superior anterior point of the C5 vertebra as a reference isocenter for each scan, residual displacement vectors to the other 6 points were calculated post-isocentric alignment. Subsequently, using the first scan as a reference, the magnitude of vector differences for all 6 points for all scans over the course of treatment were calculated. Residual systematic and random error, and the necessary compensatory CTV-to-PTV and OAR-to-PRV margins were calculated, using both observational cohort data and a bootstrap-resampled population estimator. The grand mean displacements for all anatomical points was 5.07mm, with mean systematic error of 1.1mm and mean random setup error of 2.63mm, while bootstrapped POIs grand mean displacement was 5.09mm, with mean systematic error of 1.23mm and mean random setup error of 2.61mm. Required margin for CTV-PTV expansion was 4.6mm for all cohort points, while the bootstrap estimator of the equivalent margin was 4.9mm. The calculated OAR-to-PRV expansion for the observed residual set-up error was 2.7mm, and bootstrap estimated expansion of 2.9mm. We conclude that the interfractional larynx setup error is a significant source of RT set-up/delivery error in HNC both when the larynx is considered as a CTV or OAR. We estimate the need for a uniform expansion of 5mm to compensate for set up error if the larynx is a target or 3mm if the larynx is an OAR when using a non-laryngeal bony isocenter. PMID:25679151

Comparison of variance estimators for meta-analysis of instrumental variable estimates

PubMed Central

Schmidt, AF; Hingorani, AD; Jefferis, BJ; White, J; Groenwold, RHH; Dudbridge, F

2016-01-01

Abstract Background: Mendelian randomization studies perform instrumental variable (IV) analysis using genetic IVs. Results of individual Mendelian randomization studies can be pooled through meta-analysis. We explored how different variance estimators influence the meta-analysed IV estimate. Methods: Two versions of the delta method (IV before or after pooling), four bootstrap estimators, a jack-knife estimator and a heteroscedasticity-consistent (HC) variance estimator were compared using simulation. Two types of meta-analyses were compared, a two-stage meta-analysis pooling results, and a one-stage meta-analysis pooling datasets. Results: Using a two-stage meta-analysis, coverage of the point estimate using bootstrapped estimators deviated from nominal levels at weak instrument settings and/or outcome probabilities ≤ 0.10. The jack-knife estimator was the least biased resampling method, the HC estimator often failed at outcome probabilities ≤ 0.50 and overall the delta method estimators were the least biased. In the presence of between-study heterogeneity, the delta method before meta-analysis performed best. Using a one-stage meta-analysis all methods performed equally well and better than two-stage meta-analysis of greater or equal size. Conclusions: In the presence of between-study heterogeneity, two-stage meta-analyses should preferentially use the delta method before meta-analysis. Weak instrument bias can be reduced by performing a one-stage meta-analysis. PMID:27591262

A neurocomputational theory of how explicit learning bootstraps early procedural learning.

PubMed

Paul, Erick J; Ashby, F Gregory

2013-01-01

It is widely accepted that human learning and memory is mediated by multiple memory systems that are each best suited to different requirements and demands. Within the domain of categorization, at least two systems are thought to facilitate learning: an explicit (declarative) system depending largely on the prefrontal cortex, and a procedural (non-declarative) system depending on the basal ganglia. Substantial evidence suggests that each system is optimally suited to learn particular categorization tasks. However, it remains unknown precisely how these systems interact to produce optimal learning and behavior. In order to investigate this issue, the present research evaluated the progression of learning through simulation of categorization tasks using COVIS, a well-known model of human category learning that includes both explicit and procedural learning systems. Specifically, the model's parameter space was thoroughly explored in procedurally learned categorization tasks across a variety of conditions and architectures to identify plausible interaction architectures. The simulation results support the hypothesis that one-way interaction between the systems occurs such that the explicit system "bootstraps" learning early on in the procedural system. Thus, the procedural system initially learns a suboptimal strategy employed by the explicit system and later refines its strategy. This bootstrapping could be from cortical-striatal projections that originate in premotor or motor regions of cortex, or possibly by the explicit system's control of motor responses through basal ganglia-mediated loops.

A Class of Population Covariance Matrices in the Bootstrap Approach to Covariance Structure Analysis

ERIC Educational Resources Information Center

Yuan, Ke-Hai; Hayashi, Kentaro; Yanagihara, Hirokazu

2007-01-01

Model evaluation in covariance structure analysis is critical before the results can be trusted. Due to finite sample sizes and unknown distributions of real data, existing conclusions regarding a particular statistic may not be applicable in practice. The bootstrap procedure automatically takes care of the unknown distribution and, for a given…

Calculating Confidence Intervals for Regional Economic Impacts of Recreastion by Bootstrapping Visitor Expenditures

Treesearch

Donald B.K. English

2000-01-01

In this paper I use bootstrap procedures to develop confidence intervals for estimates of total industrial output generated per thousand tourist visits. Mean expenditures from replicated visitor expenditure data included weights to correct for response bias. Impacts were estimated with IMPLAN. Ninety percent interval endpoints were 6 to 16 percent above or below the...

[The analysis of threshold effect using Empower Stats software].

PubMed

Lin, Lin; Chen, Chang-zhong; Yu, Xiao-dan

2013-11-01

In many studies about biomedical research factors influence on the outcome variable, it has no influence or has a positive effect within a certain range. Exceeding a certain threshold value, the size of the effect and/or orientation will change, which called threshold effect. Whether there are threshold effects in the analysis of factors (x) on the outcome variable (y), it can be observed through a smooth curve fitting to see whether there is a piecewise linear relationship. And then using segmented regression model, LRT test and Bootstrap resampling method to analyze the threshold effect. Empower Stats software developed by American X & Y Solutions Inc has a threshold effect analysis module. You can input the threshold value at a given threshold segmentation simulated data. You may not input the threshold, but determined the optimal threshold analog data by the software automatically, and calculated the threshold confidence intervals.

Analysis of spreadable cheese by Raman spectroscopy and chemometric tools.

PubMed

Oliveira, Kamila de Sá; Callegaro, Layce de Souza; Stephani, Rodrigo; Almeida, Mariana Ramos; de Oliveira, Luiz Fernando Cappa

2016-03-01

In this work, FT-Raman spectroscopy was explored to evaluate spreadable cheese samples. A partial least squares discriminant analysis was employed to identify the spreadable cheese samples containing starch. To build the models, two types of samples were used: commercial samples and samples manufactured in local industries. The method of supervised classification PLS-DA was employed to classify the samples as adulterated or without starch. Multivariate regression was performed using the partial least squares method to quantify the starch in the spreadable cheese. The limit of detection obtained for the model was 0.34% (w/w) and the limit of quantification was 1.14% (w/w). The reliability of the models was evaluated by determining the confidence interval, which was calculated using the bootstrap re-sampling technique. The results show that the classification models can be used to complement classical analysis and as screening methods. Copyright © 2015 Elsevier Ltd. All rights reserved.

Spatial Point Pattern Analysis of Neurons Using Ripley's K-Function in 3D

PubMed Central

Jafari-Mamaghani, Mehrdad; Andersson, Mikael; Krieger, Patrik

2010-01-01

The aim of this paper is to apply a non-parametric statistical tool, Ripley's K-function, to analyze the 3-dimensional distribution of pyramidal neurons. Ripley's K-function is a widely used tool in spatial point pattern analysis. There are several approaches in 2D domains in which this function is executed and analyzed. Drawing consistent inferences on the underlying 3D point pattern distributions in various applications is of great importance as the acquisition of 3D biological data now poses lesser of a challenge due to technological progress. As of now, most of the applications of Ripley's K-function in 3D domains do not focus on the phenomenon of edge correction, which is discussed thoroughly in this paper. The main goal is to extend the theoretical and practical utilization of Ripley's K-function and corresponding tests based on bootstrap resampling from 2D to 3D domains. PMID:20577588

Performance analysis of deciduous morphology for detecting biological siblings.

PubMed

Paul, Kathleen S; Stojanowski, Christopher M

2015-08-01

Family-centered burial practices influence cemetery structure and can represent social group composition in both modern and ancient contexts. In ancient sites dental phenotypic data are often used as proxies for underlying genotypes to identify potential biological relatives. Here, we test the performance of deciduous dental morphological traits for differentiating sibling pairs from unrelated individuals from the same population. We collected 46 deciduous morphological traits for 69 sibling pairs from the Burlington Growth Centre's long term Family Study. Deciduous crown features were recorded following published standards. After variable winnowing, inter-individual Euclidean distances were generated using 20 morphological traits. To determine whether sibling pairs are more phenotypically similar than expected by chance we used bootstrap resampling of distances to generate P values. Multidimensional scaling (MDS) plots were used to evaluate the degree of clustering among sibling pairs. Results indicate an average distance between siblings of 0.252, which is significantly less than 9,999 replicated averages of 69 resampled pseudo-distances generated from: 1) a sample of non-relative pairs (P < 0.001), and 2) a sample of relative and non-relative pairs (P < 0.001). MDS plots indicate moderate to strong clustering among siblings; families occupied 3.83% of the multidimensional space on average (versus 63.10% for the total sample). Deciduous crown morphology performed well in identifying related sibling pairs. However, there was considerable variation in the extent to which different families exhibited similarly low levels of phenotypic divergence. © 2015 Wiley Periodicals, Inc.

New Methods for Estimating Seasonal Potential Climate Predictability

NASA Astrophysics Data System (ADS)

Feng, Xia

This study develops two new statistical approaches to assess the seasonal potential predictability of the observed climate variables. One is the univariate analysis of covariance (ANOCOVA) model, a combination of autoregressive (AR) model and analysis of variance (ANOVA). It has the advantage of taking into account the uncertainty of the estimated parameter due to sampling errors in statistical test, which is often neglected in AR based methods, and accounting for daily autocorrelation that is not considered in traditional ANOVA. In the ANOCOVA model, the seasonal signals arising from external forcing are determined to be identical or not to assess any interannual variability that may exist is potentially predictable. The bootstrap is an attractive alternative method that requires no hypothesis model and is available no matter how mathematically complicated the parameter estimator. This method builds up the empirical distribution of the interannual variance from the resamplings drawn with replacement from the given sample, in which the only predictability in seasonal means arises from the weather noise. These two methods are applied to temperature and water cycle components including precipitation and evaporation, to measure the extent to which the interannual variance of seasonal means exceeds the unpredictable weather noise compared with the previous methods, including Leith-Shukla-Gutzler (LSG), Madden, and Katz. The potential predictability of temperature from ANOCOVA model, bootstrap, LSG and Madden exhibits a pronounced tropical-extratropical contrast with much larger predictability in the tropics dominated by El Nino/Southern Oscillation (ENSO) than in higher latitudes where strong internal variability lowers predictability. Bootstrap tends to display highest predictability of the four methods, ANOCOVA lies in the middle, while LSG and Madden appear to generate lower predictability. Seasonal precipitation from ANOCOVA, bootstrap, and Katz, resembling that for temperature, is more predictable over the tropical regions, and less predictable in extropics. Bootstrap and ANOCOVA are in good agreement with each other, both methods generating larger predictability than Katz. The seasonal predictability of evaporation over land bears considerably similarity with that of temperature using ANOCOVA, bootstrap, LSG and Madden. The remote SST forcing and soil moisture reveal substantial seasonality in their relations with the potentially predictable seasonal signals. For selected regions, either SST or soil moisture or both shows significant relationships with predictable signals, hence providing indirect insight on slowly varying boundary processes involved to enable useful seasonal climate predication. A multivariate analysis of covariance (MANOCOVA) model is established to identify distinctive predictable patterns, which are uncorrelated with each other. Generally speaking, the seasonal predictability from multivariate model is consistent with that from ANOCOVA. Besides unveiling the spatial variability of predictability, MANOCOVA model also reveals the temporal variability of each predictable pattern, which could be linked to the periodic oscillations.

Brain size growth in wild and captive chimpanzees (Pan troglodytes).

PubMed

Cofran, Zachary

2018-05-24

Despite many studies of chimpanzee brain size growth, intraspecific variation is under-explored. Brain size data from chimpanzees of the Taï Forest and the Yerkes Primate Research Center enable a unique glimpse into brain growth variation as age at death is known for individuals, allowing cross-sectional growth curves to be estimated. Because Taï chimpanzees are from the wild but Yerkes apes are captive, potential environmental effects on neural development can also be explored. Previous research has revealed differences in growth and health between wild and captive primates, but such habitat effects have yet to be investigated for brain growth. Here, I use an iterative curve fitting procedure to estimate brain growth and regression parameters for each population, statistically comparing growth models using bootstrapped confidence intervals. Yerkes and Taï brain sizes overlap at all ages, although the sole Taï newborn is at the low end of captive neonatal variation. Growth rate and duration are statistically indistinguishable between the two populations. Resampling the Yerkes sample to match the Taï sample size and age group composition shows that ontogenetic variation in the two groups are remarkably similar despite the latter's limited size. Best fit growth curves for each sample indicate cessation of brain size growth at around 2 years, earlier than has previously been reported. The overall similarity between wild and captive chimpanzees points to the canalization of brain growth in this species. © 2018 Wiley Periodicals, Inc.

Detecting temporal trends in species assemblages with bootstrapping procedures and hierarchical models

USGS Publications Warehouse

Gotelli, Nicholas J.; Dorazio, Robert M.; Ellison, Aaron M.; Grossman, Gary D.

2010-01-01

Quantifying patterns of temporal trends in species assemblages is an important analytical challenge in community ecology. We describe methods of analysis that can be applied to a matrix of counts of individuals that is organized by species (rows) and time-ordered sampling periods (columns). We first developed a bootstrapping procedure to test the null hypothesis of random sampling from a stationary species abundance distribution with temporally varying sampling probabilities. This procedure can be modified to account for undetected species. We next developed a hierarchical model to estimate species-specific trends in abundance while accounting for species-specific probabilities of detection. We analysed two long-term datasets on stream fishes and grassland insects to demonstrate these methods. For both assemblages, the bootstrap test indicated that temporal trends in abundance were more heterogeneous than expected under the null model. We used the hierarchical model to estimate trends in abundance and identified sets of species in each assemblage that were steadily increasing, decreasing or remaining constant in abundance over more than a decade of standardized annual surveys. Our methods of analysis are broadly applicable to other ecological datasets, and they represent an advance over most existing procedures, which do not incorporate effects of incomplete sampling and imperfect detection.

Bootstrap-based procedures for inference in nonparametric receiver-operating characteristic curve regression analysis.

PubMed

Rodríguez-Álvarez, María Xosé; Roca-Pardiñas, Javier; Cadarso-Suárez, Carmen; Tahoces, Pablo G

2018-03-01

Prior to using a diagnostic test in a routine clinical setting, the rigorous evaluation of its diagnostic accuracy is essential. The receiver-operating characteristic curve is the measure of accuracy most widely used for continuous diagnostic tests. However, the possible impact of extra information about the patient (or even the environment) on diagnostic accuracy also needs to be assessed. In this paper, we focus on an estimator for the covariate-specific receiver-operating characteristic curve based on direct regression modelling and nonparametric smoothing techniques. This approach defines the class of generalised additive models for the receiver-operating characteristic curve. The main aim of the paper is to offer new inferential procedures for testing the effect of covariates on the conditional receiver-operating characteristic curve within the above-mentioned class. Specifically, two different bootstrap-based tests are suggested to check (a) the possible effect of continuous covariates on the receiver-operating characteristic curve and (b) the presence of factor-by-curve interaction terms. The validity of the proposed bootstrap-based procedures is supported by simulations. To facilitate the application of these new procedures in practice, an R-package, known as npROCRegression, is provided and briefly described. Finally, data derived from a computer-aided diagnostic system for the automatic detection of tumour masses in breast cancer is analysed.

An objective method to determine the probability distribution of the minimum apparent age of a sample of radio-isotopic dates

NASA Astrophysics Data System (ADS)

Ickert, R. B.; Mundil, R.

2012-12-01

Dateable minerals (especially zircon U-Pb) that crystallized at high temperatures but have been redeposited, pose both unique opportunities and challenges for geochronology. Although they have the potential to provide useful information on the depositional age of their host rocks, their relationship to the host is not always well constrained. For example, primary volcanic deposits will often have a lag time (time between eruption and deposition) that is smaller than can be resolved using radiometric techniques, and the age of eruption and of deposition will be coincident within uncertainty. Alternatively, ordinary clastic sedimentary rocks will usually have a long and variable lag time, even for the youngest minerals. Intermediate cases, for example moderately reworked volcanogenic material, will have a short, but unknown lag time. A compounding problem with U-Pb zircon is that the residence time of crystals in their host magma chamber (time between crystallization and eruption) can be high and is variable, even within the products of a single eruption. In cases where the lag and/or residence time suspected to be large relative to the precision of the date, a common objective is to determine the minimum age of a sample of dates, in order to constrain the maximum age of the deposition of the host rock. However, both the extraction of that age as well as assignment of a meaningful uncertainty is not straightforward. A number of ad hoc techniques have been employed in the literature, which may be appropriate for particular data sets or specific problems, but may yield biased or misleading results. Ludwig (2012) has developed an objective, statistically justified method for the determination of the distribution of the minimum age, but it has not been widely adopted. Here we extend this algorithm with a bootstrap (which can show the effect - if any - of the sampling distribution itself). This method has a number of desirable characteristics: It can incorporate all data points while being resistant to outliers, it utilizes the measurement uncertainties, and it does not require the assumption that any given cluster of data represents a single geological event. In brief, the technique generates a synthetic distribution from the input data by resampling with replacement (a bootstrap). Each resample is a random selection from a Gaussian distribution defined by the mean and uncertainty of the data point. For this distribution, the minimum value is calculated. This procedure is repeated many times (>1000) and a distribution of minimum values is generated, from which a confidence interval can be constructed. We demonstrate the application of this technique using natural and synthetic datasets, show the advantages and limitations, and relate it to other methods. We emphasize that this estimate remains strictly a minimum age - as with any other estimate that does not explicitly incorporate lag or residence time, it will not reflect a depositional age if the lag/residence time is larger than the uncertainty of the estimate. We recommend that this or similar techniques be considered by geochronologists. Ludwig, K.R., 2012. Isoplot 3.75, A geochronological toolkit for Microsoft Excel; Berkeley Geochronology Center Special Publication no. 5

Understanding catastrophizing from a misdirected problem-solving perspective.

PubMed

Flink, Ida K; Boersma, Katja; MacDonald, Shane; Linton, Steven J

2012-05-01

The aim is to explore pain catastrophizing from a problem-solving perspective. The links between catastrophizing, problem framing, and problem-solving behaviour are examined through two possible models of mediation as inferred by two contemporary and complementary theoretical models, the misdirected problem solving model (Eccleston & Crombez, 2007) and the fear-anxiety-avoidance model (Asmundson, Norton, & Vlaeyen, 2004). In this prospective study, a general population sample (n= 173) with perceived problems with spinal pain filled out questionnaires twice; catastrophizing and problem framing were assessed on the first occasion and health care seeking (as a proxy for medically oriented problem solving) was assessed 7 months later. Two different approaches were used to explore whether the data supported any of the proposed models of mediation. First, multiple regressions were used according to traditional recommendations for mediation analyses. Second, a bootstrapping method (n= 1000 bootstrap resamples) was used to explore the significance of the indirect effects in both possible models of mediation. The results verified the concepts included in the misdirected problem solving model. However, the direction of the relations was more in line with the fear-anxiety-avoidance model. More specifically, the mediation analyses provided support for viewing catastrophizing as a mediator of the relation between biomedical problem framing and medically oriented problem-solving behaviour. These findings provide support for viewing catastrophizing from a problem-solving perspective and imply a need to examine and address problem framing and catastrophizing in back pain patients. ©2011 The British Psychological Society.

Signal detection theory and vestibular perception: III. Estimating unbiased fit parameters for psychometric functions.

PubMed

Chaudhuri, Shomesh E; Merfeld, Daniel M

2013-03-01

Psychophysics generally relies on estimating a subject's ability to perform a specific task as a function of an observed stimulus. For threshold studies, the fitted functions are called psychometric functions. While fitting psychometric functions to data acquired using adaptive sampling procedures (e.g., "staircase" procedures), investigators have encountered a bias in the spread ("slope" or "threshold") parameter that has been attributed to the serial dependency of the adaptive data. Using simulations, we confirm this bias for cumulative Gaussian parametric maximum likelihood fits on data collected via adaptive sampling procedures, and then present a bias-reduced maximum likelihood fit that substantially reduces the bias without reducing the precision of the spread parameter estimate and without reducing the accuracy or precision of the other fit parameters. As a separate topic, we explain how to implement this bias reduction technique using generalized linear model fits as well as other numeric maximum likelihood techniques such as the Nelder-Mead simplex. We then provide a comparison of the iterative bootstrap and observed information matrix techniques for estimating parameter fit variance from adaptive sampling procedure data sets. The iterative bootstrap technique is shown to be slightly more accurate; however, the observed information technique executes in a small fraction (0.005 %) of the time required by the iterative bootstrap technique, which is an advantage when a real-time estimate of parameter fit variance is required.

Are patients open to elective re-sampling of their glioblastoma? A new way of assessing treatment innovations.

PubMed

Mir, Taskia; Dirks, Peter; Mason, Warren P; Bernstein, Mark

2014-10-01

This is a qualitative study designed to examine patient acceptability of re-sampling surgery for glioblastoma multiforme (GBM) electively post-therapy or at asymptomatic relapse. Thirty patients were selected using the convenience sampling method and interviewed. Patients were presented with hypothetical scenarios including a scenario in which the surgery was offered to them routinely and a scenario in which the surgery was in a clinical trial. The results of the study suggest that about two thirds of the patients offered the surgery on a routine basis would be interested, and half of the patients would agree to the surgery as part of a clinical trial. Several overarching themes emerged, some of which include: patients expressed ethical concerns about offering financial incentives or compensation to the patients or surgeons involved in the study; patients were concerned about appropriate communication and full disclosure about the procedures involved, the legalities of tumor ownership and the use of the tumor post-surgery; patients may feel alone or vulnerable when they are approached about the surgery; patients and their families expressed immense trust in their surgeon and indicated that this trust is a major determinant of their agreeing to surgery. The overall positive response to re-sampling surgery suggests that this procedure, if designed with all the ethical concerns attended to, would be welcomed by most patients. This approach of asking patients beforehand if a treatment innovation is acceptable would appear to be more practical and ethically desirable than previous practice.

Building Intuitions about Statistical Inference Based on Resampling

ERIC Educational Resources Information Center

Watson, Jane; Chance, Beth

2012-01-01

Formal inference, which makes theoretical assumptions about distributions and applies hypothesis testing procedures with null and alternative hypotheses, is notoriously difficult for tertiary students to master. The debate about whether this content should appear in Years 11 and 12 of the "Australian Curriculum: Mathematics" has gone on…

Permutation tests for goodness-of-fit testing of mathematical models to experimental data.

PubMed

Fişek, M Hamit; Barlas, Zeynep

2013-03-01

This paper presents statistical procedures for improving the goodness-of-fit testing of theoretical models to data obtained from laboratory experiments. We use an experimental study in the expectation states research tradition which has been carried out in the "standardized experimental situation" associated with the program to illustrate the application of our procedures. We briefly review the expectation states research program and the fundamentals of resampling statistics as we develop our procedures in the resampling context. The first procedure we develop is a modification of the chi-square test which has been the primary statistical tool for assessing goodness of fit in the EST research program, but has problems associated with its use. We discuss these problems and suggest a procedure to overcome them. The second procedure we present, the "Average Absolute Deviation" test, is a new test and is proposed as an alternative to the chi square test, as being simpler and more informative. The third and fourth procedures are permutation versions of Jonckheere's test for ordered alternatives, and Kendall's tau(b), a rank order correlation coefficient. The fifth procedure is a new rank order goodness-of-fit test, which we call the "Deviation from Ideal Ranking" index, which we believe may be more useful than other rank order tests for assessing goodness-of-fit of models to experimental data. The application of these procedures to the sample data is illustrated in detail. We then present another laboratory study from an experimental paradigm different from the expectation states paradigm - the "network exchange" paradigm, and describe how our procedures may be applied to this data set. Copyright © 2012 Elsevier Inc. All rights reserved.

The balanced survivor average causal effect.

PubMed

Greene, Tom; Joffe, Marshall; Hu, Bo; Li, Liang; Boucher, Ken

2013-05-07

Statistical analysis of longitudinal outcomes is often complicated by the absence of observable values in patients who die prior to their scheduled measurement. In such cases, the longitudinal data are said to be "truncated by death" to emphasize that the longitudinal measurements are not simply missing, but are undefined after death. Recently, the truncation by death problem has been investigated using the framework of principal stratification to define the target estimand as the survivor average causal effect (SACE), which in the context of a two-group randomized clinical trial is the mean difference in the longitudinal outcome between the treatment and control groups for the principal stratum of always-survivors. The SACE is not identified without untestable assumptions. These assumptions have often been formulated in terms of a monotonicity constraint requiring that the treatment does not reduce survival in any patient, in conjunction with assumed values for mean differences in the longitudinal outcome between certain principal strata. In this paper, we introduce an alternative estimand, the balanced-SACE, which is defined as the average causal effect on the longitudinal outcome in a particular subset of the always-survivors that is balanced with respect to the potential survival times under the treatment and control. We propose a simple estimator of the balanced-SACE that compares the longitudinal outcomes between equivalent fractions of the longest surviving patients between the treatment and control groups and does not require a monotonicity assumption. We provide expressions for the large sample bias of the estimator, along with sensitivity analyses and strategies to minimize this bias. We consider statistical inference under a bootstrap resampling procedure.

Classification of Amazonian rosewood essential oil by Raman spectroscopy and PLS-DA with reliability estimation.

PubMed

Almeida, Mariana R; Fidelis, Carlos H V; Barata, Lauro E S; Poppi, Ronei J

2013-12-15

The Amazon tree Aniba rosaeodora Ducke (rosewood) provides an essential oil valuable for the perfume industry, but after decades of predatory extraction it is at risk of extinction. The extraction of the essential oil from wood implies the cutting of the tree, and then the study of oil extracted from the leaves is important as a sustainable alternative. The goal of this study was to test the applicability of Raman spectroscopy and Partial Least Square Discriminant Analysis (PLS-DA) as means to classify the essential oil extracted from different parties (wood, leaves and branches) of the Brazilian tree A. rosaeodora. For the development of classification models, the Raman spectra were split into two sets: training and test. The value of the limit that separates the classes was calculated based on the distribution of samples of training. This value was calculated in a manner that the classes are divided with a lower probability of incorrect classification for future estimates. The best model presented sensitivity and specificity of 100%, predictive accuracy and efficiency of 100%. These results give an overall vision of the behavior of the model, but do not give information about individual samples; in this case, the confidence interval for each sample of classification was also calculated using the resampling bootstrap technique. The methodology developed have the potential to be an alternative for standard procedures used for oil analysis and it can be employed as screening method, since it is fast, non-destructive and robust. © 2013 Elsevier B.V. All rights reserved.

The impact of sample size on the reproducibility of voxel-based lesion-deficit mappings.

PubMed

Lorca-Puls, Diego L; Gajardo-Vidal, Andrea; White, Jitrachote; Seghier, Mohamed L; Leff, Alexander P; Green, David W; Crinion, Jenny T; Ludersdorfer, Philipp; Hope, Thomas M H; Bowman, Howard; Price, Cathy J

2018-07-01

This study investigated how sample size affects the reproducibility of findings from univariate voxel-based lesion-deficit analyses (e.g., voxel-based lesion-symptom mapping and voxel-based morphometry). Our effect of interest was the strength of the mapping between brain damage and speech articulation difficulties, as measured in terms of the proportion of variance explained. First, we identified a region of interest by searching on a voxel-by-voxel basis for brain areas where greater lesion load was associated with poorer speech articulation using a large sample of 360 right-handed English-speaking stroke survivors. We then randomly drew thousands of bootstrap samples from this data set that included either 30, 60, 90, 120, 180, or 360 patients. For each resample, we recorded effect size estimates and p values after conducting exactly the same lesion-deficit analysis within the previously identified region of interest and holding all procedures constant. The results show (1) how often small effect sizes in a heterogeneous population fail to be detected; (2) how effect size and its statistical significance varies with sample size; (3) how low-powered studies (due to small sample sizes) can greatly over-estimate as well as under-estimate effect sizes; and (4) how large sample sizes (N ≥ 90) can yield highly significant p values even when effect sizes are so small that they become trivial in practical terms. The implications of these findings for interpreting the results from univariate voxel-based lesion-deficit analyses are discussed. Copyright © 2018 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Practice and Learning: Spatiotemporal Differences in Thalamo-Cortical-Cerebellar Networks Engagement across Learning Phases in Schizophrenia.

PubMed

Korostil, Michele; Remington, Gary; McIntosh, Anthony Randal

2016-01-01

Understanding how practice mediates the transition of brain-behavior networks between early and later stages of learning is constrained by the common approach to analysis of fMRI data. Prior imaging studies have mostly relied on a single scan, and parametric, task-related analyses. Our experiment incorporates a multisession fMRI lexicon-learning experiment with multivariate, whole-brain analysis to further knowledge of the distributed networks supporting practice-related learning in schizophrenia (SZ). Participants with SZ were compared with healthy control (HC) participants as they learned a novel lexicon during two fMRI scans over a several day period. All participants were trained to equal task proficiency prior to scanning. Behavioral-Partial Least Squares, a multivariate analytic approach, was used to analyze the imaging data. Permutation testing was used to determine statistical significance and bootstrap resampling to determine the reliability of the findings. With practice, HC participants transitioned to a brain-accuracy network incorporating dorsostriatal regions in late-learning stages. The SZ participants did not transition to this pattern despite comparable behavioral results. Instead, successful learners with SZ were differentiated primarily on the basis of greater engagement of perceptual and perceptual-integration brain regions. There is a different spatiotemporal unfolding of brain-learning relationships in SZ. In SZ, given the same amount of practice, the movement from networks suggestive of effortful learning toward subcortically driven procedural one differs from HC participants. Learning performance in SZ is driven by varying levels of engagement in perceptual regions, which suggests perception itself is impaired and may impact downstream, "higher level" cognition.

Procrustean rotation in concert with principal component analysis of molecular dynamics trajectories: Quantifying global and local differences between conformational samples.

PubMed

Oblinsky, Daniel G; Vanschouwen, Bryan M B; Gordon, Heather L; Rothstein, Stuart M

2009-12-14

Given the principal component analysis (PCA) of a molecular dynamics (MD) conformational trajectory for a model protein, we perform orthogonal Procrustean rotation to "best fit" the PCA squared-loading matrix to that of a target matrix computed for a related but different molecular system. The sum of squared deviations of the elements of the rotated matrix from those of the target, known as the error of fit (EOF), provides a quantitative measure of the dissimilarity between the two conformational samples. To estimate precision of the EOF, we perform bootstrap resampling of the molecular conformations within the trajectories, generating a distribution of EOF values for the system and target. The average EOF per variable is determined and visualized to ascertain where, locally, system and target sample properties differ. We illustrate this approach by analyzing MD trajectories for the wild-type and four selected mutants of the beta1 domain of protein G.

Procrustean rotation in concert with principal component analysis of molecular dynamics trajectories: Quantifying global and local differences between conformational samples

NASA Astrophysics Data System (ADS)

Oblinsky, Daniel G.; VanSchouwen, Bryan M. B.; Gordon, Heather L.; Rothstein, Stuart M.

2009-12-01

Given the principal component analysis (PCA) of a molecular dynamics (MD) conformational trajectory for a model protein, we perform orthogonal Procrustean rotation to "best fit" the PCA squared-loading matrix to that of a target matrix computed for a related but different molecular system. The sum of squared deviations of the elements of the rotated matrix from those of the target, known as the error of fit (EOF), provides a quantitative measure of the dissimilarity between the two conformational samples. To estimate precision of the EOF, we perform bootstrap resampling of the molecular conformations within the trajectories, generating a distribution of EOF values for the system and target. The average EOF per variable is determined and visualized to ascertain where, locally, system and target sample properties differ. We illustrate this approach by analyzing MD trajectories for the wild-type and four selected mutants of the β1 domain of protein G.

Modifications to the Patient Rule-Induction Method that utilize non-additive combinations of genetic and environmental effects to define partitions that predict ischemic heart disease.

PubMed

Dyson, Greg; Frikke-Schmidt, Ruth; Nordestgaard, Børge G; Tybjaerg-Hansen, Anne; Sing, Charles F

2009-05-01

This article extends the Patient Rule-Induction Method (PRIM) for modeling cumulative incidence of disease developed by Dyson et al. (Genet Epidemiol 31:515-527) to include the simultaneous consideration of non-additive combinations of predictor variables, a significance test of each combination, an adjustment for multiple testing and a confidence interval for the estimate of the cumulative incidence of disease in each partition. We employ the partitioning algorithm component of the Combinatorial Partitioning Method to construct combinations of predictors, permutation testing to assess the significance of each combination, theoretical arguments for incorporating a multiple testing adjustment and bootstrap resampling to produce the confidence intervals. An illustration of this revised PRIM utilizing a sample of 2,258 European male participants from the Copenhagen City Heart Study is presented that assesses the utility of genetic variants in predicting the presence of ischemic heart disease beyond the established risk factors.

Modifications to the Patient Rule-Induction Method that utilize non-additive combinations of genetic and environmental effects to define partitions that predict ischemic heart disease

PubMed Central

Dyson, Greg; Frikke-Schmidt, Ruth; Nordestgaard, Børge G.; Tybjærg-Hansen, Anne; Sing, Charles F.

2009-01-01

This paper extends the Patient Rule-Induction Method (PRIM) for modeling cumulative incidence of disease developed by Dyson et al. (2007) to include the simultaneous consideration of non-additive combinations of predictor variables, a significance test of each combination, an adjustment for multiple testing and a confidence interval for the estimate of the cumulative incidence of disease in each partition. We employ the partitioning algorithm component of the Combinatorial Partitioning Method (CPM) to construct combinations of predictors, permutation testing to assess the significance of each combination, theoretical arguments for incorporating a multiple testing adjustment and bootstrap resampling to produce the confidence intervals. An illustration of this revised PRIM utilizing a sample of 2258 European male participants from the Copenhagen City Heart Study is presented that assesses the utility of genetic variants in predicting the presence of ischemic heart disease beyond the established risk factors. PMID:19025787

PTSD symptoms and pain in Canadian military veterans: the mediating roles of anxiety, depression, and alcohol use.

PubMed

Irwin, Kara C; Konnert, Candace; Wong, May; O'Neill, Thomas A

2014-04-01

Symptoms of posttraumatic stress disorder (PTSD) and pain are often comorbid among veterans. The purpose of this study was to investigate to what extent symptoms of anxiety, depression, and alcohol use mediated the relationship between PTSD symptoms and pain among 113 treated male Canadian veterans. Measures of PTSD, pain, anxiety symptoms, depression symptoms, and alcohol use were collected as part of the initial assessment. The bootstrapped resampling analyses were consistent with the hypothesis of mediation for anxiety and depression, but not alcohol use. The confidence intervals did not include zero and the indirect effect of PTSD on pain through anxiety was .04, CI [.03, .07]. The indirect effect of PTSD on pain through depression was .04, CI [.02, .07]. These findings suggest that PTSD and pain symptoms among veterans may be related through the underlying symptoms of anxiety and depression, thus emphasizing the importance of targeting anxiety and depression symptoms when treating comorbid PTSD and pain patients. © 2014 International Society for Traumatic Stress Studies.

Changes in seasonal streamflow extremes experienced in rivers of Northwestern South America (Colombia)

NASA Astrophysics Data System (ADS)

Pierini, J. O.; Restrepo, J. C.; Aguirre, J.; Bustamante, A. M.; Velásquez, G. J.

2017-04-01

A measure of the variability in seasonal extreme streamflow was estimated for the Colombian Caribbean coast, using monthly time series of freshwater discharge from ten watersheds. The aim was to detect modifications in the streamflow monthly distribution, seasonal trends, variance and extreme monthly values. A 20-year length time moving window, with 1-year successive shiftments, was applied to the monthly series to analyze the seasonal variability of streamflow. The seasonal-windowed data were statistically fitted through the Gamma distribution function. Scale and shape parameters were computed using the Maximum Likelihood Estimation (MLE) and the bootstrap method for 1000 resample. A trend analysis was performed for each windowed-serie, allowing to detect the window of maximum absolute values for trends. Significant temporal shifts in seasonal streamflow distribution and quantiles (QT), were obtained for different frequencies. Wet and dry extremes periods increased significantly in the last decades. Such increase did not occur simultaneously through the region. Some locations exhibited continuous increases only at minimum QT.

Anxiety sensitivity and racial differences in sleep duration: Results from a national survey of adults with cardiovascular disease.

PubMed

Alcántara, Carmela; Giorgio Cosenzo, Luciana Andrea; Fan, Weijia; Doyle, David Matthew; Shaffer, Jonathan A

2017-05-01

Although Blacks sleep between 37 and 75min less per night than non-Hispanic Whites, research into what drives racial differences in sleep duration is limited. We examined the association of anxiety sensitivity, a cognitive vulnerability, and race (Blacks vs. White) with short sleep duration (<7h of sleep/night), and whether anxiety sensitivity mediated race differences in sleep duration in a nationally representative sample of adults with cardiovascular disease. Overall, 1289 adults (115 Black, 1174 White) with a self-reported physician/health professional diagnosis of ≥1 myocardial infarction completed an online survey. Weighted multivariable logistic regressions and mediation analyses with bootstrapping and case resampling were conducted. Anxiety sensitivity and Black vs. White race were associated with 4%-84% increased odds, respectively, of short sleep duration. Anxiety sensitivity mediated Black-White differences in sleep duration. Each anxiety sensitivity subscale was also a significant mediator. Implications for future intervention science to address sleep disparities are discussed. Copyright © 2016 Elsevier Ltd. All rights reserved.

Trends in extremes of temperature, dew point, and precipitation from long instrumental series from central Europe

NASA Astrophysics Data System (ADS)

Kürbis, K.; Mudelsee, M.; Tetzlaff, G.; Brázdil, R.

2009-09-01

For the analysis of trends in weather extremes, we introduce a diagnostic index variable, the exceedance product, which combines intensity and frequency of extremes. We separate trends in higher moments from trends in mean or standard deviation and use bootstrap resampling to evaluate statistical significances. The application of the concept of the exceedance product to daily meteorological time series from Potsdam (1893 to 2005) and Prague-Klementinum (1775 to 2004) reveals that extremely cold winters occurred only until the mid-20th century, whereas warm winters show upward trends. These changes were significant in higher moments of the temperature distribution. In contrast, trends in summer temperature extremes (e.g., the 2003 European heatwave) can be explained by linear changes in mean or standard deviation. While precipitation at Potsdam does not show pronounced trends, dew point does exhibit a change from maximum extremes during the 1960s to minimum extremes during the 1970s.

The role of interpersonal sensitivity, social support, and quality of life in rural older adults.

PubMed

Wedgeworth, Monika; LaRocca, Michael A; Chaplin, William F; Scogin, Forrest

The mental health of elderly individuals in rural areas is increasingly relevant as populations age and social structures change. While social support satisfaction is a well-established predictor of quality of life, interpersonal sensitivity symptoms may diminish this relation. The current study extends the findings of Scogin et al by investigating the relationship among interpersonal sensitivity, social support satisfaction, and quality of life among rural older adults and exploring the mediating role of social support in the relation between interpersonal sensitivity and quality of life (N = 128). Hierarchical regression revealed that interpersonal sensitivity and social support satisfaction predicted quality of life. In addition, bootstrapping resampling supported the role of social support satisfaction as a mediator between interpersonal sensitivity symptoms and quality of life. These results underscore the importance of nurses and allied health providers in assessing and attending to negative self-perceptions of clients, as well as the perceived quality of their social networks. Copyright © 2016 Elsevier Inc. All rights reserved.

A cluster bootstrap for two-loop MHV amplitudes

DOE PAGES

Golden, John; Spradlin, Marcus

2015-02-02

We apply a bootstrap procedure to two-loop MHV amplitudes in planar N=4 super-Yang-Mills theory. We argue that the mathematically most complicated part (the Λ 2 B 2 coproduct component) of the n-particle amplitude is uniquely determined by a simple cluster algebra property together with a few physical constraints (dihedral symmetry, analytic structure, supersymmetry, and well-defined collinear limits). Finally, we present a concise, closed-form expression which manifests these properties for all n.

Statistical approaches for the definition of landslide rainfall thresholds and their uncertainty using rain gauge and satellite data

NASA Astrophysics Data System (ADS)

Rossi, M.; Luciani, S.; Valigi, D.; Kirschbaum, D.; Brunetti, M. T.; Peruccacci, S.; Guzzetti, F.

2017-05-01

Models for forecasting rainfall-induced landslides are mostly based on the identification of empirical rainfall thresholds obtained exploiting rain gauge data. Despite their increased availability, satellite rainfall estimates are scarcely used for this purpose. Satellite data should be useful in ungauged and remote areas, or should provide a significant spatial and temporal reference in gauged areas. In this paper, the analysis of the reliability of rainfall thresholds based on rainfall remote sensed and rain gauge data for the prediction of landslide occurrence is carried out. To date, the estimation of the uncertainty associated with the empirical rainfall thresholds is mostly based on a bootstrap resampling of the rainfall duration and the cumulated event rainfall pairs (D,E) characterizing rainfall events responsible for past failures. This estimation does not consider the measurement uncertainty associated with D and E. In the paper, we propose (i) a new automated procedure to reconstruct ED conditions responsible for the landslide triggering and their uncertainties, and (ii) three new methods to identify rainfall threshold for the possible landslide occurrence, exploiting rain gauge and satellite data. In particular, the proposed methods are based on Least Square (LS), Quantile Regression (QR) and Nonlinear Least Square (NLS) statistical approaches. We applied the new procedure and methods to define empirical rainfall thresholds and their associated uncertainties in the Umbria region (central Italy) using both rain-gauge measurements and satellite estimates. We finally validated the thresholds and tested the effectiveness of the different threshold definition methods with independent landslide information. The NLS method among the others performed better in calculating thresholds in the full range of rainfall durations. We found that the thresholds obtained from satellite data are lower than those obtained from rain gauge measurements. This is in agreement with the literature, where satellite rainfall data underestimate the "ground" rainfall registered by rain gauges.

Statistical Approaches for the Definition of Landslide Rainfall Thresholds and their Uncertainty Using Rain Gauge and Satellite Data

NASA Technical Reports Server (NTRS)

Rossi, M.; Luciani, S.; Valigi, D.; Kirschbaum, D.; Brunetti, M. T.; Peruccacci, S.; Guzzetti, F.

2017-01-01

Models for forecasting rainfall-induced landslides are mostly based on the identification of empirical rainfall thresholds obtained exploiting rain gauge data. Despite their increased availability, satellite rainfall estimates are scarcely used for this purpose. Satellite data should be useful in ungauged and remote areas, or should provide a significant spatial and temporal reference in gauged areas. In this paper, the analysis of the reliability of rainfall thresholds based on rainfall remote sensed and rain gauge data for the prediction of landslide occurrence is carried out. To date, the estimation of the uncertainty associated with the empirical rainfall thresholds is mostly based on a bootstrap resampling of the rainfall duration and the cumulated event rainfall pairs (D,E) characterizing rainfall events responsible for past failures. This estimation does not consider the measurement uncertainty associated with D and E. In the paper, we propose (i) a new automated procedure to reconstruct ED conditions responsible for the landslide triggering and their uncertainties, and (ii) three new methods to identify rainfall threshold for the possible landslide occurrence, exploiting rain gauge and satellite data. In particular, the proposed methods are based on Least Square (LS), Quantile Regression (QR) and Nonlinear Least Square (NLS) statistical approaches. We applied the new procedure and methods to define empirical rainfall thresholds and their associated uncertainties in the Umbria region (central Italy) using both rain-gauge measurements and satellite estimates. We finally validated the thresholds and tested the effectiveness of the different threshold definition methods with independent landslide information. The NLS method among the others performed better in calculating thresholds in the full range of rainfall durations. We found that the thresholds obtained from satellite data are lower than those obtained from rain gauge measurements. This is in agreement with the literature, where satellite rainfall data underestimate the 'ground' rainfall registered by rain gauges.

Maximum a posteriori resampling of noisy, spatially correlated data

NASA Astrophysics Data System (ADS)

Goff, John A.; Jenkins, Chris; Calder, Brian

2006-08-01

In any geologic application, noisy data are sources of consternation for researchers, inhibiting interpretability and marring images with unsightly and unrealistic artifacts. Filtering is the typical solution to dealing with noisy data. However, filtering commonly suffers from ad hoc (i.e., uncalibrated, ungoverned) application. We present here an alternative to filtering: a newly developed method for correcting noise in data by finding the "best" value given available information. The motivating rationale is that data points that are close to each other in space cannot differ by "too much," where "too much" is governed by the field covariance. Data with large uncertainties will frequently violate this condition and therefore ought to be corrected, or "resampled." Our solution for resampling is determined by the maximum of the a posteriori density function defined by the intersection of (1) the data error probability density function (pdf) and (2) the conditional pdf, determined by the geostatistical kriging algorithm applied to proximal data values. A maximum a posteriori solution can be computed sequentially going through all the data, but the solution depends on the order in which the data are examined. We approximate the global a posteriori solution by randomizing this order and taking the average. A test with a synthetic data set sampled from a known field demonstrates quantitatively and qualitatively the improvement provided by the maximum a posteriori resampling algorithm. The method is also applied to three marine geology/geophysics data examples, demonstrating the viability of the method for diverse applications: (1) three generations of bathymetric data on the New Jersey shelf with disparate data uncertainties; (2) mean grain size data from the Adriatic Sea, which is a combination of both analytic (low uncertainty) and word-based (higher uncertainty) sources; and (3) side-scan backscatter data from the Martha's Vineyard Coastal Observatory which are, as is typical for such data, affected by speckle noise. Compared to filtering, maximum a posteriori resampling provides an objective and optimal method for reducing noise, and better preservation of the statistical properties of the sampled field. The primary disadvantage is that maximum a posteriori resampling is a computationally expensive procedure.

Measurement of cardiac troponin I in healthy lactating dairy cows using a point of care analyzer (i-STAT-1).

PubMed

Labonté, Josiane; Roy, Jean-Philippe; Dubuc, Jocelyn; Buczinski, Sébastien

2015-06-01

Cardiac troponin I (cTnI) has been shown to be an accurate predictor of myocardial injury in cattle. The point-of-care i-STAT 1 immunoassay can be used to quantify blood cTnI in cattle. However, the cTnI reference interval in whole blood of healthy early lactating dairy cows remains unknown. To determine a blood cTnI reference interval in healthy early lactating Holstein dairy cows using the analyzer i-STAT 1. Forty healthy lactating dairy Holstein cows (0-60 days in milk) were conveniently selected from four commercial dairy farms. Each selected cow was examined by a veterinarian and transthoracic echocardiography was performed. A cow-side blood cTnI dosage was measured at the same time. A bootstrap statistical analysis method using unrestricted resampling was used to determine a reference interval for blood cTnI values. Forty healthy cows were recruited in the study. Median blood cTnI was 0.02 ng/mL (minimum: 0.00, maximum: 0.05). Based on the bootstrap analysis method with 40 cases, the 95th percentile of cTnI values in healthy cows was 0.036 ng/mL (90% CI: 0.02-0.05 ng/mL). A reference interval for blood cTnI values in healthy lactating cows was determined. Further research is needed to determine whether cTnI blood values could be used to diagnose and provide a prognosis for cardiac and noncardiac diseases in lactating dairy cows. Copyright © 2015 Elsevier B.V. All rights reserved.

Bootstrap Signal-to-Noise Confidence Intervals: An Objective Method for Subject Exclusion and Quality Control in ERP Studies

PubMed Central

Parks, Nathan A.; Gannon, Matthew A.; Long, Stephanie M.; Young, Madeleine E.

2016-01-01

Analysis of event-related potential (ERP) data includes several steps to ensure that ERPs meet an appropriate level of signal quality. One such step, subject exclusion, rejects subject data if ERP waveforms fail to meet an appropriate level of signal quality. Subject exclusion is an important quality control step in the ERP analysis pipeline as it ensures that statistical inference is based only upon those subjects exhibiting clear evoked brain responses. This critical quality control step is most often performed simply through visual inspection of subject-level ERPs by investigators. Such an approach is qualitative, subjective, and susceptible to investigator bias, as there are no standards as to what constitutes an ERP of sufficient signal quality. Here, we describe a standardized and objective method for quantifying waveform quality in individual subjects and establishing criteria for subject exclusion. The approach uses bootstrap resampling of ERP waveforms (from a pool of all available trials) to compute a signal-to-noise ratio confidence interval (SNR-CI) for individual subject waveforms. The lower bound of this SNR-CI (SNRLB) yields an effective and objective measure of signal quality as it ensures that ERP waveforms statistically exceed a desired signal-to-noise criterion. SNRLB provides a quantifiable metric of individual subject ERP quality and eliminates the need for subjective evaluation of waveform quality by the investigator. We detail the SNR-CI methodology, establish the efficacy of employing this approach with Monte Carlo simulations, and demonstrate its utility in practice when applied to ERP datasets. PMID:26903849

Efficacy of Guided iCBT for Depression and Mediation of Change by Cognitive Skill Acquisition.

PubMed

Forand, Nicholas R; Barnett, Jeffrey G; Strunk, Daniel R; Hindiyeh, Mohammed U; Feinberg, Jason E; Keefe, John R

2018-03-01

Guided internet CBT (iCBT) is a promising treatment for depression; however, it is less well known through what mechanisms iCBT works. Two possible mediators of change are the acquisition of cognitive skills and increases in behavioral activation. We report results of an 8-week waitlist controlled trial of guided iCBT, and test whether early change in cognitive skills or behavioral activation mediated subsequent change in depression. The sample was 89 individuals randomized to guided iCBT (n = 59) or waitlist (n = 30). Participants were 75% female, 72% Caucasian, and 33 years old on average. The PHQ9 was the primary outcome measure. Mediators were the Competencies of Cognitive Therapy Scale-Self Report and the Behavioral Activation Scale for Depression-Short Form. Treatment was Beating the Blues plus manualized coaching. Outcomes were analyzed using linear mixed models, and mediation with a bootstrap resampling approach. The iCBT group was superior to waitlist, with large effect sizes at posttreatment (Hedges' g = 1.45). Dropout of iCBT was 29% versus 10% for waitlist. In the mediation analyses, the acquisition of cognitive skills mediated subsequent depression change (indirect effect = -.61, 95% bootstrapped biased corrected CI: -1.47, -0.09), but increases in behavioral activation did not. iCBT is an effective treatment for depression, but dropout rates remain high. Change in iCBT appears to be mediated by improvements in the use of cognitive skills, such as critically evaluating and restructuring negative thoughts. Copyright © 2017. Published by Elsevier Ltd.

Prediction of two month modified Rankin Scale with an ordinal prediction model in patients with aneurysmal subarachnoid haemorrhage

PubMed Central

2010-01-01

Background Aneurysmal subarachnoid haemorrhage (aSAH) is a devastating event with a frequently disabling outcome. Our aim was to develop a prognostic model to predict an ordinal clinical outcome at two months in patients with aSAH. Methods We studied patients enrolled in the International Subarachnoid Aneurysm Trial (ISAT), a randomized multicentre trial to compare coiling and clipping in aSAH patients. Several models were explored to estimate a patient's outcome according to the modified Rankin Scale (mRS) at two months after aSAH. Our final model was validated internally with bootstrapping techniques. Results The study population comprised of 2,128 patients of whom 159 patients died within 2 months (8%). Multivariable proportional odds analysis identified World Federation of Neurosurgical Societies (WFNS) grade as the most important predictor, followed by age, sex, lumen size of the aneurysm, Fisher grade, vasospasm on angiography, and treatment modality. The model discriminated moderately between those with poor and good mRS scores (c statistic = 0.65), with minor optimism according to bootstrap re-sampling (optimism corrected c statistic = 0.64). Conclusion We presented a calibrated and internally validated ordinal prognostic model to predict two month mRS in aSAH patients who survived the early stage up till a treatment decision. Although generalizability of the model is limited due to the selected population in which it was developed, this model could eventually be used to support clinical decision making after external validation. Trial Registration International Standard Randomised Controlled Trial, Number ISRCTN49866681 PMID:20920243

Using the Bootstrap Method to Evaluate the Critical Range of Misfit for Polytomous Rasch Fit Statistics.

PubMed

Seol, Hyunsoo

2016-06-01

The purpose of this study was to apply the bootstrap procedure to evaluate how the bootstrapped confidence intervals (CIs) for polytomous Rasch fit statistics might differ according to sample sizes and test lengths in comparison with the rule-of-thumb critical value of misfit. A total of 25 simulated data sets were generated to fit the Rasch measurement and then a total of 1,000 replications were conducted to compute the bootstrapped CIs under each of 25 testing conditions. The results showed that rule-of-thumb critical values for assessing the magnitude of misfit were not applicable because the infit and outfit mean square error statistics showed different magnitudes of variability over testing conditions and the standardized fit statistics did not exactly follow the standard normal distribution. Further, they also do not share the same critical range for the item and person misfit. Based on the results of the study, the bootstrapped CIs can be used to identify misfitting items or persons as they offer a reasonable alternative solution, especially when the distributions of the infit and outfit statistics are not well known and depend on sample size. © The Author(s) 2016.

Resampling procedures to identify important SNPs using a consensus approach.

PubMed

Pardy, Christopher; Motyer, Allan; Wilson, Susan

2011-11-29

Our goal is to identify common single-nucleotide polymorphisms (SNPs) (minor allele frequency > 1%) that add predictive accuracy above that gained by knowledge of easily measured clinical variables. We take an algorithmic approach to predict each phenotypic variable using a combination of phenotypic and genotypic predictors. We perform our procedure on the first simulated replicate and then validate against the others. Our procedure performs well when predicting Q1 but is less successful for the other outcomes. We use resampling procedures where possible to guard against false positives and to improve generalizability. The approach is based on finding a consensus regarding important SNPs by applying random forests and the least absolute shrinkage and selection operator (LASSO) on multiple subsamples. Random forests are used first to discard unimportant predictors, narrowing our focus to roughly 100 important SNPs. A cross-validation LASSO is then used to further select variables. We combine these procedures to guarantee that cross-validation can be used to choose a shrinkage parameter for the LASSO. If the clinical variables were unavailable, this prefiltering step would be essential. We perform the SNP-based analyses simultaneously rather than one at a time to estimate SNP effects in the presence of other causal variants. We analyzed the first simulated replicate of Genetic Analysis Workshop 17 without knowledge of the true model. Post-conference knowledge of the simulation parameters allowed us to investigate the limitations of our approach. We found that many of the false positives we identified were substantially correlated with genuine causal SNPs.

Estimating parasitic sea lamprey abundance in Lake Huron from heterogenous data sources

USGS Publications Warehouse

Young, Robert J.; Jones, Michael L.; Bence, James R.; McDonald, Rodney B.; Mullett, Katherine M.; Bergstedt, Roger A.

2003-01-01

The Great Lakes Fishery Commission uses time series of transformer, parasitic, and spawning population estimates to evaluate the effectiveness of its sea lamprey (Petromyzon marinus) control program. This study used an inverse variance weighting method to integrate Lake Huron sea lamprey population estimates derived from two estimation procedures: 1) prediction of the lake-wide spawning population from a regression model based on stream size and, 2) whole-lake mark and recapture estimates. In addition, we used a re-sampling procedure to evaluate the effect of trading off sampling effort between the regression and mark-recapture models. Population estimates derived from the regression model ranged from 132,000 to 377,000 while mark-recapture estimates of marked recently metamorphosed juveniles and parasitic sea lampreys ranged from 536,000 to 634,000 and 484,000 to 1,608,000, respectively. The precision of the estimates varied greatly among estimation procedures and years. The integrated estimate of the mark-recapture and spawner regression procedures ranged from 252,000 to 702,000 transformers. The re-sampling procedure indicated that the regression model is more sensitive to reduction in sampling effort than the mark-recapture model. Reliance on either the regression or mark-recapture model alone could produce misleading estimates of abundance of sea lampreys and the effect of the control program on sea lamprey abundance. These analyses indicate that the precision of the lakewide population estimate can be maximized by re-allocating sampling effort from marking sea lampreys to trapping additional streams.

Variability of drought characteristics in Europe over the last 250 years

NASA Astrophysics Data System (ADS)

Hanel, Martin; Rakovec, Oldrich; Máca, Petr; Markonis, Yannis; Samaniego, Luis; Kumar, Rohini

2017-04-01

The mesoscale hydrological model (mHM) with spatial resolution 0.5deg is applied to simulate water balance across large part of continental Europe (excluding Scandinavia and Russia) for the period 1766-2015. The model is driven by available European gridded monthly temperature and precipitation reconstructions (Casty et al, 2007), which are disaggregated into daily time step using k-nearest neighbour resampling (Lall and Sharma, 1996). To quantify the uncertainty due to temporal disaggregation, several replicates of precipitation and temperature fields for the whole period are considered. In parallel, model parameter uncertainty is addressed by an ensemble of parameter realizations provided by Rakovec et al (2016). Deficit periods with respect to total runoff and soil moisture are identified at each grid cell using the variable threshold method. We assess the severity and intensity of drought, spatial extent of area under drought as well as the length of deficit periods. In addition, we also determine the occurrence of multi-year droughts during the period and evaluate the extremity of recent droughts in Europe (i.e., 2003, 2015) in the context of the whole multi-decadal record. References: Casty, C., Raible, C.C., Stocker, T.F., Luterbacher, J. and H. Wanner (2007), A European pattern climatology 1766-2000, Climate Dynamics, 29(7), DOI:10.1007/s00382-007-0257-6. Lall, U., and A. Sharma (1996), A Nearest neighbor bootstrap for resampling hydrologic time series, Water Resour. Res., 32(3), 679-693, DOI:10.1029/95WR02966. Rakovec, O., Kumar, R., Attinger, S. and Samaniego, L. (2016), Improving the realism of hydrologic model functioning through multivariate parameter estimation, Water Resour. Res., 52, DOI:10.1002/2016WR019430

Forensic identification of resampling operators: A semi non-intrusive approach.

PubMed

Cao, Gang; Zhao, Yao; Ni, Rongrong

2012-03-10

Recently, several new resampling operators have been proposed and successfully invalidate the existing resampling detectors. However, the reliability of such anti-forensic techniques is unaware and needs to be investigated. In this paper, we focus on the forensic identification of digital image resampling operators including the traditional type and the anti-forensic type which hides the trace of traditional resampling. Various resampling algorithms involving geometric distortion (GD)-based, dual-path-based and postprocessing-based are investigated. The identification is achieved in the manner of semi non-intrusive, supposing the resampling software could be accessed. Given an input pattern of monotone signal, polarity aberration of GD-based resampled signal's first derivative is analyzed theoretically and measured by effective feature metric. Dual-path-based and postprocessing-based resampling can also be identified by feeding proper test patterns. Experimental results on various parameter settings demonstrate the effectiveness of the proposed approach. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Variable selection under multiple imputation using the bootstrap in a prognostic study

PubMed Central

Heymans, Martijn W; van Buuren, Stef; Knol, Dirk L; van Mechelen, Willem; de Vet, Henrica CW

2007-01-01

Background Missing data is a challenging problem in many prognostic studies. Multiple imputation (MI) accounts for imputation uncertainty that allows for adequate statistical testing. We developed and tested a methodology combining MI with bootstrapping techniques for studying prognostic variable selection. Method In our prospective cohort study we merged data from three different randomized controlled trials (RCTs) to assess prognostic variables for chronicity of low back pain. Among the outcome and prognostic variables data were missing in the range of 0 and 48.1%. We used four methods to investigate the influence of respectively sampling and imputation variation: MI only, bootstrap only, and two methods that combine MI and bootstrapping. Variables were selected based on the inclusion frequency of each prognostic variable, i.e. the proportion of times that the variable appeared in the model. The discriminative and calibrative abilities of prognostic models developed by the four methods were assessed at different inclusion levels. Results We found that the effect of imputation variation on the inclusion frequency was larger than the effect of sampling variation. When MI and bootstrapping were combined at the range of 0% (full model) to 90% of variable selection, bootstrap corrected c-index values of 0.70 to 0.71 and slope values of 0.64 to 0.86 were found. Conclusion We recommend to account for both imputation and sampling variation in sets of missing data. The new procedure of combining MI with bootstrapping for variable selection, results in multivariable prognostic models with good performance and is therefore attractive to apply on data sets with missing values. PMID:17629912

Use of a (137)Cs re-sampling technique to investigate temporal changes in soil erosion and sediment mobilisation for a small forested catchment in southern Italy.

PubMed

Porto, Paolo; Walling, Des E; Alewell, Christine; Callegari, Giovanni; Mabit, Lionel; Mallimo, Nicola; Meusburger, Katrin; Zehringer, Markus

2014-12-01

Soil erosion and both its on-site and off-site impacts are increasingly seen as a serious environmental problem across the world. The need for an improved evidence base on soil loss and soil redistribution rates has directed attention to the use of fallout radionuclides, and particularly (137)Cs, for documenting soil redistribution rates. This approach possesses important advantages over more traditional means of documenting soil erosion and soil redistribution. However, one key limitation of the approach is the time-averaged or lumped nature of the estimated erosion rates. In nearly all cases, these will relate to the period extending from the main period of bomb fallout to the time of sampling. Increasing concern for the impact of global change, particularly that related to changing land use and climate change, has frequently directed attention to the need to document changes in soil redistribution rates within this period. Re-sampling techniques, which should be distinguished from repeat-sampling techniques, have the potential to meet this requirement. As an example, the use of a re-sampling technique to derive estimates of the mean annual net soil loss from a small (1.38 ha) forested catchment in southern Italy is reported. The catchment was originally sampled in 1998 and samples were collected from points very close to the original sampling points again in 2013. This made it possible to compare the estimate of mean annual erosion for the period 1954-1998 with that for the period 1999-2013. The availability of measurements of sediment yield from the catchment for parts of the overall period made it possible to compare the results provided by the (137)Cs re-sampling study with the estimates of sediment yield for the same periods. In order to compare the estimates of soil loss and sediment yield for the two different periods, it was necessary to establish the uncertainty associated with the individual estimates. In the absence of a generally accepted procedure for such calculations, key factors influencing the uncertainty of the estimates were identified and a procedure developed. The results of the study demonstrated that there had been no significant change in mean annual soil loss in recent years and this was consistent with the information provided by the estimates of sediment yield from the catchment for the same periods. The study demonstrates the potential for using a re-sampling technique to document recent changes in soil redistribution rates. Copyright © 2014. Published by Elsevier Ltd.

The prevalence of terraced treescapes in analyses of phylogenetic data sets.

PubMed

Dobrin, Barbara H; Zwickl, Derrick J; Sanderson, Michael J

2018-04-04

The pattern of data availability in a phylogenetic data set may lead to the formation of terraces, collections of equally optimal trees. Terraces can arise in tree space if trees are scored with parsimony or with partitioned, edge-unlinked maximum likelihood. Theory predicts that terraces can be large, but their prevalence in contemporary data sets has never been surveyed. We selected 26 data sets and phylogenetic trees reported in recent literature and investigated the terraces to which the trees would belong, under a common set of inference assumptions. We examined terrace size as a function of the sampling properties of the data sets, including taxon coverage density (the proportion of taxon-by-gene positions with any data present) and a measure of gene sampling "sufficiency". We evaluated each data set in relation to the theoretical minimum gene sampling depth needed to reduce terrace size to a single tree, and explored the impact of the terraces found in replicate trees in bootstrap methods. Terraces were identified in nearly all data sets with taxon coverage densities < 0.90. They were not found, however, in high-coverage-density (i.e., ≥ 0.94) transcriptomic and genomic data sets. The terraces could be very large, and size varied inversely with taxon coverage density and with gene sampling sufficiency. Few data sets achieved a theoretical minimum gene sampling depth needed to reduce terrace size to a single tree. Terraces found during bootstrap resampling reduced overall support. If certain inference assumptions apply, trees estimated from empirical data sets often belong to large terraces of equally optimal trees. Terrace size correlates to data set sampling properties. Data sets seldom include enough genes to reduce terrace size to one tree. When bootstrap replicate trees lie on a terrace, statistical support for phylogenetic hypotheses may be reduced. Although some of the published analyses surveyed were conducted with edge-linked inference models (which do not induce terraces), unlinked models have been used and advocated. The present study describes the potential impact of that inference assumption on phylogenetic inference in the context of the kinds of multigene data sets now widely assembled for large-scale tree construction.

Estimation of daily interfractional larynx residual setup error after isocentric alignment for head and neck radiotherapy: quality assurance implications for target volume and organs‐at‐risk margination using daily CT on‐rails imaging

PubMed Central

Baron, Charles A.; Awan, Musaddiq J.; Mohamed, Abdallah S.R.; Akel, Imad; Rosenthal, David I.; Gunn, G. Brandon; Garden, Adam S.; Dyer, Brandon A.; Court, Laurence; Sevak, Parag R.; Kocak‐Uzel, Esengul

2014-01-01

Larynx may alternatively serve as a target or organs at risk (OAR) in head and neck cancer (HNC) image‐guided radiotherapy (IGRT). The objective of this study was to estimate IGRT parameters required for larynx positional error independent of isocentric alignment and suggest population‐based compensatory margins. Ten HNC patients receiving radiotherapy (RT) with daily CT on‐rails imaging were assessed. Seven landmark points were placed on each daily scan. Taking the most superior‐anterior point of the C5 vertebra as a reference isocenter for each scan, residual displacement vectors to the other six points were calculated postisocentric alignment. Subsequently, using the first scan as a reference, the magnitude of vector differences for all six points for all scans over the course of treatment was calculated. Residual systematic and random error and the necessary compensatory CTV‐to‐PTV and OAR‐to‐PRV margins were calculated, using both observational cohort data and a bootstrap‐resampled population estimator. The grand mean displacements for all anatomical points was 5.07 mm, with mean systematic error of 1.1 mm and mean random setup error of 2.63 mm, while bootstrapped POIs grand mean displacement was 5.09 mm, with mean systematic error of 1.23 mm and mean random setup error of 2.61 mm. Required margin for CTV‐PTV expansion was 4.6 mm for all cohort points, while the bootstrap estimator of the equivalent margin was 4.9 mm. The calculated OAR‐to‐PRV expansion for the observed residual setup error was 2.7 mm and bootstrap estimated expansion of 2.9 mm. We conclude that the interfractional larynx setup error is a significant source of RT setup/delivery error in HNC, both when the larynx is considered as a CTV or OAR. We estimate the need for a uniform expansion of 5 mm to compensate for setup error if the larynx is a target, or 3 mm if the larynx is an OAR, when using a nonlaryngeal bony isocenter. PACS numbers: 87.55.D‐, 87.55.Qr

Assessment of predictive performance in incomplete data by combining internal validation and multiple imputation.

PubMed

Wahl, Simone; Boulesteix, Anne-Laure; Zierer, Astrid; Thorand, Barbara; van de Wiel, Mark A

2016-10-26

Missing values are a frequent issue in human studies. In many situations, multiple imputation (MI) is an appropriate missing data handling strategy, whereby missing values are imputed multiple times, the analysis is performed in every imputed data set, and the obtained estimates are pooled. If the aim is to estimate (added) predictive performance measures, such as (change in) the area under the receiver-operating characteristic curve (AUC), internal validation strategies become desirable in order to correct for optimism. It is not fully understood how internal validation should be combined with multiple imputation. In a comprehensive simulation study and in a real data set based on blood markers as predictors for mortality, we compare three combination strategies: Val-MI, internal validation followed by MI on the training and test parts separately, MI-Val, MI on the full data set followed by internal validation, and MI(-y)-Val, MI on the full data set omitting the outcome followed by internal validation. Different validation strategies, including bootstrap und cross-validation, different (added) performance measures, and various data characteristics are considered, and the strategies are evaluated with regard to bias and mean squared error of the obtained performance estimates. In addition, we elaborate on the number of resamples and imputations to be used, and adopt a strategy for confidence interval construction to incomplete data. Internal validation is essential in order to avoid optimism, with the bootstrap 0.632+ estimate representing a reliable method to correct for optimism. While estimates obtained by MI-Val are optimistically biased, those obtained by MI(-y)-Val tend to be pessimistic in the presence of a true underlying effect. Val-MI provides largely unbiased estimates, with a slight pessimistic bias with increasing true effect size, number of covariates and decreasing sample size. In Val-MI, accuracy of the estimate is more strongly improved by increasing the number of bootstrap draws rather than the number of imputations. With a simple integrated approach, valid confidence intervals for performance estimates can be obtained. When prognostic models are developed on incomplete data, Val-MI represents a valid strategy to obtain estimates of predictive performance measures.

Adaptive topographic mass correction for satellite gravity and gravity gradient data

NASA Astrophysics Data System (ADS)

Holzrichter, Nils; Szwillus, Wolfgang; Götze, Hans-Jürgen

2014-05-01

Subsurface modelling with gravity data includes a reliable topographic mass correction. Since decades, this mandatory step is a standard procedure. However, originally methods were developed for local terrestrial surveys. Therefore, these methods often include defaults like a limited correction area of 167 km around an observation point, resampling topography depending on the distance to the station or disregard the curvature of the earth. New satellite gravity data (e.g. GOCE) can be used for large scale lithospheric modelling with gravity data. The investigation areas can include thousands of kilometres. In addition, measurements are located in the flight height of the satellite (e.g. ~250 km for GOCE). The standard definition of the correction area and the specific grid spacing around an observation point was not developed for stations located in these heights and areas of these dimensions. This asks for a revaluation of the defaults used for topographic correction. We developed an algorithm which resamples the topography based on an adaptive approach. Instead of resampling topography depending on the distance to the station, the grids will be resampled depending on its influence at the station. Therefore, the only value the user has to define is the desired accuracy of the topographic correction. It is not necessary to define the grid spacing and a limited correction area. Furthermore, the algorithm calculates the topographic mass response with a spherical shaped polyhedral body. We show examples for local and global gravity datasets and compare the results of the topographic mass correction to existing approaches. We provide suggestions how satellite gravity and gradient data should be corrected.

Collateral Information for Equating in Small Samples: A Preliminary Investigation

ERIC Educational Resources Information Center

Kim, Sooyeon; Livingston, Samuel A.; Lewis, Charles

2011-01-01

This article describes a preliminary investigation of an empirical Bayes (EB) procedure for using collateral information to improve equating of scores on test forms taken by small numbers of examinees. Resampling studies were done on two different forms of the same test. In each study, EB and non-EB versions of two equating methods--chained linear…

Lightweight CoAP-Based Bootstrapping Service for the Internet of Things.

PubMed

Garcia-Carrillo, Dan; Marin-Lopez, Rafael

2016-03-11

The Internet of Things (IoT) is becoming increasingly important in several fields of industrial applications and personal applications, such as medical e-health, smart cities, etc. The research into protocols and security aspects related to this area is continuously advancing in making these networks more reliable and secure, taking into account these aspects by design. Bootstrapping is a procedure by which a user obtains key material and configuration information, among other parameters, to operate as an authenticated party in a security domain. Until now solutions have focused on re-using security protocols that were not developed for IoT constraints. For this reason, in this work we propose a design and implementation of a lightweight bootstrapping service for IoT networks that leverages one of the application protocols used in IoT : Constrained Application Protocol (CoAP). Additionally, in order to provide flexibility, scalability, support for large scale deployment, accountability and identity federation, our design uses technologies such as the Extensible Authentication Protocol (EAP) and Authentication Authorization and Accounting (AAA). We have named this service CoAP-EAP. First, we review the state of the art in the field of bootstrapping and specifically for IoT. Second, we detail the bootstrapping service: the architecture with entities and interfaces and the flow operation. Third, we obtain performance measurements of CoAP-EAP (bootstrapping time, memory footprint, message processing time, message length and energy consumption) and compare them with PANATIKI. The most significant and constrained representative of the bootstrapping solutions related with CoAP-EAP. As we will show, our solution provides significant improvements, mainly due to an important reduction of the message length.

Lightweight CoAP-Based Bootstrapping Service for the Internet of Things

PubMed Central

Garcia-Carrillo, Dan; Marin-Lopez, Rafael

2016-01-01

The Internet of Things (IoT) is becoming increasingly important in several fields of industrial applications and personal applications, such as medical e-health, smart cities, etc. The research into protocols and security aspects related to this area is continuously advancing in making these networks more reliable and secure, taking into account these aspects by design. Bootstrapping is a procedure by which a user obtains key material and configuration information, among other parameters, to operate as an authenticated party in a security domain. Until now solutions have focused on re-using security protocols that were not developed for IoT constraints. For this reason, in this work we propose a design and implementation of a lightweight bootstrapping service for IoT networks that leverages one of the application protocols used in IoT : Constrained Application Protocol (CoAP). Additionally, in order to provide flexibility, scalability, support for large scale deployment, accountability and identity federation, our design uses technologies such as the Extensible Authentication Protocol (EAP) and Authentication Authorization and Accounting (AAA). We have named this service CoAP-EAP. First, we review the state of the art in the field of bootstrapping and specifically for IoT. Second, we detail the bootstrapping service: the architecture with entities and interfaces and the flow operation. Third, we obtain performance measurements of CoAP-EAP (bootstrapping time, memory footprint, message processing time, message length and energy consumption) and compare them with PANATIKI. The most significant and constrained representative of the bootstrapping solutions related with CoAP-EAP. As we will show, our solution provides significant improvements, mainly due to an important reduction of the message length. PMID:26978362

The effects of normal aging on multiple aspects of financial decision-making.

PubMed

Bangma, Dorien F; Fuermaier, Anselm B M; Tucha, Lara; Tucha, Oliver; Koerts, Janneke

2017-01-01

Financial decision-making (FDM) is crucial for independent living. Due to cognitive decline that accompanies normal aging, older adults might have difficulties in some aspects of FDM. However, an improved knowledge, personal experience and affective decision-making, which are also related to normal aging, may lead to a stable or even improved age-related performance in some other aspects of FDM. Therefore, the present explorative study examines the effects of normal aging on multiple aspects of FDM. One-hundred and eighty participants (range 18-87 years) were assessed with eight FDM tests and several standard neuropsychological tests. Age effects were evaluated using hierarchical multiple regression analyses. The validity of the prediction models was examined by internal validation (i.e. bootstrap resampling procedure) as well as external validation on another, independent, sample of participants (n = 124). Multiple regression and correlation analyses were applied to investigate the mediation effect of standard measures of cognition on the observed effects of age on FDM. On a relatively basic level of FDM (e.g., paying bills or using FDM styles) no significant effects of aging were found. However more complex FDM, such as making decisions in accordance with specific rules, becomes more difficult with advancing age. Furthermore, an older age was found to be related to a decreased sensitivity for impulsive buying. These results were confirmed by the internal and external validation analyses. Mediation effects of numeracy and planning were found to explain parts of the association between one aspect of FDM (i.e. Competence in decision rules) and age; however, these cognitive domains were not able to completely explain the relation between age and FDM. Normal aging has a negative influence on a complex aspect of FDM, however, other aspects appear to be unaffected by normal aging or improve.

Cost in the use of enoxaparin compared with unfractionated heparin in patients with atrial fibrillation undergoing a transesophageal echocardiography-guided cardioversion (from Assessment of Cardioversion using Transesophageal Echocardiography [ACUTE] II randomized multicenter study).

PubMed

Zhao, Liping; Zhang, Zefeng; Kolm, Paul; Jasper, Susan; Lewis, Cheryl; Klein, Allan; Weintraub, William

2008-02-01

The ACUTE II study demonstrated that transesophageal echocardiographically guided cardioversion with enoxaparin in patients with atrial fibrillation was associated with shorter initial hospital stay, more normal sinus rhythm at 5 weeks, and no significant differences in stroke, bleeding, or death compared with unfractionated heparin (UFH). The present study evaluated resource use and costs in enoxaparin (n=76) and UFH (n=79) during 5-week follow-up. Resources included initial and subsequent hospitalizations, study drugs, outpatient services, and emergency room visits. Two costing approaches were employed for the hospitalization costing. The first approach was based on the UB-92 formulation of hospital bill and diagnosis-related group. The second approach was based on UB-92 and imputation using multivariable linear regression. Costs for outpatient and emergency room visits were determined from the Medicare fee schedule. Sensitivity analysis was performed to assess the robustness of the results. A bootstrap resample approach was used to obtain the confidence interval (CI) for the cost differences. Costs of initial and subsequent hospitalizations, outpatient procedures, and emergency room visits were lower in the enoxaparin group. Average total costs remained significantly lower for the enoxaparin group for the 2 costing approaches ($5,800 vs $8,167, difference $2,367, 95% CI 855 to 4,388, for the first approach; $7,942 vs $10,076, difference $2,134, 95% CI 437 to 4,207, for the second approach). Sensitivity analysis showed that cost differences between strategies are robust to variation of drug costs. In conclusion, the use of enoxaparin as a bridging therapy is a cost-saving strategy (similar clinical outcomes and lower costs) for atrial fibrillation.

Relationship between plethysmographic waveform changes and hemodynamic variables in anesthetized, mechanically ventilated patients undergoing continuous cardiac output monitoring.

PubMed

Thiele, Robert H; Colquhoun, Douglas A; Patrie, James; Nie, Sarah H; Huffmyer, Julie L

2011-12-01

To assess the relation between photoplethysmographically-derived parameters and invasively-determined hemodynamic variables. After induction of anesthesia and placement of a Swan-Ganz CCOmbo catheter, a Nonin OEM III probe was placed on each patient's earlobe. Photoplethysmographic signals were recorded in conjunction with cardiac output. Photoplethysmographic metrics (amplitude of absorbance waveform, maximal slope of absorbance waveform, area under the curve, and width) were calculated offline and compared with invasively determined hemodynamic variables. Subject-specific associations between each dependent and independent variable pair were summarized on a per-subject basis by the nonparametric Spearman rank correlation coefficient. The bias-corrected accelerated bootstrap resampling procedure of Efron and Tibshirani was used to obtain a 95% confidence interval for the median subject-specific correlation coefficient, and Wilcoxon sign-rank tests were conducted to test the null hypothesis that the median of the subject-specific correlation coefficients were equal to 0. University hospital. Eighteen patients undergoing coronary artery bypass graft surgery. Placement of a Swan-Ganz CCOmbo catheter and a Nonin OEM III pulse oximetry probe. There was a positive, statistically significant correlation between stroke volume and width (median correlation coefficient, 0.29; confidence interval, 0.01-0.46; p = 0.034). The concordance between changes in stroke volume and changes in width was 53%. No other correlations achieved statistical significance. This study was unable to reproduce the results of prior studies. Only stroke volume and photoplethysmographic width were correlated in this study; however, the correlation and concordance (based on analysis of a 4-quadrant plot) were too weak to be clinically useful. Future studies in patients undergoing low-to-moderate risk surgery may result in improved correlations and clinical utility. Copyright © 2011 Elsevier Inc. All rights reserved.

Statistical inference based on the nonparametric maximum likelihood estimator under double-truncation.

PubMed

Emura, Takeshi; Konno, Yoshihiko; Michimae, Hirofumi

2015-07-01

Doubly truncated data consist of samples whose observed values fall between the right- and left- truncation limits. With such samples, the distribution function of interest is estimated using the nonparametric maximum likelihood estimator (NPMLE) that is obtained through a self-consistency algorithm. Owing to the complicated asymptotic distribution of the NPMLE, the bootstrap method has been suggested for statistical inference. This paper proposes a closed-form estimator for the asymptotic covariance function of the NPMLE, which is computationally attractive alternative to bootstrapping. Furthermore, we develop various statistical inference procedures, such as confidence interval, goodness-of-fit tests, and confidence bands to demonstrate the usefulness of the proposed covariance estimator. Simulations are performed to compare the proposed method with both the bootstrap and jackknife methods. The methods are illustrated using the childhood cancer dataset.

Neural-Net Processed Characteristic Patterns for Measurement of Structural Integrity of Pressure Cycled Components

NASA Technical Reports Server (NTRS)

Decker, A. J.

2001-01-01

A neural-net inspection process has been combined with a bootstrap training procedure and electronic holography to detect changes or damage in a pressure-cycled International Space Station cold plate to be used for cooling instrumentation. The cold plate was excited to vibrate in a normal mode at low amplitude, and the neural net was trained by example to flag small changes in the mode shape. The NDE (nondestructive-evaluation) technique is straightforward but in its infancy; its applications are ad-hoc and uncalibrated. Nevertheless previous research has shown that the neural net can detect displacement changes to better than 1/100 the maximum displacement amplitude. Development efforts that support the NDE technique are mentioned briefly, followed by descriptions of electronic holography and neural-net processing. The bootstrap training procedure and its application to detection of damage in a pressure-cycled cold plate are discussed. Suggestions for calibrating and quantifying the NDE procedure are presented.

CREATION OF A MODEL TO PREDICT SURVIVAL IN PATIENTS WITH REFRACTORY COELIAC DISEASE USING A MULTINATIONAL REGISTRY

PubMed Central

Rubio-Tapia, Alberto; Malamut, Georgia; Verbeek, Wieke H.M.; van Wanrooij, Roy L.J.; Leffler, Daniel A.; Niveloni, Sonia I.; Arguelles-Grande, Carolina; Lahr, Brian D.; Zinsmeister, Alan R.; Murray, Joseph A.; Kelly, Ciaran P.; Bai, Julio C.; Green, Peter H.; Daum, Severin; Mulder, Chris J.J.; Cellier, Christophe

2016-01-01

Background Refractory coeliac disease is a severe complication of coeliac disease with heterogeneous outcome. Aim To create a prognostic model to estimate survival of patients with refractory coeliac disease. Methods We evaluated predictors of 5-year mortality using Cox proportional hazards regression on subjects from a multinational registry. Bootstrap re-sampling was used to internally validate the individual factors and overall model performance. The mean of the estimated regression coefficients from 400 bootstrap models was used to derive a risk score for 5-year mortality. Results The multinational cohort was composed of 232 patients diagnosed with refractory coeliac disease across 7 centers (range of 11–63 cases per center). The median age was 53 years and 150 (64%) were women. A total of 51 subjects died during 5-year follow-up (cumulative 5-year all-cause mortality = 30%). From a multiple variable Cox proportional hazards model, the following variables were significantly associated with 5-year mortality: age at refractory coeliac disease diagnosis (per 20 year increase, hazard ratio = 2.21; 95% confidence interval: 1.38, 3.55), abnormal intraepithelial lymphocytes (hazard ratio = 2.85; 95% confidence interval: 1.22, 6.62), and albumin (per 0.5 unit increase, hazard ratio = 0.72; 95% confidence interval: 0.61, 0.85). A simple weighted 3-factor risk score was created to estimate 5-year survival. Conclusions Using data from a multinational registry and previously-reported risk factors, we create a prognostic model to predict 5-year mortality among patients with refractory coeliac disease. This new model may help clinicians to guide treatment and follow-up. PMID:27485029

Improved human observer performance in digital reconstructed radiograph verification in head and neck cancer radiotherapy.

PubMed

Sturgeon, Jared D; Cox, John A; Mayo, Lauren L; Gunn, G Brandon; Zhang, Lifei; Balter, Peter A; Dong, Lei; Awan, Musaddiq; Kocak-Uzel, Esengul; Mohamed, Abdallah Sherif Radwan; Rosenthal, David I; Fuller, Clifton David

2015-10-01

Digitally reconstructed radiographs (DRRs) are routinely used as an a priori reference for setup correction in radiotherapy. The spatial resolution of DRRs may be improved to reduce setup error in fractionated radiotherapy treatment protocols. The influence of finer CT slice thickness reconstruction (STR) and resultant increased resolution DRRs on physician setup accuracy was prospectively evaluated. Four head and neck patient CT-simulation images were acquired and used to create DRR cohorts by varying STRs at 0.5, 1, 2, 2.5, and 3 mm. DRRs were displaced relative to a fixed isocenter using 0-5 mm random shifts in the three cardinal axes. Physician observers reviewed DRRs of varying STRs and displacements and then aligned reference and test DRRs replicating daily KV imaging workflow. A total of 1,064 images were reviewed by four blinded physicians. Observer errors were analyzed using nonparametric statistics (Friedman's test) to determine whether STR cohorts had detectably different displacement profiles. Post hoc bootstrap resampling was applied to evaluate potential generalizability. The observer-based trial revealed a statistically significant difference between cohort means for observer displacement vector error ([Formula: see text]) and for [Formula: see text]-axis [Formula: see text]. Bootstrap analysis suggests a 15% gain in isocenter translational setup error with reduction of STR from 3 mm to [Formula: see text]2 mm, though interobserver variance was a larger feature than STR-associated measurement variance. Higher resolution DRRs generated using finer CT scan STR resulted in improved observer performance at shift detection and could decrease operator-dependent geometric error. Ideally, CT STRs [Formula: see text]2 mm should be utilized for DRR generation in the head and neck.

Evaluation of wound healing in diabetic foot ulcer using platelet-rich plasma gel: A single-arm clinical trial.

PubMed

Mohammadi, Mohammad Hossein; Molavi, Behnam; Mohammadi, Saeed; Nikbakht, Mohsen; Mohammadi, Ashraf Malek; Mostafaei, Shayan; Norooznezhad, Amir Hossein; Ghorbani Abdegah, Ali; Ghavamzadeh, Ardeshir

2017-04-01

The aim of the present study was to evaluate the effectiveness of using autologous platelet-rich plasma (PRP) gel for treatment of diabetic foot ulcer (DFU) during the first 4 weeks of the treatment. In this longitudinal and single-arm trial, 100 patients were randomly selected after meeting certain inclusion and exclusion criteria; of these 100 patients, 70 (70%) were enrolled in the trial. After the primary care actions such as wound debridement, the area of each wound was calculated and recorded. The PRP therapy (2mL/cm 2 of ulcers) was performed weekly until the healing time for each patient. We used one sample T-test for healing wounds and Bootstrap resampling approach for reporting confidence interval with 1000 Bootstrap samples. The p-value<0.05 were considered statistically significant. The mean (SD) of DFU duration was 19.71 weeks (4.94) for units sampling. The ratio of subjects who withdrew from the study was calculated to be 2 (2.8%). Average area of 71 ulcers in the mentioned number of cases was calculated to be 6.11cm 2 (SD: 4.37). Also, the mean, median (SD) of healing time was 8.7, 8 weeks (SD: 3.93) except for 2 mentioned cases. According to one sample T-test, wound area (cm 2 ), on average, significantly decreased to 51.9% (CI: 46.7-57.1) through the first four weeks of therapy. Furthermore, significant correlation (0.22) was not found between area of ulcers and healing duration (p-value>0.5). According to the results, PRP could be considered as a candidate treatment for non-healing DFUs as it may prevent future complications such as amputation or death in this pathological phenomenon. Copyright © 2016 Elsevier Ltd. All rights reserved.

A random sampling approach for robust estimation of tissue-to-plasma ratio from extremely sparse data.

PubMed

Chu, Hui-May; Ette, Ene I

2005-09-02

his study was performed to develop a new nonparametric approach for the estimation of robust tissue-to-plasma ratio from extremely sparsely sampled paired data (ie, one sample each from plasma and tissue per subject). Tissue-to-plasma ratio was estimated from paired/unpaired experimental data using independent time points approach, area under the curve (AUC) values calculated with the naïve data averaging approach, and AUC values calculated using sampling based approaches (eg, the pseudoprofile-based bootstrap [PpbB] approach and the random sampling approach [our proposed approach]). The random sampling approach involves the use of a 2-phase algorithm. The convergence of the sampling/resampling approaches was investigated, as well as the robustness of the estimates produced by different approaches. To evaluate the latter, new data sets were generated by introducing outlier(s) into the real data set. One to 2 concentration values were inflated by 10% to 40% from their original values to produce the outliers. Tissue-to-plasma ratios computed using the independent time points approach varied between 0 and 50 across time points. The ratio obtained from AUC values acquired using the naive data averaging approach was not associated with any measure of uncertainty or variability. Calculating the ratio without regard to pairing yielded poorer estimates. The random sampling and pseudoprofile-based bootstrap approaches yielded tissue-to-plasma ratios with uncertainty and variability. However, the random sampling approach, because of the 2-phase nature of its algorithm, yielded more robust estimates and required fewer replications. Therefore, a 2-phase random sampling approach is proposed for the robust estimation of tissue-to-plasma ratio from extremely sparsely sampled data.

Creation of a model to predict survival in patients with refractory coeliac disease using a multinational registry.

PubMed

Rubio-Tapia, A; Malamut, G; Verbeek, W H M; van Wanrooij, R L J; Leffler, D A; Niveloni, S I; Arguelles-Grande, C; Lahr, B D; Zinsmeister, A R; Murray, J A; Kelly, C P; Bai, J C; Green, P H; Daum, S; Mulder, C J J; Cellier, C

2016-10-01

Refractory coeliac disease is a severe complication of coeliac disease with heterogeneous outcome. To create a prognostic model to estimate survival of patients with refractory coeliac disease. We evaluated predictors of 5-year mortality using Cox proportional hazards regression on subjects from a multinational registry. Bootstrap resampling was used to internally validate the individual factors and overall model performance. The mean of the estimated regression coefficients from 400 bootstrap models was used to derive a risk score for 5-year mortality. The multinational cohort was composed of 232 patients diagnosed with refractory coeliac disease across seven centres (range of 11-63 cases per centre). The median age was 53 years and 150 (64%) were women. A total of 51 subjects died during a 5-year follow-up (cumulative 5-year all-cause mortality = 30%). From a multiple variable Cox proportional hazards model, the following variables were significantly associated with 5-year mortality: age at refractory coeliac disease diagnosis (per 20 year increase, hazard ratio = 2.21; 95% confidence interval, CI: 1.38-3.55), abnormal intraepithelial lymphocytes (hazard ratio = 2.85; 95% CI: 1.22-6.62), and albumin (per 0.5 unit increase, hazard ratio = 0.72; 95% CI: 0.61-0.85). A simple weighted three-factor risk score was created to estimate 5-year survival. Using data from a multinational registry and previously reported risk factors, we create a prognostic model to predict 5-year mortality among patients with refractory coeliac disease. This new model may help clinicians to guide treatment and follow-up. © 2016 John Wiley & Sons Ltd.

Modular reweighting software for statistical mechanical analysis of biased equilibrium data

NASA Astrophysics Data System (ADS)

Sindhikara, Daniel J.

2012-07-01

Here a simple, useful, modular approach and software suite designed for statistical reweighting and analysis of equilibrium ensembles is presented. Statistical reweighting is useful and sometimes necessary for analysis of equilibrium enhanced sampling methods, such as umbrella sampling or replica exchange, and also in experimental cases where biasing factors are explicitly known. Essentially, statistical reweighting allows extrapolation of data from one or more equilibrium ensembles to another. Here, the fundamental separable steps of statistical reweighting are broken up into modules - allowing for application to the general case and avoiding the black-box nature of some “all-inclusive” reweighting programs. Additionally, the programs included are, by-design, written with little dependencies. The compilers required are either pre-installed on most systems, or freely available for download with minimal trouble. Examples of the use of this suite applied to umbrella sampling and replica exchange molecular dynamics simulations will be shown along with advice on how to apply it in the general case. New version program summaryProgram title: Modular reweighting version 2 Catalogue identifier: AEJH_v2_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEJH_v2_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: GNU General Public License, version 3 No. of lines in distributed program, including test data, etc.: 179 118 No. of bytes in distributed program, including test data, etc.: 8 518 178 Distribution format: tar.gz Programming language: C++, Python 2.6+, Perl 5+ Computer: Any Operating system: Any RAM: 50-500 MB Supplementary material: An updated version of the original manuscript (Comput. Phys. Commun. 182 (2011) 2227) is available Classification: 4.13 Catalogue identifier of previous version: AEJH_v1_0 Journal reference of previous version: Comput. Phys. Commun. 182 (2011) 2227 Does the new version supersede the previous version?: Yes Nature of problem: While equilibrium reweighting is ubiquitous, there are no public programs available to perform the reweighting in the general case. Further, specific programs often suffer from many library dependencies and numerical instability. Solution method: This package is written in a modular format that allows for easy applicability of reweighting in the general case. Modules are small, numerically stable, and require minimal libraries. Reasons for new version: Some minor bugs, some upgrades needed, error analysis added. analyzeweight.py/analyzeweight.py2 has been replaced by “multihist.py”. This new program performs all the functions of its predecessor while being versatile enough to handle other types of histograms and probability analysis. “bootstrap.py” was added. This script performs basic bootstrap resampling allowing for error analysis of data. “avg_dev_distribution.py” was added. This program computes the averages and standard deviations of multiple distributions, making error analysis (e.g. from bootstrap resampling) easier to visualize. WRE.cpp was slightly modified purely for cosmetic reasons. The manual was updated for clarity and to reflect version updates. Examples were removed from the manual in favor of online tutorials (packaged examples remain). Examples were updated to reflect the new format. An additional example is included to demonstrate error analysis. Running time: Preprocessing scripts 1-5 minutes, WHAM engine <1 minute, postprocess script ∼1-5 minutes.

Limited sampling hampers “big data” estimation of species richness in a tropical biodiversity hotspot

PubMed Central

Engemann, Kristine; Enquist, Brian J; Sandel, Brody; Boyle, Brad; Jørgensen, Peter M; Morueta-Holme, Naia; Peet, Robert K; Violle, Cyrille; Svenning, Jens-Christian

2015-01-01

Macro-scale species richness studies often use museum specimens as their main source of information. However, such datasets are often strongly biased due to variation in sampling effort in space and time. These biases may strongly affect diversity estimates and may, thereby, obstruct solid inference on the underlying diversity drivers, as well as mislead conservation prioritization. In recent years, this has resulted in an increased focus on developing methods to correct for sampling bias. In this study, we use sample-size-correcting methods to examine patterns of tropical plant diversity in Ecuador, one of the most species-rich and climatically heterogeneous biodiversity hotspots. Species richness estimates were calculated based on 205,735 georeferenced specimens of 15,788 species using the Margalef diversity index, the Chao estimator, the second-order Jackknife and Bootstrapping resampling methods, and Hill numbers and rarefaction. Species richness was heavily correlated with sampling effort, and only rarefaction was able to remove this effect, and we recommend this method for estimation of species richness with “big data” collections. PMID:25692000

Limited sampling hampers "big data" estimation of species richness in a tropical biodiversity hotspot.

PubMed

Engemann, Kristine; Enquist, Brian J; Sandel, Brody; Boyle, Brad; Jørgensen, Peter M; Morueta-Holme, Naia; Peet, Robert K; Violle, Cyrille; Svenning, Jens-Christian

2015-02-01

Macro-scale species richness studies often use museum specimens as their main source of information. However, such datasets are often strongly biased due to variation in sampling effort in space and time. These biases may strongly affect diversity estimates and may, thereby, obstruct solid inference on the underlying diversity drivers, as well as mislead conservation prioritization. In recent years, this has resulted in an increased focus on developing methods to correct for sampling bias. In this study, we use sample-size-correcting methods to examine patterns of tropical plant diversity in Ecuador, one of the most species-rich and climatically heterogeneous biodiversity hotspots. Species richness estimates were calculated based on 205,735 georeferenced specimens of 15,788 species using the Margalef diversity index, the Chao estimator, the second-order Jackknife and Bootstrapping resampling methods, and Hill numbers and rarefaction. Species richness was heavily correlated with sampling effort, and only rarefaction was able to remove this effect, and we recommend this method for estimation of species richness with "big data" collections.

Variable Selection in the Presence of Missing Data: Imputation-based Methods.

PubMed

Zhao, Yize; Long, Qi

2017-01-01

Variable selection plays an essential role in regression analysis as it identifies important variables that associated with outcomes and is known to improve predictive accuracy of resulting models. Variable selection methods have been widely investigated for fully observed data. However, in the presence of missing data, methods for variable selection need to be carefully designed to account for missing data mechanisms and statistical techniques used for handling missing data. Since imputation is arguably the most popular method for handling missing data due to its ease of use, statistical methods for variable selection that are combined with imputation are of particular interest. These methods, valid used under the assumptions of missing at random (MAR) and missing completely at random (MCAR), largely fall into three general strategies. The first strategy applies existing variable selection methods to each imputed dataset and then combine variable selection results across all imputed datasets. The second strategy applies existing variable selection methods to stacked imputed datasets. The third variable selection strategy combines resampling techniques such as bootstrap with imputation. Despite recent advances, this area remains under-developed and offers fertile ground for further research.

The seven deadly sins of DNA barcoding.

PubMed

Collins, R A; Cruickshank, R H

2013-11-01

Despite the broad benefits that DNA barcoding can bring to a diverse range of biological disciplines, a number of shortcomings still exist in terms of the experimental design of studies incorporating this approach. One underlying reason for this lies in the confusion that often exists between species discovery and specimen identification, and this is reflected in the way that hypotheses are generated and tested. Although these aims can be associated, they are quite distinct and require different methodological approaches, but their conflation has led to the frequently inappropriate use of commonly used analytical methods such as neighbour-joining trees, bootstrap resampling and fixed distance thresholds. Furthermore, the misidentification of voucher specimens can also have serious implications for end users of reference libraries such as the Barcode of Life Data Systems, and in this regard we advocate increased diligence in the a priori identification of specimens to be used for this purpose. This commentary provides an assessment of seven deficiencies that we identify as common in the DNA barcoding literature, and outline some potential improvements for its adaptation and adoption towards more reliable and accurate outcomes. © 2012 John Wiley & Sons Ltd.

Methods to achieve accurate projection of regional and global raster databases

USGS Publications Warehouse

Usery, E.L.; Seong, J.C.; Steinwand, D.R.; Finn, M.P.

2002-01-01

This research aims at building a decision support system (DSS) for selecting an optimum projection considering various factors, such as pixel size, areal extent, number of categories, spatial pattern of categories, resampling methods, and error correction methods. Specifically, this research will investigate three goals theoretically and empirically and, using the already developed empirical base of knowledge with these results, develop an expert system for map projection of raster data for regional and global database modeling. The three theoretical goals are as follows: (1) The development of a dynamic projection that adjusts projection formulas for latitude on the basis of raster cell size to maintain equal-sized cells. (2) The investigation of the relationships between the raster representation and the distortion of features, number of categories, and spatial pattern. (3) The development of an error correction and resampling procedure that is based on error analysis of raster projection.

Uncertainty estimation of Intensity-Duration-Frequency relationships: A regional analysis

NASA Astrophysics Data System (ADS)

Mélèse, Victor; Blanchet, Juliette; Molinié, Gilles

2018-03-01

We propose in this article a regional study of uncertainties in IDF curves derived from point-rainfall maxima. We develop two generalized extreme value models based on the simple scaling assumption, first in the frequentist framework and second in the Bayesian framework. Within the frequentist framework, uncertainties are obtained i) from the Gaussian density stemming from the asymptotic normality theorem of the maximum likelihood and ii) with a bootstrap procedure. Within the Bayesian framework, uncertainties are obtained from the posterior densities. We confront these two frameworks on the same database covering a large region of 100, 000 km2 in southern France with contrasted rainfall regime, in order to be able to draw conclusion that are not specific to the data. The two frameworks are applied to 405 hourly stations with data back to the 1980's, accumulated in the range 3 h-120 h. We show that i) the Bayesian framework is more robust than the frequentist one to the starting point of the estimation procedure, ii) the posterior and the bootstrap densities are able to better adjust uncertainty estimation to the data than the Gaussian density, and iii) the bootstrap density give unreasonable confidence intervals, in particular for return levels associated to large return period. Therefore our recommendation goes towards the use of the Bayesian framework to compute uncertainty.

Bi-resampled data study

NASA Technical Reports Server (NTRS)

Benner, R.; Young, W.

1977-01-01

The results of an experimental study conducted to determine the geometric and radiometric effects of double resampling (bi-resampling) performed on image data in the process of performing map projection transformations are reported.

Comparing Eye Tracking with Electrooculography for Measuring Individual Sentence Comprehension Duration

PubMed Central

Müller, Jana Annina; Wendt, Dorothea; Kollmeier, Birger; Brand, Thomas

2016-01-01

The aim of this study was to validate a procedure for performing the audio-visual paradigm introduced by Wendt et al. (2015) with reduced practical challenges. The original paradigm records eye fixations using an eye tracker and calculates the duration of sentence comprehension based on a bootstrap procedure. In order to reduce practical challenges, we first reduced the measurement time by evaluating a smaller measurement set with fewer trials. The results of 16 listeners showed effects comparable to those obtained when testing the original full measurement set on a different collective of listeners. Secondly, we introduced electrooculography as an alternative technique for recording eye movements. The correlation between the results of the two recording techniques (eye tracker and electrooculography) was r = 0.97, indicating that both methods are suitable for estimating the processing duration of individual participants. Similar changes in processing duration arising from sentence complexity were found using the eye tracker and the electrooculography procedure. Thirdly, the time course of eye fixations was estimated with an alternative procedure, growth curve analysis, which is more commonly used in recent studies analyzing eye tracking data. The results of the growth curve analysis were compared with the results of the bootstrap procedure. Both analysis methods show similar processing durations. PMID:27764125

Empirical best linear unbiased prediction method for small areas with restricted maximum likelihood and bootstrap procedure to estimate the average of household expenditure per capita in Banjar Regency

NASA Astrophysics Data System (ADS)

Aminah, Agustin Siti; Pawitan, Gandhi; Tantular, Bertho

2017-03-01

So far, most of the data published by Statistics Indonesia (BPS) as data providers for national statistics are still limited to the district level. Less sufficient sample size for smaller area levels to make the measurement of poverty indicators with direct estimation produced high standard error. Therefore, the analysis based on it is unreliable. To solve this problem, the estimation method which can provide a better accuracy by combining survey data and other auxiliary data is required. One method often used for the estimation is the Small Area Estimation (SAE). There are many methods used in SAE, one of them is Empirical Best Linear Unbiased Prediction (EBLUP). EBLUP method of maximum likelihood (ML) procedures does not consider the loss of degrees of freedom due to estimating β with β ^. This drawback motivates the use of the restricted maximum likelihood (REML) procedure. This paper proposed EBLUP with REML procedure for estimating poverty indicators by modeling the average of household expenditures per capita and implemented bootstrap procedure to calculate MSE (Mean Square Error) to compare the accuracy EBLUP method with the direct estimation method. Results show that EBLUP method reduced MSE in small area estimation.

Cost-effectiveness of silver dressings for paediatric partial thickness burns: An economic evaluation from a randomized controlled trial.

PubMed

Gee Kee, E; Stockton, K; Kimble, R M; Cuttle, L; McPhail, S M

2017-06-01

Partial thickness burns of up to 10% total body surface area (TBSA) in children are common injuries primarily treated in the outpatient setting using expensive silver-containing dressings. However, economic evaluations in the paediatric burns population are lacking to assist healthcare providers when choosing which dressing to use. The aim of this study was to conduct a cost-effectiveness analysis of three silver dressings for partial thickness burns ≤10% TBSA in children aged 0-15 years using days to full wound re-epithelialization as the health outcome. This study was a trial based economic evaluation (incremental cost effectiveness) conducted from a healthcare provider perspective. Ninety-six children participated in the trial investigating Acticoat™, Acticoat™ with Mepitel™ or Mepilex Ag™. Costs directly related to the management of partial thickness burns ≤10% TBSA were collected during the trial from March 2013 to July 2014 and for a one year after re-epithelialization time horizon. Incremental cost effectiveness ratios were estimated and dominance probabilities calculated from bootstrap resampling trial data. Sensitivity analyses were conducted to examine the potential effect of accounting for infrequent, but high cost, skin grafting surgical procedures. Costs (dressing, labour, analgesics, scar management) were considerably lower in the Mepilex Ag™ group (median AUD$94.45) compared to the Acticoat™ (median $244.90) and Acticoat™ with Mepitel™ (median $196.66) interventions. There was a 99% and 97% probability that Mepilex Ag™ dominated (cheaper and more effective than) Acticoat™ and Acticoat™ with Mepitel™, respectively. This pattern of dominance was consistent across raw cost and effects, after a priori adjustments, and sensitivity analyses. There was an 82% probability that Acticoat™ with Mepitel dominated Acticoat™ in the primary analysis, although this probability was sensitive to the effect of skin graft procedures. This economic evaluation has demonstrated that Mepilex Ag™ was the dominant dressing choice over both Acticoat™ and Acticoat™ with Mepitel™ in this trial-based economic evaluation and is recommended for treatment of paediatric partial thickness burns ≤10% TBSA. Copyright © 2016 Elsevier Ltd and ISBI. All rights reserved.

Mesoscale, Radiometrically Referenced, Multi-Temporal Hyperspectral Data for Co2 Leak Detection by Locating Spatial Variation of Biophysically Relevant Parameters

NASA Astrophysics Data System (ADS)

McCann, Cooper Patrick

Low-cost flight-based hyperspectral imaging systems have the potential to provide valuable information for ecosystem and environmental studies as well as aide in land management and land health monitoring. This thesis describes (1) a bootstrap method of producing mesoscale, radiometrically-referenced hyperspectral data using the Landsat surface reflectance (LaSRC) data product as a reference target, (2) biophysically relevant basis functions to model the reflectance spectra, (3) an unsupervised classification technique based on natural histogram splitting of these biophysically relevant parameters, and (4) local and multi-temporal anomaly detection. The bootstrap method extends standard processing techniques to remove uneven illumination conditions between flight passes, allowing the creation of radiometrically self-consistent data. Through selective spectral and spatial resampling, LaSRC data is used as a radiometric reference target. Advantages of the bootstrap method include the need for minimal site access, no ancillary instrumentation, and automated data processing. Data from a flight on 06/02/2016 is compared with concurrently collected ground based reflectance spectra as a means of validation achieving an average error of 2.74%. Fitting reflectance spectra using basis functions, based on biophysically relevant spectral features, allows both noise and data reductions while shifting information from spectral bands to biophysical features. Histogram splitting is used to determine a clustering based on natural splittings of these fit parameters. The Indian Pines reference data enabled comparisons of the efficacy of this technique to established techniques. The splitting technique is shown to be an improvement over the ISODATA clustering technique with an overall accuracy of 34.3/19.0% before merging and 40.9/39.2% after merging. This improvement is also seen as an improvement of kappa before/after merging of 24.8/30.5 for the histogram splitting technique compared to 15.8/28.5 for ISODATA. Three hyperspectral flights over the Kevin Dome area, covering 1843 ha, acquired 06/21/2014, 06/24/2015 and 06/26/2016 are examined with different methods of anomaly detection. Detection of anomalies within a single data set is examined to determine, on a local scale, areas that are significantly different from the surrounding area. Additionally, the detection and identification of persistent anomalies and non-persistent anomalies was investigated across multiple data sets.

Parametric and non-parametric masking of randomness in sequence alignments can be improved and leads to better resolved trees.

PubMed

Kück, Patrick; Meusemann, Karen; Dambach, Johannes; Thormann, Birthe; von Reumont, Björn M; Wägele, Johann W; Misof, Bernhard

2010-03-31

Methods of alignment masking, which refers to the technique of excluding alignment blocks prior to tree reconstructions, have been successful in improving the signal-to-noise ratio in sequence alignments. However, the lack of formally well defined methods to identify randomness in sequence alignments has prevented a routine application of alignment masking. In this study, we compared the effects on tree reconstructions of the most commonly used profiling method (GBLOCKS) which uses a predefined set of rules in combination with alignment masking, with a new profiling approach (ALISCORE) based on Monte Carlo resampling within a sliding window, using different data sets and alignment methods. While the GBLOCKS approach excludes variable sections above a certain threshold which choice is left arbitrary, the ALISCORE algorithm is free of a priori rating of parameter space and therefore more objective. ALISCORE was successfully extended to amino acids using a proportional model and empirical substitution matrices to score randomness in multiple sequence alignments. A complex bootstrap resampling leads to an even distribution of scores of randomly similar sequences to assess randomness of the observed sequence similarity. Testing performance on real data, both masking methods, GBLOCKS and ALISCORE, helped to improve tree resolution. The sliding window approach was less sensitive to different alignments of identical data sets and performed equally well on all data sets. Concurrently, ALISCORE is capable of dealing with different substitution patterns and heterogeneous base composition. ALISCORE and the most relaxed GBLOCKS gap parameter setting performed best on all data sets. Correspondingly, Neighbor-Net analyses showed the most decrease in conflict. Alignment masking improves signal-to-noise ratio in multiple sequence alignments prior to phylogenetic reconstruction. Given the robust performance of alignment profiling, alignment masking should routinely be used to improve tree reconstructions. Parametric methods of alignment profiling can be easily extended to more complex likelihood based models of sequence evolution which opens the possibility of further improvements.

Cost-effectiveness of FreeO2 in patients with chronic obstructive pulmonary disease hospitalised for acute exacerbations: analysis of a pilot study in Quebec

PubMed Central

Poder, Thomas G; Kouakou, Christian R C; Bouchard, Pierre-Alexandre; Tremblay, Véronique; Blais, Sébastien; Maltais, François; Lellouche, François

2018-01-01

Objective Conduct a cost-effectiveness analysis of FreeO2 technology versus manual oxygen-titration technology for patients with chronic obstructive pulmonary disease (COPD) hospitalised for acute exacerbations. Setting Tertiary acute care hospital in Quebec, Canada. Participants 47 patients with COPD hospitalised for acute exacerbations. Intervention An automated oxygen-titration and oxygen-weaning technology. Methods and outcomes The costs for hospitalisation and follow-up for 180 days were calculated using a microcosting approach and included the cost of FreeO2 technology. Incremental cost-effectiveness ratios (ICERs) were calculated using bootstrap resampling with 5000 replications. The main effect variable was the percentage of time spent at the target oxygen saturation (SpO2). The other two effect variables were the time spent in hyperoxia (target SpO2+5%) and in severe hypoxaemia (SpO2 <85%). The resamplings were based on data from a randomised controlled trial with 47 patients with COPD hospitalised for acute exacerbations. Results FreeO2 generated savings of 20.7% of the per-patient costs at 180 days (ie, −$C2959.71). This decrease is nevertheless not significant at the 95% threshold (P=0.13), but the effect variables all improved (P<0.001). The improvement in the time spent at the target SpO2 was 56.3%. The ICERs indicate that FreeO2 technology is more cost-effective than manual oxygen titration with a savings of −$C96.91 per percentage point of time spent at the target SpO2 (95% CI −301.26 to 116.96). Conclusion FreeO2 technology could significantly enhance the efficiency of the health system by reducing per-patient costs at 180 days. A study with a larger patient sample needs to be carried out to confirm these preliminary results. Trial registration number NCT01393015; Post-results. PMID:29362258

Bootstrap-after-bootstrap model averaging for reducing model uncertainty in model selection for air pollution mortality studies.

PubMed

Roberts, Steven; Martin, Michael A

2010-01-01

Concerns have been raised about findings of associations between particulate matter (PM) air pollution and mortality that have been based on a single "best" model arising from a model selection procedure, because such a strategy may ignore model uncertainty inherently involved in searching through a set of candidate models to find the best model. Model averaging has been proposed as a method of allowing for model uncertainty in this context. To propose an extension (double BOOT) to a previously described bootstrap model-averaging procedure (BOOT) for use in time series studies of the association between PM and mortality. We compared double BOOT and BOOT with Bayesian model averaging (BMA) and a standard method of model selection [standard Akaike's information criterion (AIC)]. Actual time series data from the United States are used to conduct a simulation study to compare and contrast the performance of double BOOT, BOOT, BMA, and standard AIC. Double BOOT produced estimates of the effect of PM on mortality that have had smaller root mean squared error than did those produced by BOOT, BMA, and standard AIC. This performance boost resulted from estimates produced by double BOOT having smaller variance than those produced by BOOT and BMA. Double BOOT is a viable alternative to BOOT and BMA for producing estimates of the mortality effect of PM.

Image re-sampling detection through a novel interpolation kernel.

PubMed

Hilal, Alaa

2018-06-01

Image re-sampling involved in re-size and rotation transformations is an essential element block in a typical digital image alteration. Fortunately, traces left from such processes are detectable, proving that the image has gone a re-sampling transformation. Within this context, we present in this paper two original contributions. First, we propose a new re-sampling interpolation kernel. It depends on five independent parameters that controls its amplitude, angular frequency, standard deviation, and duration. Then, we demonstrate its capacity to imitate the same behavior of the most frequent interpolation kernels used in digital image re-sampling applications. Secondly, the proposed model is used to characterize and detect the correlation coefficients involved in re-sampling transformations. The involved process includes a minimization of an error function using the gradient method. The proposed method is assessed over a large database of 11,000 re-sampled images. Additionally, it is implemented within an algorithm in order to assess images that had undergone complex transformations. Obtained results demonstrate better performance and reduced processing time when compared to a reference method validating the suitability of the proposed approaches. Copyright © 2018 Elsevier B.V. All rights reserved.

Exploration of the factor structure of the Kirton Adaption-Innovation Inventory using bootstrapping estimation.

PubMed

Im, Subin; Min, Soonhong

2013-04-01

Exploratory factor analyses of the Kirton Adaption-Innovation Inventory (KAI), which serves to measure individual cognitive styles, generally indicate three factors: sufficiency of originality, efficiency, and rule/group conformity. In contrast, a 2005 study by Im and Hu using confirmatory factor analysis supported a four-factor structure, dividing the sufficiency of originality dimension into two subdimensions, idea generation and preference for change. This study extends Im and Hu's (2005) study of a derived version of the KAI by providing additional evidence of the four-factor structure. Specifically, the authors test the robustness of the parameter estimates to the violation of normality assumptions in the sample using bootstrap methods. A bias-corrected confidence interval bootstrapping procedure conducted among a sample of 356 participants--members of the Arkansas Household Research Panel, with middle SES and average age of 55.6 yr. (SD = 13.9)--showed that the four-factor model with two subdimensions of sufficiency of originality fits the data significantly better than the three-factor model in non-normality conditions.

The Effect of Special Reduction Procedures of IFU Observations from Gemini-NIFS on Dynamical Measurements of Nearby AGN

NASA Astrophysics Data System (ADS)

Pope, Crystal L.; Crenshaw, D. Michael; Fischer, Travis C.

2016-01-01

We present a preliminary analysis of the inflows and outflows in the narrow-line regions of nearby (z<0.1) AGN using observations from the Gemini-North telescope's Near-Infared Integral Field Spectrograph (NIFS). In addition to the standard reduction procedure for NIFS data cubes, these observations were treated for multiple sources of noise and artifacts from the adaptive optics observations and the NIFS instrument. This procedure included the following steps: correction of the differential atmospheric refraction, spatial resampling, low-pass Butterworth spatial filtering, removal of the "instrumental fingerprint", and the Richardson-Lucy deconvolution. We compare measurements from NIFS data cubes with and without the additional correction procedures to determine the effect of this data treatment on our scientific results.

System for monitoring non-coincident, nonstationary process signals

DOEpatents

Gross, Kenneth C.; Wegerich, Stephan W.

2005-01-04

An improved system for monitoring non-coincident, non-stationary, process signals. The mean, variance, and length of a reference signal is defined by an automated system, followed by the identification of the leading and falling edges of a monitored signal and the length of the monitored signal. The monitored signal is compared to the reference signal, and the monitored signal is resampled in accordance with the reference signal. The reference signal is then correlated with the resampled monitored signal such that the reference signal and the resampled monitored signal are coincident in time with each other. The resampled monitored signal is then compared to the reference signal to determine whether the resampled monitored signal is within a set of predesignated operating conditions.

Communication Optimizations for a Wireless Distributed Prognostic Framework

NASA Technical Reports Server (NTRS)

Saha, Sankalita; Saha, Bhaskar; Goebel, Kai

2009-01-01

Distributed architecture for prognostics is an essential step in prognostic research in order to enable feasible real-time system health management. Communication overhead is an important design problem for such systems. In this paper we focus on communication issues faced in the distributed implementation of an important class of algorithms for prognostics - particle filters. In spite of being computation and memory intensive, particle filters lend well to distributed implementation except for one significant step - resampling. We propose new resampling scheme called parameterized resampling that attempts to reduce communication between collaborating nodes in a distributed wireless sensor network. Analysis and comparison with relevant resampling schemes is also presented. A battery health management system is used as a target application. A new resampling scheme for distributed implementation of particle filters has been discussed in this paper. Analysis and comparison of this new scheme with existing resampling schemes in the context for minimizing communication overhead have also been discussed. Our proposed new resampling scheme performs significantly better compared to other schemes by attempting to reduce both the communication message length as well as number total communication messages exchanged while not compromising prediction accuracy and precision. Future work will explore the effects of the new resampling scheme in the overall computational performance of the whole system as well as full implementation of the new schemes on the Sun SPOT devices. Exploring different network architectures for efficient communication is an importance future research direction as well.

Zero- vs. one-dimensional, parametric vs. non-parametric, and confidence interval vs. hypothesis testing procedures in one-dimensional biomechanical trajectory analysis.

PubMed

Pataky, Todd C; Vanrenterghem, Jos; Robinson, Mark A

2015-05-01

Biomechanical processes are often manifested as one-dimensional (1D) trajectories. It has been shown that 1D confidence intervals (CIs) are biased when based on 0D statistical procedures, and the non-parametric 1D bootstrap CI has emerged in the Biomechanics literature as a viable solution. The primary purpose of this paper was to clarify that, for 1D biomechanics datasets, the distinction between 0D and 1D methods is much more important than the distinction between parametric and non-parametric procedures. A secondary purpose was to demonstrate that a parametric equivalent to the 1D bootstrap exists in the form of a random field theory (RFT) correction for multiple comparisons. To emphasize these points we analyzed six datasets consisting of force and kinematic trajectories in one-sample, paired, two-sample and regression designs. Results showed, first, that the 1D bootstrap and other 1D non-parametric CIs were qualitatively identical to RFT CIs, and all were very different from 0D CIs. Second, 1D parametric and 1D non-parametric hypothesis testing results were qualitatively identical for all six datasets. Last, we highlight the limitations of 1D CIs by demonstrating that they are complex, design-dependent, and thus non-generalizable. These results suggest that (i) analyses of 1D data based on 0D models of randomness are generally biased unless one explicitly identifies 0D variables before the experiment, and (ii) parametric and non-parametric 1D hypothesis testing provide an unambiguous framework for analysis when one׳s hypothesis explicitly or implicitly pertains to whole 1D trajectories. Copyright © 2015 Elsevier Ltd. All rights reserved.

A Multivariate and Probabilistic Assessment of Drought in the Pacific Northwest under Observed and Future Climate.

NASA Astrophysics Data System (ADS)

Mortuza, M. R.; Demissie, Y. K.

2015-12-01

In lieu with the recent and anticipated more server and frequently droughts incidences in Yakima River Basin (YRB), a reliable and comprehensive drought assessment is deemed necessary to avoid major crop production loss and better manage the water right issues in the region during low precipitation and/or snow accumulation years. In this study, we have conducted frequency analysis of hydrological droughts and quantified associated uncertainty in the YRB under both historical and changing climate. Streamflow drought index (SDI) was employed to identify mutually correlated drought characteristics (e.g., severity, duration and peak). The historical and future characteristics of drought were estimated by applying tri-variate copulas probability distribution, which effectively describe the joint distribution and dependence of drought severity, duration, and peak. The associated prediction uncertainty, related to parameters of the joint probability and climate projections, were evaluated using the Bayesian approach with bootstrap resampling. For the climate change scenarios, two future representative pathways (RCP4.5 and RCP8.5) from University of Idaho's Multivariate Adaptive Constructed Analogs (MACA) database were considered. The results from the study are expected to provide useful information towards drought risk management in YRB under anticipated climate changes.

An artifact caused by undersampling optimal trees in supermatrix analyses of locally sampled characters.

PubMed

Simmons, Mark P; Goloboff, Pablo A

2013-10-01

Empirical and simulated examples are used to demonstrate an artifact caused by undersampling optimal trees in data matrices that consist mostly or entirely of locally sampled (as opposed to globally, for most or all terminals) characters. The artifact is that unsupported clades consisting entirely of terminals scored for the same locally sampled partition may be resolved and assigned high resampling support-despite their being properly unsupported (i.e., not resolved in the strict consensus of all optimal trees). This artifact occurs despite application of random-addition sequences for stepwise terminal addition. The artifact is not necessarily obviated with thorough conventional branch swapping methods (even tree-bisection-reconnection) when just a single tree is held, as is sometimes implemented in parsimony bootstrap pseudoreplicates, and in every GARLI, PhyML, and RAxML pseudoreplicate and search for the most likely tree for the matrix as a whole. Hence GARLI, RAxML, and PhyML-based likelihood results require extra scrutiny, particularly when they provide high resolution and support for clades that are entirely unsupported by methods that perform more thorough searches, as in most parsimony analyses. Copyright © 2013 Elsevier Inc. All rights reserved.

Forensic discrimination of copper wire using trace element concentrations.

PubMed

Dettman, Joshua R; Cassabaum, Alyssa A; Saunders, Christopher P; Snyder, Deanna L; Buscaglia, JoAnn

2014-08-19

Copper may be recovered as evidence in high-profile cases such as thefts and improvised explosive device incidents; comparison of copper samples from the crime scene and those associated with the subject of an investigation can provide probative associative evidence and investigative support. A solution-based inductively coupled plasma mass spectrometry method for measuring trace element concentrations in high-purity copper was developed using standard reference materials. The method was evaluated for its ability to use trace element profiles to statistically discriminate between copper samples considering the precision of the measurement and manufacturing processes. The discriminating power was estimated by comparing samples chosen on the basis of the copper refining and production process to represent the within-source (samples expected to be similar) and between-source (samples expected to be different) variability using multivariate parametric- and empirical-based data simulation models with bootstrap resampling. If the false exclusion rate is set to 5%, >90% of the copper samples can be correctly determined to originate from different sources using a parametric-based model and >87% with an empirical-based approach. These results demonstrate the potential utility of the developed method for the comparison of copper samples encountered as forensic evidence.

Modelling road accident blackspots data with the discrete generalized Pareto distribution.

PubMed

Prieto, Faustino; Gómez-Déniz, Emilio; Sarabia, José María

2014-10-01

This study shows how road traffic networks events, in particular road accidents on blackspots, can be modelled with simple probabilistic distributions. We considered the number of crashes and the number of fatalities on Spanish blackspots in the period 2003-2007, from Spanish General Directorate of Traffic (DGT). We modelled those datasets, respectively, with the discrete generalized Pareto distribution (a discrete parametric model with three parameters) and with the discrete Lomax distribution (a discrete parametric model with two parameters, and particular case of the previous model). For that, we analyzed the basic properties of both parametric models: cumulative distribution, survival, probability mass, quantile and hazard functions, genesis and rth-order moments; applied two estimation methods of their parameters: the μ and (μ+1) frequency method and the maximum likelihood method; used two goodness-of-fit tests: Chi-square test and discrete Kolmogorov-Smirnov test based on bootstrap resampling; and compared them with the classical negative binomial distribution in terms of absolute probabilities and in models including covariates. We found that those probabilistic models can be useful to describe the road accident blackspots datasets analyzed. Copyright © 2014 Elsevier Ltd. All rights reserved.

Tourscape: A systematic approach towards a sustainable rural tourism management

NASA Astrophysics Data System (ADS)

Lo, M. C.; Wang, Y. C.; Songan, P.; Yeo, A. W.

2014-02-01

Tourism plays an important role in the Malaysian economy as it is considered to be one of the corner stones of the country's economy. The purpose of this research is to conduct an analysis based on the existing tourism industry in rural tourism destinations in Malaysia by examining the impact of economics, environmental, social and cultural factors of the tourism industry on the local communities in Malaysia. 516 respondents comprising of tourism stakeholders from 34 rural tourism sites in Malaysia took part voluntarily in this study. To assess the developed model, SmartPLS 2.0 (M3) was applied based on path modeling and then bootstrapping with 200 re-samples was applied to generate the standard error of the estimate and t-values. Subsequently, a system named Tourscape was designed to manage the information. This system can be considered as a benchmark for tourism industry stakeholders as it is able to display the current situational analysis and the tourism health of selected tourism destination sites by capturing data and information, not only from local communities but industry players and tourists as well. The findings from this study revealed that the cooperation from various stakeholders has created significant impact on the development of rural tourism.

The relationship between trait self-control, consideration for future consequence and organizational citizenship behavior among Chinese employees.

PubMed

Wang, Yu-Jie; Dou, Kai; Tang, Zhi-Wen

2017-01-01

Organizational citizenship behavior (OCB) is important to the development of an organization. Research into factors that foster OCB and the underlying processes are therefore substantially crucial. The current study aimed to test the association between trait self-control and OCB and the mediating role of consideration for future consequence. Four hundred and ninety-four Chinese employees (275 men, 219 women) took part in the study. Participants completed a battery of self-report measures online that assessed trait self-control, tendencies of consideration of future consequence, and organizational citizenship behavior. Path analysis was conducted and bootstrapping technique (N = 5000), a resampling method that is asymptotically more accurate than the standard intervals using sample variance and assumptions of normality, was used to judge the significance of the mediation. Results of path analysis showed that trait self-control was positively related to OCB. More importantly, the "trait self-control-OCB" link was mediated by consideration of future consequence-future, but not by consideration of future consequence-immediate. Employees with high trait self-control engage in more organizational citizenship behavior and this link can be partly explained by consideration of future consequence-future.

Survival estimation and the effects of dependency among animals

USGS Publications Warehouse

Schmutz, Joel A.; Ward, David H.; Sedinger, James S.; Rexstad, Eric A.

1995-01-01

Survival models assume that fates of individuals are independent, yet the robustness of this assumption has been poorly quantified. We examine how empirically derived estimates of the variance of survival rates are affected by dependency in survival probability among individuals. We used Monte Carlo simulations to generate known amounts of dependency among pairs of individuals and analyzed these data with Kaplan-Meier and Cormack-Jolly-Seber models. Dependency significantly increased these empirical variances as compared to theoretically derived estimates of variance from the same populations. Using resighting data from 168 pairs of black brant, we used a resampling procedure and program RELEASE to estimate empirical and mean theoretical variances. We estimated that the relationship between paired individuals caused the empirical variance of the survival rate to be 155% larger than the empirical variance for unpaired individuals. Monte Carlo simulations and use of this resampling strategy can provide investigators with information on how robust their data are to this common assumption of independent survival probabilities.

Resampling: A Marriage of Computers and Statistics. ERIC/TM Digest.

ERIC Educational Resources Information Center

Rudner, Lawrence M.; Shafer, Mary Morello

Advances in computer technology are making it possible for educational researchers to use simpler statistical methods to address a wide range of questions with smaller data sets and fewer, and less restrictive, assumptions. This digest introduces computationally intensive statistics, collectively called resampling techniques. Resampling is a…

Technical and scale efficiency in public and private Irish nursing homes - a bootstrap DEA approach.

PubMed

Ni Luasa, Shiovan; Dineen, Declan; Zieba, Marta

2016-10-27

This article provides methodological and empirical insights into the estimation of technical efficiency in the nursing home sector. Focusing on long-stay care and using primary data, we examine technical and scale efficiency in 39 public and 73 private Irish nursing homes by applying an input-oriented data envelopment analysis (DEA). We employ robust bootstrap methods to validate our nonparametric DEA scores and to integrate the effects of potential determinants in estimating the efficiencies. Both the homogenous and two-stage double bootstrap procedures are used to obtain confidence intervals for the bias-corrected DEA scores. Importantly, the application of the double bootstrap approach affords true DEA technical efficiency scores after adjusting for the effects of ownership, size, case-mix, and other determinants such as location, and quality. Based on our DEA results for variable returns to scale technology, the average technical efficiency score is 62 %, and the mean scale efficiency is 88 %, with nearly all units operating on the increasing returns to scale part of the production frontier. Moreover, based on the double bootstrap results, Irish nursing homes are less technically efficient, and more scale efficient than the conventional DEA estimates suggest. Regarding the efficiency determinants, in terms of ownership, we find that private facilities are less efficient than the public units. Furthermore, the size of the nursing home has a positive effect, and this reinforces our finding that Irish homes produce at increasing returns to scale. Also, notably, we find that a tendency towards quality improvements can lead to poorer technical efficiency performance.

Transformational leadership in the consumer service workgroup: competing models of job satisfaction, change commitment, and cooperative conflict resolution.

PubMed

Yang, Yi-Feng

2014-02-01

This paper discusses the effects of transformational leadership on cooperative conflict resolution (management) by evaluating several alternative models related to the mediating role of job satisfaction and change commitment. Samples of data from customer service personnel in Taiwan were analyzed. Based on the bootstrap sample technique, an empirical study was carried out to yield the best fitting model. The procedure of hierarchical nested model analysis was used, incorporating the methods of bootstrapping mediation, PRODCLIN2, and structural equation modeling (SEM) comparison. The analysis suggests that leadership that promotes integration (change commitment) and provides inspiration and motivation (job satisfaction), in the proper order, creates the means for cooperative conflict resolution.

Rapid Training of Information Extraction with Local and Global Data Views

DTIC Science & Technology

2012-05-01

relation type extension system based on active learning a relation type extension system based on semi-supervised learning, and a crossdomain...bootstrapping system for domain adaptive named entity extraction. The active learning procedure adopts features extracted at the sentence level as the local

Fine-mapping additive and dominant SNP effects using group-LASSO and Fractional Resample Model Averaging

PubMed Central

Sabourin, Jeremy; Nobel, Andrew B.; Valdar, William

2014-01-01

Genomewide association studies sometimes identify loci at which both the number and identities of the underlying causal variants are ambiguous. In such cases, statistical methods that model effects of multiple SNPs simultaneously can help disentangle the observed patterns of association and provide information about how those SNPs could be prioritized for follow-up studies. Current multi-SNP methods, however, tend to assume that SNP effects are well captured by additive genetics; yet when genetic dominance is present, this assumption translates to reduced power and faulty prioritizations. We describe a statistical procedure for prioritizing SNPs at GWAS loci that efficiently models both additive and dominance effects. Our method, LLARRMA-dawg, combines a group LASSO procedure for sparse modeling of multiple SNP effects with a resampling procedure based on fractional observation weights; it estimates for each SNP the robustness of association with the phenotype both to sampling variation and to competing explanations from other SNPs. In producing a SNP prioritization that best identifies underlying true signals, we show that: our method easily outperforms a single marker analysis; when additive-only signals are present, our joint model for additive and dominance is equivalent to or only slightly less powerful than modeling additive-only effects; and, when dominance signals are present, even in combination with substantial additive effects, our joint model is unequivocally more powerful than a model assuming additivity. We also describe how performance can be improved through calibrated randomized penalization, and discuss how dominance in ungenotyped SNPs can be incorporated through either heterozygote dosage or multiple imputation. PMID:25417853

An add-in implementation of the RESAMPLING syntax under Microsoft EXCEL.

PubMed

Meineke, I

2000-10-01

The RESAMPLING syntax defines a set of powerful commands, which allow the programming of probabilistic statistical models with few, easily memorized statements. This paper presents an implementation of the RESAMPLING syntax using Microsoft EXCEL with Microsoft WINDOWS(R) as a platform. Two examples are given to demonstrate typical applications of RESAMPLING in biomedicine. Details of the implementation with special emphasis on the programming environment are discussed at length. The add-in is available electronically to interested readers upon request. The use of the add-in facilitates numerical statistical analyses of data from within EXCEL in a comfortable way.

Assessment of Person Fit Using Resampling-Based Approaches

ERIC Educational Resources Information Center

Sinharay, Sandip

2016-01-01

De la Torre and Deng suggested a resampling-based approach for person-fit assessment (PFA). The approach involves the use of the [math equation unavailable] statistic, a corrected expected a posteriori estimate of the examinee ability, and the Monte Carlo (MC) resampling method. The Type I error rate of the approach was closer to the nominal level…

CME Velocity and Acceleration Error Estimates Using the Bootstrap Method

NASA Technical Reports Server (NTRS)

Michalek, Grzegorz; Gopalswamy, Nat; Yashiro, Seiji

2017-01-01

The bootstrap method is used to determine errors of basic attributes of coronal mass ejections (CMEs) visually identified in images obtained by the Solar and Heliospheric Observatory (SOHO) mission's Large Angle and Spectrometric Coronagraph (LASCO) instruments. The basic parameters of CMEs are stored, among others, in a database known as the SOHO/LASCO CME catalog and are widely employed for many research studies. The basic attributes of CMEs (e.g. velocity and acceleration) are obtained from manually generated height-time plots. The subjective nature of manual measurements introduces random errors that are difficult to quantify. In many studies the impact of such measurement errors is overlooked. In this study we present a new possibility to estimate measurements errors in the basic attributes of CMEs. This approach is a computer-intensive method because it requires repeating the original data analysis procedure several times using replicate datasets. This is also commonly called the bootstrap method in the literature. We show that the bootstrap approach can be used to estimate the errors of the basic attributes of CMEs having moderately large numbers of height-time measurements. The velocity errors are in the vast majority small and depend mostly on the number of height-time points measured for a particular event. In the case of acceleration, the errors are significant, and for more than half of all CMEs, they are larger than the acceleration itself.

Using the Bootstrap Method for a Statistical Significance Test of Differences between Summary Histograms

NASA Technical Reports Server (NTRS)

Xu, Kuan-Man

2006-01-01

A new method is proposed to compare statistical differences between summary histograms, which are the histograms summed over a large ensemble of individual histograms. It consists of choosing a distance statistic for measuring the difference between summary histograms and using a bootstrap procedure to calculate the statistical significance level. Bootstrapping is an approach to statistical inference that makes few assumptions about the underlying probability distribution that describes the data. Three distance statistics are compared in this study. They are the Euclidean distance, the Jeffries-Matusita distance and the Kuiper distance. The data used in testing the bootstrap method are satellite measurements of cloud systems called cloud objects. Each cloud object is defined as a contiguous region/patch composed of individual footprints or fields of view. A histogram of measured values over footprints is generated for each parameter of each cloud object and then summary histograms are accumulated over all individual histograms in a given cloud-object size category. The results of statistical hypothesis tests using all three distances as test statistics are generally similar, indicating the validity of the proposed method. The Euclidean distance is determined to be most suitable after comparing the statistical tests of several parameters with distinct probability distributions among three cloud-object size categories. Impacts on the statistical significance levels resulting from differences in the total lengths of satellite footprint data between two size categories are also discussed.

The effects of normal aging on multiple aspects of financial decision-making

PubMed Central

Bangma, Dorien F.; Fuermaier, Anselm B. M.; Tucha, Lara; Tucha, Oliver; Koerts, Janneke

2017-01-01

Objectives Financial decision-making (FDM) is crucial for independent living. Due to cognitive decline that accompanies normal aging, older adults might have difficulties in some aspects of FDM. However, an improved knowledge, personal experience and affective decision-making, which are also related to normal aging, may lead to a stable or even improved age-related performance in some other aspects of FDM. Therefore, the present explorative study examines the effects of normal aging on multiple aspects of FDM. Methods One-hundred and eighty participants (range 18–87 years) were assessed with eight FDM tests and several standard neuropsychological tests. Age effects were evaluated using hierarchical multiple regression analyses. The validity of the prediction models was examined by internal validation (i.e. bootstrap resampling procedure) as well as external validation on another, independent, sample of participants (n = 124). Multiple regression and correlation analyses were applied to investigate the mediation effect of standard measures of cognition on the observed effects of age on FDM. Results On a relatively basic level of FDM (e.g., paying bills or using FDM styles) no significant effects of aging were found. However more complex FDM, such as making decisions in accordance with specific rules, becomes more difficult with advancing age. Furthermore, an older age was found to be related to a decreased sensitivity for impulsive buying. These results were confirmed by the internal and external validation analyses. Mediation effects of numeracy and planning were found to explain parts of the association between one aspect of FDM (i.e. Competence in decision rules) and age; however, these cognitive domains were not able to completely explain the relation between age and FDM. Conclusion Normal aging has a negative influence on a complex aspect of FDM, however, other aspects appear to be unaffected by normal aging or improve. PMID:28792973

Applying the Bootstrap to Taxometric Analysis: Generating Empirical Sampling Distributions to Help Interpret Results

ERIC Educational Resources Information Center

Ruscio, John; Ruscio, Ayelet Meron; Meron, Mati

2007-01-01

Meehl's taxometric method was developed to distinguish categorical and continuous constructs. However, taxometric output can be difficult to interpret because expected results for realistic data conditions and differing procedural implementations have not been derived analytically or studied through rigorous simulations. By applying bootstrap…

Kappa statistic for the clustered dichotomous responses from physicians and patients

PubMed Central

Kang, Chaeryon; Qaqish, Bahjat; Monaco, Jane; Sheridan, Stacey L.; Cai, Jianwen

2013-01-01

The bootstrap method for estimating the standard error of the kappa statistic in the presence of clustered data is evaluated. Such data arise, for example, in assessing agreement between physicians and their patients regarding their understanding of the physician-patient interaction and discussions. We propose a computationally efficient procedure for generating correlated dichotomous responses for physicians and assigned patients for simulation studies. The simulation result demonstrates that the proposed bootstrap method produces better estimate of the standard error and better coverage performance compared to the asymptotic standard error estimate that ignores dependence among patients within physicians with at least a moderately large number of clusters. An example of an application to a coronary heart disease prevention study is presented. PMID:23533082

[Investigation of the accurate measurement of the basic imaging properties for the digital radiographic system based on flat panel detector].

PubMed

Katayama, R; Sakai, S; Sakaguchi, T; Maeda, T; Takada, K; Hayabuchi, N; Morishita, J

2008-07-20

PURPOSE/AIM OF THE EXHIBIT: The purpose of this exhibit is: 1. To explain "resampling", an image data processing, performed by the digital radiographic system based on flat panel detector (FPD). 2. To show the influence of "resampling" on the basic imaging properties. 3. To present accurate measurement methods of the basic imaging properties of the FPD system. 1. The relationship between the matrix sizes of the output image and the image data acquired on FPD that automatically changes depending on a selected image size (FOV). 2. The explanation of the image data processing of "resampling". 3. The evaluation results of the basic imaging properties of the FPD system using two types of DICOM image to which "resampling" was performed: characteristic curves, presampled MTFs, noise power spectra, detective quantum efficiencies. CONCLUSION/SUMMARY: The major points of the exhibit are as follows: 1. The influence of "resampling" should not be disregarded in the evaluation of the basic imaging properties of the flat panel detector system. 2. It is necessary for the basic imaging properties to be measured by using DICOM image to which no "resampling" is performed.

Comment on: 'A Poisson resampling method for simulating reduced counts in nuclear medicine images'.

PubMed

de Nijs, Robin

2015-07-21

In order to be able to calculate half-count images from already acquired data, White and Lawson published their method based on Poisson resampling. They verified their method experimentally by measurements with a Co-57 flood source. In this comment their results are reproduced and confirmed by a direct numerical simulation in Matlab. Not only Poisson resampling, but also two direct redrawing methods were investigated. Redrawing methods were based on a Poisson and a Gaussian distribution. Mean, standard deviation, skewness and excess kurtosis half-count/full-count ratios were determined for all methods, and compared to the theoretical values for a Poisson distribution. Statistical parameters showed the same behavior as in the original note and showed the superiority of the Poisson resampling method. Rounding off before saving of the half count image had a severe impact on counting statistics for counts below 100. Only Poisson resampling was not affected by this, while Gaussian redrawing was less affected by it than Poisson redrawing. Poisson resampling is the method of choice, when simulating half-count (or less) images from full-count images. It simulates correctly the statistical properties, also in the case of rounding off of the images.

On long-only information-based portfolio diversification framework

NASA Astrophysics Data System (ADS)

Santos, Raphael A.; Takada, Hellinton H.

2014-12-01

Using the concepts from information theory, it is possible to improve the traditional frameworks for long-only asset allocation. In modern portfolio theory, the investor has two basic procedures: the choice of a portfolio that maximizes its risk-adjusted excess return or the mixed allocation between the maximum Sharpe portfolio and the risk-free asset. In the literature, the first procedure was already addressed using information theory. One contribution of this paper is the consideration of the second procedure in the information theory context. The performance of these approaches was compared with three traditional asset allocation methodologies: the Markowitz's mean-variance, the resampled mean-variance and the equally weighted portfolio. Using simulated and real data, the information theory-based methodologies were verified to be more robust when dealing with the estimation errors.

Wavelet analysis in ecology and epidemiology: impact of statistical tests

PubMed Central

Cazelles, Bernard; Cazelles, Kévin; Chavez, Mario

2014-01-01

Wavelet analysis is now frequently used to extract information from ecological and epidemiological time series. Statistical hypothesis tests are conducted on associated wavelet quantities to assess the likelihood that they are due to a random process. Such random processes represent null models and are generally based on synthetic data that share some statistical characteristics with the original time series. This allows the comparison of null statistics with those obtained from original time series. When creating synthetic datasets, different techniques of resampling result in different characteristics shared by the synthetic time series. Therefore, it becomes crucial to consider the impact of the resampling method on the results. We have addressed this point by comparing seven different statistical testing methods applied with different real and simulated data. Our results show that statistical assessment of periodic patterns is strongly affected by the choice of the resampling method, so two different resampling techniques could lead to two different conclusions about the same time series. Moreover, our results clearly show the inadequacy of resampling series generated by white noise and red noise that are nevertheless the methods currently used in the wide majority of wavelets applications. Our results highlight that the characteristics of a time series, namely its Fourier spectrum and autocorrelation, are important to consider when choosing the resampling technique. Results suggest that data-driven resampling methods should be used such as the hidden Markov model algorithm and the ‘beta-surrogate’ method. PMID:24284892

Wavelet analysis in ecology and epidemiology: impact of statistical tests.

PubMed

Cazelles, Bernard; Cazelles, Kévin; Chavez, Mario

2014-02-06

Wavelet analysis is now frequently used to extract information from ecological and epidemiological time series. Statistical hypothesis tests are conducted on associated wavelet quantities to assess the likelihood that they are due to a random process. Such random processes represent null models and are generally based on synthetic data that share some statistical characteristics with the original time series. This allows the comparison of null statistics with those obtained from original time series. When creating synthetic datasets, different techniques of resampling result in different characteristics shared by the synthetic time series. Therefore, it becomes crucial to consider the impact of the resampling method on the results. We have addressed this point by comparing seven different statistical testing methods applied with different real and simulated data. Our results show that statistical assessment of periodic patterns is strongly affected by the choice of the resampling method, so two different resampling techniques could lead to two different conclusions about the same time series. Moreover, our results clearly show the inadequacy of resampling series generated by white noise and red noise that are nevertheless the methods currently used in the wide majority of wavelets applications. Our results highlight that the characteristics of a time series, namely its Fourier spectrum and autocorrelation, are important to consider when choosing the resampling technique. Results suggest that data-driven resampling methods should be used such as the hidden Markov model algorithm and the 'beta-surrogate' method.

Computationally Efficient Resampling of Nonuniform Oversampled SAR Data

DTIC Science & Technology

2010-05-01

noncoherently . The resample data is calculated using both a simple average and a weighted average of the demodulated data. The average nonuniform...trials with randomly varying accelerations. The results are shown in Fig. 5 for the noncoherent power difference and Fig. 6 for and coherent power...simple average. Figure 5. Noncoherent difference between SAR imagery generated with uniform sampling and nonuniform sampling that was resampled

Performance of DIMTEST-and NOHARM-Based Statistics for Testing Unidimensionality

ERIC Educational Resources Information Center

Finch, Holmes; Habing, Brian

2007-01-01

This Monte Carlo study compares the ability of the parametric bootstrap version of DIMTEST with three goodness-of-fit tests calculated from a fitted NOHARM model to detect violations of the assumption of unidimensionality in testing data. The effectiveness of the procedures was evaluated for different numbers of items, numbers of examinees,…

Consonant Inventories in the Spontaneous Speech of Young Children: A Bootstrapping Procedure

ERIC Educational Resources Information Center

Van Severen, Lieve; Van Den Berg, Renate; Molemans, Inge; Gillis, Steven

2012-01-01

Consonant inventories are commonly drawn to assess the phonological acquisition of toddlers. However, the spontaneous speech data that are analysed often vary substantially in size and composition. Consequently, comparisons between children and across studies are fundamentally hampered. This study aims to examine the effect of sample size on the…

Bootstrap calculation of ultimate strength temperature maxima for neutron irradiated ferritic/martensitic steels

NASA Astrophysics Data System (ADS)

Obraztsov, S. M.; Konobeev, Yu. V.; Birzhevoy, G. A.; Rachkov, V. I.

2006-12-01

The dependence of mechanical properties of ferritic/martensitic (F/M) steels on irradiation temperature is of interest because these steels are used as structural materials for fast, fusion reactors and accelerator driven systems. Experimental data demonstrating temperature peaks in physical and mechanical properties of neutron irradiated pure iron, nickel, vanadium, and austenitic stainless steels are available in the literature. A lack of such an information for F/M steels forces one to apply a computational mathematical-statistical modeling methods. The bootstrap procedure is one of such methods that allows us to obtain the necessary statistical characteristics using only a sample of limited size. In the present work this procedure is used for modeling the frequency distribution histograms of ultimate strength temperature peaks in pure iron and Russian F/M steels EP-450 and EP-823. Results of fitting the sums of Lorentz or Gauss functions to the calculated distributions are presented. It is concluded that there are two temperature (at 360 and 390 °C) peaks of the ultimate strength in EP-450 steel and single peak at 390 °C in EP-823.

Testing for Granger Causality in the Frequency Domain: A Phase Resampling Method.

PubMed

Liu, Siwei; Molenaar, Peter

2016-01-01

This article introduces phase resampling, an existing but rarely used surrogate data method for making statistical inferences of Granger causality in frequency domain time series analysis. Granger causality testing is essential for establishing causal relations among variables in multivariate dynamic processes. However, testing for Granger causality in the frequency domain is challenging due to the nonlinear relation between frequency domain measures (e.g., partial directed coherence, generalized partial directed coherence) and time domain data. Through a simulation study, we demonstrate that phase resampling is a general and robust method for making statistical inferences even with short time series. With Gaussian data, phase resampling yields satisfactory type I and type II error rates in all but one condition we examine: when a small effect size is combined with an insufficient number of data points. Violations of normality lead to slightly higher error rates but are mostly within acceptable ranges. We illustrate the utility of phase resampling with two empirical examples involving multivariate electroencephalography (EEG) and skin conductance data.

A 'bootstrapped' Teaching/Learning Procedure

NASA Astrophysics Data System (ADS)

Odusina Odusote, Olusogo

1998-04-01

Erasing preconceived antiphysics ideas by nonscience/nonmajor physics students have elicited diverse teaching methods. Introductory general physics courses at college level have been taught by a 'bootstrap' approach. A concise treatment of the syllabus by the teacher in about 1/2 of the course duration, with brief exercises and examples. Students are then introduced to real life situations - toys, home appliances, sports, disasters, etc, and the embedded physics concepts discussed. Usually this generates a feeling of deja vu, which elicits desire for more. Each application usually encompasses topics in a broad range of the syllabus. The other half of the course is used by students to work individually/groups on assigned and graded home-works and essays, with guidance from the lecture notes and the teacher/supervisor. An end of course examination shows increase in the success rate.

Kappa statistic for clustered dichotomous responses from physicians and patients.

PubMed

Kang, Chaeryon; Qaqish, Bahjat; Monaco, Jane; Sheridan, Stacey L; Cai, Jianwen

2013-09-20

The bootstrap method for estimating the standard error of the kappa statistic in the presence of clustered data is evaluated. Such data arise, for example, in assessing agreement between physicians and their patients regarding their understanding of the physician-patient interaction and discussions. We propose a computationally efficient procedure for generating correlated dichotomous responses for physicians and assigned patients for simulation studies. The simulation result demonstrates that the proposed bootstrap method produces better estimate of the standard error and better coverage performance compared with the asymptotic standard error estimate that ignores dependence among patients within physicians with at least a moderately large number of clusters. We present an example of an application to a coronary heart disease prevention study. Copyright © 2013 John Wiley & Sons, Ltd.

Rumination and depression in Chinese university students: The mediating role of overgeneral autobiographical memory.

PubMed

Kong, Tianzhu; He, Yini; Auerbach, Randy P; McWhinnie, Chad M; Xiao, Jing

2015-04-01

In this study, we examined the mediator effects of overgeneral autobiographical memory (OGM) on the relationship between rumination and depression in 323 Chinese university students. 323 undergraduates completed the questionnaires measuring OGM (Autobiographical Memory Test), rumination (Ruminative Response Scale) and depression (Center for Epidemiologic Studies Depression Scale). Results using structural equation modeling showed that OGM partially-mediated the relationship between rumination and depression (χ 2 = 88.61, p < .01; RMSEA = .051; SRMR = .040; and CFI = .91). Bootstrap methods were used to assess the magnitude of the indirect effects. The results of the bootstrap estimation procedure and subsequent analyses indicated that the indirect effects of OGM on the relationship between rumination and depressive symptoms were significant. The results indicated that rumination and depression were partially mediated by OGM.

A simple plug-in bagging ensemble based on threshold-moving for classifying binary and multiclass imbalanced data.

PubMed

Collell, Guillem; Prelec, Drazen; Patil, Kaustubh R

2018-01-31

Class imbalance presents a major hurdle in the application of classification methods. A commonly taken approach is to learn ensembles of classifiers using rebalanced data. Examples include bootstrap averaging (bagging) combined with either undersampling or oversampling of the minority class examples. However, rebalancing methods entail asymmetric changes to the examples of different classes, which in turn can introduce their own biases. Furthermore, these methods often require specifying the performance measure of interest a priori, i.e., before learning. An alternative is to employ the threshold moving technique, which applies a threshold to the continuous output of a model, offering the possibility to adapt to a performance measure a posteriori , i.e., a plug-in method. Surprisingly, little attention has been paid to this combination of a bagging ensemble and threshold-moving. In this paper, we study this combination and demonstrate its competitiveness. Contrary to the other resampling methods, we preserve the natural class distribution of the data resulting in well-calibrated posterior probabilities. Additionally, we extend the proposed method to handle multiclass data. We validated our method on binary and multiclass benchmark data sets by using both, decision trees and neural networks as base classifiers. We perform analyses that provide insights into the proposed method.

Geographic and temporal validity of prediction models: Different approaches were useful to examine model performance

PubMed Central

Austin, Peter C.; van Klaveren, David; Vergouwe, Yvonne; Nieboer, Daan; Lee, Douglas S.; Steyerberg, Ewout W.

2017-01-01

Objective Validation of clinical prediction models traditionally refers to the assessment of model performance in new patients. We studied different approaches to geographic and temporal validation in the setting of multicenter data from two time periods. Study Design and Setting We illustrated different analytic methods for validation using a sample of 14,857 patients hospitalized with heart failure at 90 hospitals in two distinct time periods. Bootstrap resampling was used to assess internal validity. Meta-analytic methods were used to assess geographic transportability. Each hospital was used once as a validation sample, with the remaining hospitals used for model derivation. Hospital-specific estimates of discrimination (c-statistic) and calibration (calibration intercepts and slopes) were pooled using random effects meta-analysis methods. I2 statistics and prediction interval width quantified geographic transportability. Temporal transportability was assessed using patients from the earlier period for model derivation and patients from the later period for model validation. Results Estimates of reproducibility, pooled hospital-specific performance, and temporal transportability were on average very similar, with c-statistics of 0.75. Between-hospital variation was moderate according to I2 statistics and prediction intervals for c-statistics. Conclusion This study illustrates how performance of prediction models can be assessed in settings with multicenter data at different time periods. PMID:27262237

Characterization of Escherichia coli isolates from different fecal sources by means of classification tree analysis of fatty acid methyl ester (FAME) profiles.

PubMed

Seurinck, Sylvie; Deschepper, Ellen; Deboch, Bishaw; Verstraete, Willy; Siciliano, Steven

2006-03-01

Microbial source tracking (MST) methods need to be rapid, inexpensive and accurate. Unfortunately, many MST methods provide a wealth of information that is difficult to interpret by the regulators who use this information to make decisions. This paper describes the use of classification tree analysis to interpret the results of a MST method based on fatty acid methyl ester (FAME) profiles of Escherichia coli isolates, and to present results in a format readily interpretable by water quality managers. Raw sewage E. coli isolates and animal E. coli isolates from cow, dog, gull, and horse were isolated and their FAME profiles collected. Correct classification rates determined with leaveone-out cross-validation resulted in an overall low correct classification rate of 61%. A higher overall correct classification rate of 85% was obtained when the animal isolates were pooled together and compared to the raw sewage isolates. Bootstrap aggregation or adaptive resampling and combining of the FAME profile data increased correct classification rates substantially. Other MST methods may be better suited to differentiate between different fecal sources but classification tree analysis has enabled us to distinguish raw sewage from animal E. coli isolates, which previously had not been possible with other multivariate methods such as principal component analysis and cluster analysis.

Retrieval of suspended sediment concentrations using Landsat-8 OLI satellite images in the Orinoco River (Venezuela)

NASA Astrophysics Data System (ADS)

Yepez, Santiago; Laraque, Alain; Martinez, Jean-Michel; De Sa, Jose; Carrera, Juan Manuel; Castellanos, Bartolo; Gallay, Marjorie; Lopez, Jose L.

2018-01-01

In this study, 81 Landsat-8 scenes acquired from 2013 to 2015 were used to estimate the suspended sediment concentration (SSC) in the Orinoco River at its main hydrological station at Ciudad Bolivar, Venezuela. This gauging station monitors an upstream area corresponding to 89% of the total catchment area where the mean discharge is of 33,000 m3·s-1. SSC spatial and temporal variabilities were analyzed in relation to the hydrological cycle and to local geomorphological characteristics of the river mainstream. Three types of atmospheric correction models were evaluated to correct the Landsat-8 images: DOS, FLAASH, and L8SR. Surface reflectance was compared with monthly water sampling to calibrate a SSC retrieval model using a bootstrapping resampling. A regression model based on surface reflectance at the Near-Infrared wavelengths showed the best performance: R2 = 0.92 (N = 27) for the whole range of SSC (18 to 203 mg·l-1) measured at this station during the studied period. The method offers a simple new approach to estimate the SSC along the lower Orinoco River and demonstrates the feasibility and reliability of remote sensing images to map the spatiotemporal variability in sediment transport over large rivers.

Support Vector Feature Selection for Early Detection of Anastomosis Leakage From Bag-of-Words in Electronic Health Records.

PubMed

Soguero-Ruiz, Cristina; Hindberg, Kristian; Rojo-Alvarez, Jose Luis; Skrovseth, Stein Olav; Godtliebsen, Fred; Mortensen, Kim; Revhaug, Arthur; Lindsetmo, Rolv-Ole; Augestad, Knut Magne; Jenssen, Robert

2016-09-01

The free text in electronic health records (EHRs) conveys a huge amount of clinical information about health state and patient history. Despite a rapidly growing literature on the use of machine learning techniques for extracting this information, little effort has been invested toward feature selection and the features' corresponding medical interpretation. In this study, we focus on the task of early detection of anastomosis leakage (AL), a severe complication after elective surgery for colorectal cancer (CRC) surgery, using free text extracted from EHRs. We use a bag-of-words model to investigate the potential for feature selection strategies. The purpose is earlier detection of AL and prediction of AL with data generated in the EHR before the actual complication occur. Due to the high dimensionality of the data, we derive feature selection strategies using the robust support vector machine linear maximum margin classifier, by investigating: 1) a simple statistical criterion (leave-one-out-based test); 2) an intensive-computation statistical criterion (Bootstrap resampling); and 3) an advanced statistical criterion (kernel entropy). Results reveal a discriminatory power for early detection of complications after CRC (sensitivity 100%; specificity 72%). These results can be used to develop prediction models, based on EHR data, that can support surgeons and patients in the preoperative decision making phase.

Application of change-point problem to the detection of plant patches.

PubMed

López, I; Gámez, M; Garay, J; Standovár, T; Varga, Z

2010-03-01

In ecology, if the considered area or space is large, the spatial distribution of individuals of a given plant species is never homogeneous; plants form different patches. The homogeneity change in space or in time (in particular, the related change-point problem) is an important research subject in mathematical statistics. In the paper, for a given data system along a straight line, two areas are considered, where the data of each area come from different discrete distributions, with unknown parameters. In the paper a method is presented for the estimation of the distribution change-point between both areas and an estimate is given for the distributions separated by the obtained change-point. The solution of this problem will be based on the maximum likelihood method. Furthermore, based on an adaptation of the well-known bootstrap resampling, a method for the estimation of the so-called change-interval is also given. The latter approach is very general, since it not only applies in the case of the maximum-likelihood estimation of the change-point, but it can be also used starting from any other change-point estimation known in the ecological literature. The proposed model is validated against typical ecological situations, providing at the same time a verification of the applied algorithms.

Cost-effectiveness of FreeO2 in patients with chronic obstructive pulmonary disease hospitalised for acute exacerbations: analysis of a pilot study in Quebec.

PubMed

Poder, Thomas G; Kouakou, Christian R C; Bouchard, Pierre-Alexandre; Tremblay, Véronique; Blais, Sébastien; Maltais, François; Lellouche, François

2018-01-23

Conduct a cost-effectiveness analysis of FreeO 2 technology versus manual oxygen-titration technology for patients with chronic obstructive pulmonary disease (COPD) hospitalised for acute exacerbations. Tertiary acute care hospital in Quebec, Canada. 47 patients with COPD hospitalised for acute exacerbations. An automated oxygen-titration and oxygen-weaning technology. The costs for hospitalisation and follow-up for 180 days were calculated using a microcosting approach and included the cost of FreeO 2 technology. Incremental cost-effectiveness ratios (ICERs) were calculated using bootstrap resampling with 5000 replications. The main effect variable was the percentage of time spent at the target oxygen saturation (SpO 2 ). The other two effect variables were the time spent in hyperoxia (target SpO 2 +5%) and in severe hypoxaemia (SpO 2 <85%). The resamplings were based on data from a randomised controlled trial with 47 patients with COPD hospitalised for acute exacerbations. FreeO 2 generated savings of 20.7% of the per-patient costs at 180 days (ie, -$C2959.71). This decrease is nevertheless not significant at the 95% threshold (P=0.13), but the effect variables all improved (P<0.001). The improvement in the time spent at the target SpO 2 was 56.3%. The ICERs indicate that FreeO 2 technology is more cost-effective than manual oxygen titration with a savings of -$C96.91 per percentage point of time spent at the target SpO 2 (95% CI -301.26 to 116.96). FreeO 2 technology could significantly enhance the efficiency of the health system by reducing per-patient costs at 180 days. A study with a larger patient sample needs to be carried out to confirm these preliminary results. NCT01393015; Post-results. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

The Use of Invariance and Bootstrap Procedures as a Method to Establish the Reliability of Research Results.

ERIC Educational Resources Information Center

Sandler, Andrew B.

Statistical significance is misused in educational and psychological research when it is applied as a method to establish the reliability of research results. Other techniques have been developed which can be correctly utilized to establish the generalizability of findings. Methods that do provide such estimates are known as invariance or…

Correlation Attenuation Due to Measurement Error: A New Approach Using the Bootstrap Procedure

ERIC Educational Resources Information Center

Padilla, Miguel A.; Veprinsky, Anna

2012-01-01

Issues with correlation attenuation due to measurement error are well documented. More than a century ago, Spearman proposed a correction for attenuation. However, this correction has seen very little use since it can potentially inflate the true correlation beyond one. In addition, very little confidence interval (CI) research has been done for…

Spectral resampling based on user-defined inter-band correlation filter: C3 and C4 grass species classification

NASA Astrophysics Data System (ADS)

Adjorlolo, Clement; Mutanga, Onisimo; Cho, Moses A.; Ismail, Riyad

2013-04-01

In this paper, a user-defined inter-band correlation filter function was used to resample hyperspectral data and thereby mitigate the problem of multicollinearity in classification analysis. The proposed resampling technique convolves the spectral dependence information between a chosen band-centre and its shorter and longer wavelength neighbours. Weighting threshold of inter-band correlation (WTC, Pearson's r) was calculated, whereby r = 1 at the band-centre. Various WTC (r = 0.99, r = 0.95 and r = 0.90) were assessed, and bands with coefficients beyond a chosen threshold were assigned r = 0. The resultant data were used in the random forest analysis to classify in situ C3 and C4 grass canopy reflectance. The respective WTC datasets yielded improved classification accuracies (kappa = 0.82, 0.79 and 0.76) with less correlated wavebands when compared to resampled Hyperion bands (kappa = 0.76). Overall, the results obtained from this study suggested that resampling of hyperspectral data should account for the spectral dependence information to improve overall classification accuracy as well as reducing the problem of multicollinearity.

Ensemble-based prediction of RNA secondary structures.

PubMed

Aghaeepour, Nima; Hoos, Holger H

2013-04-24

Accurate structure prediction methods play an important role for the understanding of RNA function. Energy-based, pseudoknot-free secondary structure prediction is one of the most widely used and versatile approaches, and improved methods for this task have received much attention over the past five years. Despite the impressive progress that as been achieved in this area, existing evaluations of the prediction accuracy achieved by various algorithms do not provide a comprehensive, statistically sound assessment. Furthermore, while there is increasing evidence that no prediction algorithm consistently outperforms all others, no work has been done to exploit the complementary strengths of multiple approaches. In this work, we present two contributions to the area of RNA secondary structure prediction. Firstly, we use state-of-the-art, resampling-based statistical methods together with a previously published and increasingly widely used dataset of high-quality RNA structures to conduct a comprehensive evaluation of existing RNA secondary structure prediction procedures. The results from this evaluation clarify the performance relationship between ten well-known existing energy-based pseudoknot-free RNA secondary structure prediction methods and clearly demonstrate the progress that has been achieved in recent years. Secondly, we introduce AveRNA, a generic and powerful method for combining a set of existing secondary structure prediction procedures into an ensemble-based method that achieves significantly higher prediction accuracies than obtained from any of its component procedures. Our new, ensemble-based method, AveRNA, improves the state of the art for energy-based, pseudoknot-free RNA secondary structure prediction by exploiting the complementary strengths of multiple existing prediction procedures, as demonstrated using a state-of-the-art statistical resampling approach. In addition, AveRNA allows an intuitive and effective control of the trade-off between false negative and false positive base pair predictions. Finally, AveRNA can make use of arbitrary sets of secondary structure prediction procedures and can therefore be used to leverage improvements in prediction accuracy offered by algorithms and energy models developed in the future. Our data, MATLAB software and a web-based version of AveRNA are publicly available at http://www.cs.ubc.ca/labs/beta/Software/AveRNA.

Volume and tissue composition preserving deformation of breast CT images to simulate breast compression in mammographic imaging

NASA Astrophysics Data System (ADS)

Han, Tao; Chen, Lingyun; Lai, Chao-Jen; Liu, Xinming; Shen, Youtao; Zhong, Yuncheng; Ge, Shuaiping; Yi, Ying; Wang, Tianpeng; Shaw, Chris C.

2009-02-01

Images of mastectomy breast specimens have been acquired with a bench top experimental Cone beam CT (CBCT) system. The resulting images have been segmented to model an uncompressed breast for simulation of various CBCT techniques. To further simulate conventional or tomosynthesis mammographic imaging for comparison with the CBCT technique, a deformation technique was developed to convert the CT data for an uncompressed breast to a compressed breast without altering the breast volume or regional breast density. With this technique, 3D breast deformation is separated into two 2D deformations in coronal and axial views. To preserve the total breast volume and regional tissue composition, each 2D deformation step was achieved by altering the square pixels into rectangular ones with the pixel areas unchanged and resampling with the original square pixels using bilinear interpolation. The compression was modeled by first stretching the breast in the superior-inferior direction in the coronal view. The image data were first deformed by distorting the voxels with a uniform distortion ratio. These deformed data were then deformed again using distortion ratios varying with the breast thickness and re-sampled. The deformation procedures were applied in the axial view to stretch the breast in the chest wall to nipple direction while shrinking it in the mediolateral to lateral direction re-sampled and converted into data for uniform cubic voxels. Threshold segmentation was applied to the final deformed image data to obtain the 3D compressed breast model. Our results show that the original segmented CBCT image data were successfully converted into those for a compressed breast with the same volume and regional density preserved. Using this compressed breast model, conventional and tomosynthesis mammograms were simulated for comparison with CBCT.

Motion vector field phase-to-amplitude resampling for 4D motion-compensated cone-beam CT

NASA Astrophysics Data System (ADS)

Sauppe, Sebastian; Kuhm, Julian; Brehm, Marcus; Paysan, Pascal; Seghers, Dieter; Kachelrieß, Marc

2018-02-01

We propose a phase-to-amplitude resampling (PTAR) method to reduce motion blurring in motion-compensated (MoCo) 4D cone-beam CT (CBCT) image reconstruction, without increasing the computational complexity of the motion vector field (MVF) estimation approach. PTAR is able to improve the image quality in reconstructed 4D volumes, including both regular and irregular respiration patterns. The PTAR approach starts with a robust phase-gating procedure for the initial MVF estimation and then switches to a phase-adapted amplitude gating method. The switch implies an MVF-resampling, which makes them amplitude-specific. PTAR ensures that the MVFs, which have been estimated on phase-gated reconstructions, are still valid for all amplitude-gated reconstructions. To validate the method, we use an artificially deformed clinical CT scan with a realistic breathing pattern and several patient data sets acquired with a TrueBeamTM integrated imaging system (Varian Medical Systems, Palo Alto, CA, USA). Motion blurring, which still occurs around the area of the diaphragm or at small vessels above the diaphragm in artifact-specific cyclic motion compensation (acMoCo) images based on phase-gating, is significantly reduced by PTAR. Also, small lung structures appear sharper in the images. This is demonstrated both for simulated and real patient data. A quantification of the sharpness of the diaphragm confirms these findings. PTAR improves the image quality of 4D MoCo reconstructions compared to conventional phase-gated MoCo images, in particular for irregular breathing patterns. Thus, PTAR increases the robustness of MoCo reconstructions for CBCT. Because PTAR does not require any additional steps for the MVF estimation, it is computationally efficient. Our method is not restricted to CBCT but could rather be applied to other image modalities.

0-2 Ma Paleomagnetic Field Behavior from Lava Flow Data Sets

NASA Astrophysics Data System (ADS)

Johnson, C. L.; Constable, C.; Tauxe, L.; Cromwell, G.

2010-12-01

The global time-averaged (TAF) structure of the paleomagnetic field and paleosecular variation (PSV) provide important constraints for numerical geodynamo simulations. Studies of the TAF have sought to characterize the nature of non-geocentric-axial dipole contributions to the field, in particular any such contributions that may be diagnostic of the influence of core-mantle boundary conditions on field generation. Similarly geographical variations in PSV are of interest, in particular the long-standing debate concerning anomalously low VGP (virtual geomagnetic pole) dispersion at Hawaii. Here, we analyze updated global directional data sets from lava flows. We present global models for the time-averaged field for the Brunhes and Matuyama epochs. New TAF models based on lava flow directional data for the Brunhes show longitudinal structure. In particular, high latitude flux lobes are observed, constrained by improved data sets from N. and S. America, Japan, and New Zealand. Anomalous TAF structure is also observed in the region around Hawaii. At Hawaii, previous inferences of the anomalous TAF (large inclination anomaly) and PSV (low VGP dispersion) have been argued to be the result of temporal sampling bias toward young flows. We use resampling techniques to examine possible biases in the TAF and PSV incurred by uneven temporal sampling. Resampling of the paleodirectional data onto a uniform temporal distribution, incorporating site ages and age errors leads to a TAF estimate for the Brunhes that is close to that reported for the actual data set, but an estimate for VGP dispersion that is increased relative to that obtained from the unevenly sampled data. Future investigations will incorporate the temporal resampling procedures into TAF modeling efforts, as well as recent progress in modeling the 0-2 Ma paleomagnetic dipole moment.

Rumination and depression in Chinese university students: The mediating role of overgeneral autobiographical memory

PubMed Central

Kong, Tianzhu; He, Yini; Auerbach, Randy P.; McWhinnie, Chad M.; Xiao, Jing

2015-01-01

Objective In this study, we examined the mediator effects of overgeneral autobiographical memory (OGM) on the relationship between rumination and depression in 323 Chinese university students. Method 323 undergraduates completed the questionnaires measuring OGM (Autobiographical Memory Test), rumination (Ruminative Response Scale) and depression (Center for Epidemiologic Studies Depression Scale). Results Results using structural equation modeling showed that OGM partially-mediated the relationship between rumination and depression (χ2 = 88.61, p < .01; RMSEA = .051; SRMR = .040; and CFI = .91). Bootstrap methods were used to assess the magnitude of the indirect effects. The results of the bootstrap estimation procedure and subsequent analyses indicated that the indirect effects of OGM on the relationship between rumination and depressive symptoms were significant. Conclusion The results indicated that rumination and depression were partially mediated by OGM. PMID:25977594

Standard Errors and Confidence Intervals from Bootstrapping for Ramsay-Curve Item Response Theory Model Item Parameters

ERIC Educational Resources Information Center

Gu, Fei; Skorupski, William P.; Hoyle, Larry; Kingston, Neal M.

2011-01-01

Ramsay-curve item response theory (RC-IRT) is a nonparametric procedure that estimates the latent trait using splines, and no distributional assumption about the latent trait is required. For item parameters of the two-parameter logistic (2-PL), three-parameter logistic (3-PL), and polytomous IRT models, RC-IRT can provide more accurate estimates…

A PRIM approach to predictive-signature development for patient stratification

PubMed Central

Chen, Gong; Zhong, Hua; Belousov, Anton; Devanarayan, Viswanath

2015-01-01

Patients often respond differently to a treatment because of individual heterogeneity. Failures of clinical trials can be substantially reduced if, prior to an investigational treatment, patients are stratified into responders and nonresponders based on biological or demographic characteristics. These characteristics are captured by a predictive signature. In this paper, we propose a procedure to search for predictive signatures based on the approach of patient rule induction method. Specifically, we discuss selection of a proper objective function for the search, present its algorithm, and describe a resampling scheme that can enhance search performance. Through simulations, we characterize conditions under which the procedure works well. To demonstrate practical uses of the procedure, we apply it to two real-world data sets. We also compare the results with those obtained from a recent regression-based approach, Adaptive Index Models, and discuss their respective advantages. In this study, we focus on oncology applications with survival responses. PMID:25345685

Standard errors and confidence intervals for variable importance in random forest regression, classification, and survival.

PubMed

Ishwaran, Hemant; Lu, Min

2018-06-04

Random forests are a popular nonparametric tree ensemble procedure with broad applications to data analysis. While its widespread popularity stems from its prediction performance, an equally important feature is that it provides a fully nonparametric measure of variable importance (VIMP). A current limitation of VIMP, however, is that no systematic method exists for estimating its variance. As a solution, we propose a subsampling approach that can be used to estimate the variance of VIMP and for constructing confidence intervals. The method is general enough that it can be applied to many useful settings, including regression, classification, and survival problems. Using extensive simulations, we demonstrate the effectiveness of the subsampling estimator and in particular find that the delete-d jackknife variance estimator, a close cousin, is especially effective under low subsampling rates due to its bias correction properties. These 2 estimators are highly competitive when compared with the .164 bootstrap estimator, a modified bootstrap procedure designed to deal with ties in out-of-sample data. Most importantly, subsampling is computationally fast, thus making it especially attractive for big data settings. Copyright © 2018 John Wiley & Sons, Ltd.

A resampling procedure for generating conditioned daily weather sequences

USGS Publications Warehouse

Clark, Martyn P.; Gangopadhyay, Subhrendu; Brandon, David; Werner, Kevin; Hay, Lauren E.; Rajagopalan, Balaji; Yates, David

2004-01-01

A method is introduced to generate conditioned daily precipitation and temperature time series at multiple stations. The method resamples data from the historical record “nens” times for the period of interest (nens = number of ensemble members) and reorders the ensemble members to reconstruct the observed spatial (intersite) and temporal correlation statistics. The weather generator model is applied to 2307 stations in the contiguous United States and is shown to reproduce the observed spatial correlation between neighboring stations, the observed correlation between variables (e.g., between precipitation and temperature), and the observed temporal correlation between subsequent days in the generated weather sequence. The weather generator model is extended to produce sequences of weather that are conditioned on climate indices (in this case the Niño 3.4 index). Example illustrations of conditioned weather sequences are provided for a station in Arizona (Petrified Forest, 34.8°N, 109.9°W), where El Niño and La Niña conditions have a strong effect on winter precipitation. The conditioned weather sequences generated using the methods described in this paper are appropriate for use as input to hydrologic models to produce multiseason forecasts of streamflow.

Assessing differential expression in two-color microarrays: a resampling-based empirical Bayes approach.

PubMed

Li, Dongmei; Le Pape, Marc A; Parikh, Nisha I; Chen, Will X; Dye, Timothy D

2013-01-01

Microarrays are widely used for examining differential gene expression, identifying single nucleotide polymorphisms, and detecting methylation loci. Multiple testing methods in microarray data analysis aim at controlling both Type I and Type II error rates; however, real microarray data do not always fit their distribution assumptions. Smyth's ubiquitous parametric method, for example, inadequately accommodates violations of normality assumptions, resulting in inflated Type I error rates. The Significance Analysis of Microarrays, another widely used microarray data analysis method, is based on a permutation test and is robust to non-normally distributed data; however, the Significance Analysis of Microarrays method fold change criteria are problematic, and can critically alter the conclusion of a study, as a result of compositional changes of the control data set in the analysis. We propose a novel approach, combining resampling with empirical Bayes methods: the Resampling-based empirical Bayes Methods. This approach not only reduces false discovery rates for non-normally distributed microarray data, but it is also impervious to fold change threshold since no control data set selection is needed. Through simulation studies, sensitivities, specificities, total rejections, and false discovery rates are compared across the Smyth's parametric method, the Significance Analysis of Microarrays, and the Resampling-based empirical Bayes Methods. Differences in false discovery rates controls between each approach are illustrated through a preterm delivery methylation study. The results show that the Resampling-based empirical Bayes Methods offer significantly higher specificity and lower false discovery rates compared to Smyth's parametric method when data are not normally distributed. The Resampling-based empirical Bayes Methods also offers higher statistical power than the Significance Analysis of Microarrays method when the proportion of significantly differentially expressed genes is large for both normally and non-normally distributed data. Finally, the Resampling-based empirical Bayes Methods are generalizable to next generation sequencing RNA-seq data analysis.

A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities

NASA Astrophysics Data System (ADS)

Vallières, M.; Freeman, C. R.; Skamene, S. R.; El Naqa, I.

2015-07-01

This study aims at developing a joint FDG-PET and MRI texture-based model for the early evaluation of lung metastasis risk in soft-tissue sarcomas (STSs). We investigate if the creation of new composite textures from the combination of FDG-PET and MR imaging information could better identify aggressive tumours. Towards this goal, a cohort of 51 patients with histologically proven STSs of the extremities was retrospectively evaluated. All patients had pre-treatment FDG-PET and MRI scans comprised of T1-weighted and T2-weighted fat-suppression sequences (T2FS). Nine non-texture features (SUV metrics and shape features) and forty-one texture features were extracted from the tumour region of separate (FDG-PET, T1 and T2FS) and fused (FDG-PET/T1 and FDG-PET/T2FS) scans. Volume fusion of the FDG-PET and MRI scans was implemented using the wavelet transform. The influence of six different extraction parameters on the predictive value of textures was investigated. The incorporation of features into multivariable models was performed using logistic regression. The multivariable modeling strategy involved imbalance-adjusted bootstrap resampling in the following four steps leading to final prediction model construction: (1) feature set reduction; (2) feature selection; (3) prediction performance estimation; and (4) computation of model coefficients. Univariate analysis showed that the isotropic voxel size at which texture features were extracted had the most impact on predictive value. In multivariable analysis, texture features extracted from fused scans significantly outperformed those from separate scans in terms of lung metastases prediction estimates. The best performance was obtained using a combination of four texture features extracted from FDG-PET/T1 and FDG-PET/T2FS scans. This model reached an area under the receiver-operating characteristic curve of 0.984 ± 0.002, a sensitivity of 0.955 ± 0.006, and a specificity of 0.926 ± 0.004 in bootstrapping evaluations. Ultimately, lung metastasis risk assessment at diagnosis of STSs could improve patient outcomes by allowing better treatment adaptation.

How Much Can Remotely-Sensed Natural Resource Inventories Benefit from Finer Spatial Resolutions?

NASA Astrophysics Data System (ADS)

Hou, Z.; Xu, Q.; McRoberts, R. E.; Ståhl, G.; Greenberg, J. A.

2017-12-01

For remote sensing facilitated natural resource inventories, the effects of spatial resolution in the form of pixel size and the effects of subpixel information on estimates of population parameters were evaluated by comparing results obtained using Landsat 8 and RapidEye auxiliary imagery. The study area was in Burkina Faso, and the variable of interest was the stem volume (m3/ha) convertible to the woodland aboveground biomass. A sample consisting of 160 field plots was selected and measured from the population following a two-stage sampling design. Models were fit using weighted least squares; the population mean, mu, and the variance of the estimator of the population mean, Var(mu.hat), were estimated in two inferential frameworks, model-based and model-assisted, and compared; for each framework, Var(mu.hat) was estimated both analytically and empirically. Empirical variances were estimated with bootstrapping that for resampling takes clustering effects into account. The primary results were twofold. First, for the effects of spatial resolution and subpixel information, four conclusions are relevant: (1) finer spatial resolution imagery indeed contributes to greater precision for estimators of population parameter, but this increase is slight at a maximum rate of 20% considering that RapidEye data are 36 times finer resolution than Landsat 8 data; (2) subpixel information on texture is marginally beneficial when it comes to making inference for population of large areas; (3) cost-effectiveness is more favorable for the free of charge Landsat 8 imagery than RapidEye imagery; and (4) for a given plot size, candidate remote sensing auxiliary datasets are more cost-effective when their spatial resolutions are similar to the plot size than with much finer alternatives. Second, for the comparison between estimators, three conclusions are relevant: (1) model-based variance estimates are consistent with each other and about half as large as stabilized model-assisted estimates, suggesting superior effectiveness of model-based inference to model-assisted inference; (2) bootstrapping is an effective alternative to analytical variance estimators; and (3) prediction accuracy expressed by RMSE is useful for screening candidate models to be used for population inferences.

ClonEvol: clonal ordering and visualization in cancer sequencing.

PubMed

Dang, H X; White, B S; Foltz, S M; Miller, C A; Luo, J; Fields, R C; Maher, C A

2017-12-01

Reconstruction of clonal evolution is critical for understanding tumor progression and implementing personalized therapies. This is often done by clustering somatic variants based on their cellular prevalence estimated via bulk tumor sequencing of multiple samples. The clusters, consisting of the clonal marker variants, are then ordered based on their estimated cellular prevalence to reconstruct clonal evolution trees, a process referred to as 'clonal ordering'. However, cellular prevalence estimate is confounded by statistical variability and errors in sequencing/data analysis, and therefore inhibits accurate reconstruction of the clonal evolution. This problem is further complicated by intra- and inter-tumor heterogeneity. Furthermore, the field lacks a comprehensive visualization tool to facilitate the interpretation of complex clonal relationships. To address these challenges we developed ClonEvol, a unified software tool for clonal ordering, visualization, and interpretation. ClonEvol uses a bootstrap resampling technique to estimate the cellular fraction of the clones and probabilistically models the clonal ordering constraints to account for statistical variability. The bootstrapping allows identification of the sample founding- and sub-clones, thus enabling interpretation of clonal seeding. ClonEvol automates the generation of multiple widely used visualizations for reconstructing and interpreting clonal evolution. ClonEvol outperformed three of the state of the art tools (LICHeE, Canopy and PhyloWGS) for clonal evolution inference, showing more robust error tolerance and producing more accurate trees in a simulation. Building upon multiple recent publications that utilized ClonEvol to study metastasis and drug resistance in solid cancers, here we show that ClonEvol rediscovered relapsed subclones in two published acute myeloid leukemia patients. Furthermore, we demonstrated that through noninvasive monitoring ClonEvol recapitulated the emerging subclones throughout metastatic progression observed in the tumors of a published breast cancer patient. ClonEvol has broad applicability for longitudinal monitoring of clonal populations in tumor biopsies, or noninvasively, to guide precision medicine. ClonEvol is written in R and is available at https://github.com/ChrisMaherLab/ClonEvol. © The Author 2017. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Best (but oft-forgotten) practices: the multiple problems of multiplicity-whether and how to correct for many statistical tests.

PubMed

Streiner, David L

2015-10-01

Testing many null hypotheses in a single study results in an increased probability of detecting a significant finding just by chance (the problem of multiplicity). Debates have raged over many years with regard to whether to correct for multiplicity and, if so, how it should be done. This article first discusses how multiple tests lead to an inflation of the α level, then explores the following different contexts in which multiplicity arises: testing for baseline differences in various types of studies, having >1 outcome variable, conducting statistical tests that produce >1 P value, taking multiple "peeks" at the data, and unplanned, post hoc analyses (i.e., "data dredging," "fishing expeditions," or "P-hacking"). It then discusses some of the methods that have been proposed for correcting for multiplicity, including single-step procedures (e.g., Bonferroni); multistep procedures, such as those of Holm, Hochberg, and Šidák; false discovery rate control; and resampling approaches. Note that these various approaches describe different aspects and are not necessarily mutually exclusive. For example, resampling methods could be used to control the false discovery rate or the family-wise error rate (as defined later in this article). However, the use of one of these approaches presupposes that we should correct for multiplicity, which is not universally accepted, and the article presents the arguments for and against such "correction." The final section brings together these threads and presents suggestions with regard to when it makes sense to apply the corrections and how to do so. © 2015 American Society for Nutrition.

A simple signaling rule for variable life-adjusted display derived from an equivalent risk-adjusted CUSUM chart.

PubMed

Wittenberg, Philipp; Gan, Fah Fatt; Knoth, Sven

2018-04-17

The variable life-adjusted display (VLAD) is the first risk-adjusted graphical procedure proposed in the literature for monitoring the performance of a surgeon. It displays the cumulative sum of expected minus observed deaths. It has since become highly popular because the statistic plotted is easy to understand. But it is also easy to misinterpret a surgeon's performance by utilizing the VLAD, potentially leading to grave consequences. The problem of misinterpretation is essentially caused by the variance of the VLAD's statistic that increases with sample size. In order for the VLAD to be truly useful, a simple signaling rule is desperately needed. Various forms of signaling rules have been developed, but they are usually quite complicated. Without signaling rules, making inferences using the VLAD alone is difficult if not misleading. In this paper, we establish an equivalence between a VLAD with V-mask and a risk-adjusted cumulative sum (RA-CUSUM) chart based on the difference between the estimated probability of death and surgical outcome. Average run length analysis based on simulation shows that this particular RA-CUSUM chart has similar performance as compared to the established RA-CUSUM chart based on the log-likelihood ratio statistic obtained by testing the odds ratio of death. We provide a simple design procedure for determining the V-mask parameters based on a resampling approach. Resampling from a real data set ensures that these parameters can be estimated appropriately. Finally, we illustrate the monitoring of a real surgeon's performance using VLAD with V-mask. Copyright © 2018 John Wiley & Sons, Ltd.

Measuring environmental change in forest ecosystems by repeated soil sampling: a North American perspective

USGS Publications Warehouse

Lawrence, Gregory B.; Fernandez, Ivan J.; Richter, Daniel D.; Ross, Donald S.; Hazlett, Paul W.; Bailey, Scott W.; Oiumet, Rock; Warby, Richard A.F.; Johnson, Arthur H.; Lin, Henry; Kaste, James M.; Lapenis, Andrew G.; Sullivan, Timothy J.

2013-01-01

Environmental change is monitored in North America through repeated measurements of weather, stream and river flow, air and water quality, and most recently, soil properties. Some skepticism remains, however, about whether repeated soil sampling can effectively distinguish between temporal and spatial variability, and efforts to document soil change in forest ecosystems through repeated measurements are largely nascent and uncoordinated. In eastern North America, repeated soil sampling has begun to provide valuable information on environmental problems such as air pollution. This review synthesizes the current state of the science to further the development and use of soil resampling as an integral method for recording and understanding environmental change in forested settings. The origins of soil resampling reach back to the 19th century in England and Russia. The concepts and methodologies involved in forest soil resampling are reviewed and evaluated through a discussion of how temporal and spatial variability can be addressed with a variety of sampling approaches. Key resampling studies demonstrate the type of results that can be obtained through differing approaches. Ongoing, large-scale issues such as recovery from acidification, long-term N deposition, C sequestration, effects of climate change, impacts from invasive species, and the increasing intensification of soil management all warrant the use of soil resampling as an essential tool for environmental monitoring and assessment. Furthermore, with better awareness of the value of soil resampling, studies can be designed with a long-term perspective so that information can be efficiently obtained well into the future to address problems that have not yet surfaced.

Revisiting the southern pine growth decline: Where are we 10 years later?

Treesearch

Gary L. Gadbury; Michael S. Williams; Hans T. Schreuder

2004-01-01

This paper evaluates changes in growth of pine stands in the state of Georgia, U.S.A., using USDA Forest Service Forest Inventory and Analysis (FIA) data. In particular, data representing an additional 10-year growth cy-cle has been added to previously published results from two earlier growth cycles. A robust regression procedure is combined with a bootstrap technique...

The Relationships among Students' Future-Oriented Goals and Subgoals, Perceived Task Instrumentality, and Task-Oriented Self-Regulation Strategies in an Academic Environment

ERIC Educational Resources Information Center

Tabachnick, Sharon E.; Miller, Raymond B.; Relyea, George E.

2008-01-01

The authors performed path analysis, followed by a bootstrap procedure, to test the predictions of a model explaining the relationships among students' distal future goals (both extrinsic and intrinsic), their adoption of a middle-range subgoal, their perceptions of task instrumentality, and their proximal task-oriented self-regulation strategies.…

Bearing fault diagnosis under unknown time-varying rotational speed conditions via multiple time-frequency curve extraction

NASA Astrophysics Data System (ADS)

Huang, Huan; Baddour, Natalie; Liang, Ming

2018-02-01

Under normal operating conditions, bearings often run under time-varying rotational speed conditions. Under such circumstances, the bearing vibrational signal is non-stationary, which renders ineffective the techniques used for bearing fault diagnosis under constant running conditions. One of the conventional methods of bearing fault diagnosis under time-varying speed conditions is resampling the non-stationary signal to a stationary signal via order tracking with the measured variable speed. With the resampled signal, the methods available for constant condition cases are thus applicable. However, the accuracy of the order tracking is often inadequate and the time-varying speed is sometimes not measurable. Thus, resampling-free methods are of interest for bearing fault diagnosis under time-varying rotational speed for use without tachometers. With the development of time-frequency analysis, the time-varying fault character manifests as curves in the time-frequency domain. By extracting the Instantaneous Fault Characteristic Frequency (IFCF) from the Time-Frequency Representation (TFR) and converting the IFCF, its harmonics, and the Instantaneous Shaft Rotational Frequency (ISRF) into straight lines, the bearing fault can be detected and diagnosed without resampling. However, so far, the extraction of the IFCF for bearing fault diagnosis is mostly based on the assumption that at each moment the IFCF has the highest amplitude in the TFR, which is not always true. Hence, a more reliable T-F curve extraction approach should be investigated. Moreover, if the T-F curves including the IFCF, its harmonic, and the ISRF can be all extracted from the TFR directly, no extra processing is needed for fault diagnosis. Therefore, this paper proposes an algorithm for multiple T-F curve extraction from the TFR based on a fast path optimization which is more reliable for T-F curve extraction. Then, a new procedure for bearing fault diagnosis under unknown time-varying speed conditions is developed based on the proposed algorithm and a new fault diagnosis strategy. The average curve-to-curve ratios are utilized to describe the relationship of the extracted curves and fault diagnosis can then be achieved by comparing the ratios to the fault characteristic coefficients. The effectiveness of the proposed method is validated by simulated and experimental signals.

Temporal rainfall disaggregation using a multiplicative cascade model for spatial application in urban hydrology

NASA Astrophysics Data System (ADS)

Müller, H.; Haberlandt, U.

2018-01-01

Rainfall time series of high temporal resolution and spatial density are crucial for urban hydrology. The multiplicative random cascade model can be used for temporal rainfall disaggregation of daily data to generate such time series. Here, the uniform splitting approach with a branching number of 3 in the first disaggregation step is applied. To achieve a final resolution of 5 min, subsequent steps after disaggregation are necessary. Three modifications at different disaggregation levels are tested in this investigation (uniform splitting at Δt = 15 min, linear interpolation at Δt = 7.5 min and Δt = 3.75 min). Results are compared both with observations and an often used approach, based on the assumption that a time steps with Δt = 5.625 min, as resulting if a branching number of 2 is applied throughout, can be replaced with Δt = 5 min (called the 1280 min approach). Spatial consistence is implemented in the disaggregated time series using a resampling algorithm. In total, 24 recording stations in Lower Saxony, Northern Germany with a 5 min resolution have been used for the validation of the disaggregation procedure. The urban-hydrological suitability is tested with an artificial combined sewer system of about 170 hectares. The results show that all three variations outperform the 1280 min approach regarding reproduction of wet spell duration, average intensity, fraction of dry intervals and lag-1 autocorrelation. Extreme values with durations of 5 min are also better represented. For durations of 1 h, all approaches show only slight deviations from the observed extremes. The applied resampling algorithm is capable to achieve sufficient spatial consistence. The effects on the urban hydrological simulations are significant. Without spatial consistence, flood volumes of manholes and combined sewer overflow are strongly underestimated. After resampling, results using disaggregated time series as input are in the range of those using observed time series. Best overall performance regarding rainfall statistics are obtained by the method in which the disaggregation process ends at time steps with 7.5 min duration, deriving the 5 min time steps by linear interpolation. With subsequent resampling this method leads to a good representation of manhole flooding and combined sewer overflow volume in terms of hydrological simulations and outperforms the 1280 min approach.

Low Computational Signal Acquisition for GNSS Receivers Using a Resampling Strategy and Variable Circular Correlation Time

PubMed Central

Zhang, Yeqing; Wang, Meiling; Li, Yafeng

2018-01-01

For the objective of essentially decreasing computational complexity and time consumption of signal acquisition, this paper explores a resampling strategy and variable circular correlation time strategy specific to broadband multi-frequency GNSS receivers. In broadband GNSS receivers, the resampling strategy is established to work on conventional acquisition algorithms by resampling the main lobe of received broadband signals with a much lower frequency. Variable circular correlation time is designed to adapt to different signal strength conditions and thereby increase the operation flexibility of GNSS signal acquisition. The acquisition threshold is defined as the ratio of the highest and second highest correlation results in the search space of carrier frequency and code phase. Moreover, computational complexity of signal acquisition is formulated by amounts of multiplication and summation operations in the acquisition process. Comparative experiments and performance analysis are conducted on four sets of real GPS L2C signals with different sampling frequencies. The results indicate that the resampling strategy can effectively decrease computation and time cost by nearly 90–94% with just slight loss of acquisition sensitivity. With circular correlation time varying from 10 ms to 20 ms, the time cost of signal acquisition has increased by about 2.7–5.6% per millisecond, with most satellites acquired successfully. PMID:29495301

Low Computational Signal Acquisition for GNSS Receivers Using a Resampling Strategy and Variable Circular Correlation Time.

PubMed

Zhang, Yeqing; Wang, Meiling; Li, Yafeng

2018-02-24

For the objective of essentially decreasing computational complexity and time consumption of signal acquisition, this paper explores a resampling strategy and variable circular correlation time strategy specific to broadband multi-frequency GNSS receivers. In broadband GNSS receivers, the resampling strategy is established to work on conventional acquisition algorithms by resampling the main lobe of received broadband signals with a much lower frequency. Variable circular correlation time is designed to adapt to different signal strength conditions and thereby increase the operation flexibility of GNSS signal acquisition. The acquisition threshold is defined as the ratio of the highest and second highest correlation results in the search space of carrier frequency and code phase. Moreover, computational complexity of signal acquisition is formulated by amounts of multiplication and summation operations in the acquisition process. Comparative experiments and performance analysis are conducted on four sets of real GPS L2C signals with different sampling frequencies. The results indicate that the resampling strategy can effectively decrease computation and time cost by nearly 90-94% with just slight loss of acquisition sensitivity. With circular correlation time varying from 10 ms to 20 ms, the time cost of signal acquisition has increased by about 2.7-5.6% per millisecond, with most satellites acquired successfully.

Graphing within-subjects confidence intervals using SPSS and S-Plus.

PubMed

Wright, Daniel B

2007-02-01

Within-subjects confidence intervals are often appropriate to report and to display. Loftus and Masson (1994) have reported methods to calculate these, and their use is becoming common. In the present article, procedures for calculating within-subjects confidence intervals in SPSS and S-Plus are presented (an R version is on the accompanying Web site). The procedure in S-Plus allows the user to report the bias corrected and adjusted bootstrap confidence intervals as well as the standard confidence intervals based on traditional methods. The presented code can be easily altered to fit the individual user's needs.

Factors that affect willingness to donate blood for the purpose of biospecimen research in the Korean American community.

PubMed

Yen, Glorian P; Davey, Adam; Ma, Grace X

2015-04-01

Biorepositories have been key resources in examining genetically-linked diseases, particularly cancer. Asian Americans contribute to biorepositories at lower rates than other racial groups, but the reasons for this are unclear. We hypothesized that attitudes toward biospecimen research mediate the relationship between demographic and healthcare access factors, and willingness to donate blood for research purposes among individuals of Korean heritage. Descriptive statistics and bivariate analyses were utilized to characterize the sample with respect to demographic, psychosocial, and behavioral variables. Structural equation modeling with 5000 re-sample bootstrapping was used to assess each component of the proposed simple mediation models. Attitudes towards biospecimen research fully mediate associations between age, income, number of years lived in the United States, and having a regular physician and willingness to donate blood for the purpose of research. Participants were willing to donate blood for the purpose of research despite having neutral feelings towards biospecimen research as a whole. Participants reported higher willingness to donate blood for research purposes when they were older, had lived in the United States longer, had higher income, and had a regular doctor that they visited. Many of the significant relationships between demographic and health care access factors, attitudes towards biospecimen research, and willingness to donate blood for the purpose of research may be explained by the extent of acculturation of the participants in the United States.

The relationship between academic self-concept, intrinsic motivation, test anxiety, and academic achievement among nursing students: mediating and moderating effects.

PubMed

Khalaila, Rabia

2015-03-01

The impact of cognitive factors on academic achievement is well documented. However, little is known about the mediating and moderating effects of non-cognitive, motivational and situational factors on academic achievement among nursing students. The aim of this study is to explore the direct and/or indirect effects of academic self-concept on academic achievement, and examine whether intrinsic motivation moderates the negative effect of test anxiety on academic achievement. This descriptive-correlational study was carried out on a convenience sample of 170 undergraduate nursing students, in an academic college in northern Israel. Academic motivation, academic self-concept and test anxiety scales were used as measuring instruments. Bootstrapping with resampling strategies was used for testing multiple mediators' model and examining the moderator effect. A higher self-concept was found to be directly related to greater academic achievement. Test anxiety and intrinsic motivation were found to be significant mediators in the relationship between self-concept and academic achievement. In addition, intrinsic motivation significantly moderated the negative effect of test anxiety on academic achievement. The results suggested that institutions should pay more attention to the enhancement of motivational factors (e.g., self-concept and motivation) and alleviate the negative impact of situational factors (e.g., test anxiety) when offering psycho-educational interventions designed to improve nursing students' academic achievements. Copyright © 2014 Elsevier Ltd. All rights reserved.

Computing camera heading: A study

NASA Astrophysics Data System (ADS)

Zhang, John Jiaxiang

2000-08-01

An accurate estimate of the motion of a camera is a crucial first step for the 3D reconstruction of sites, objects, and buildings from video. Solutions to the camera heading problem can be readily applied to many areas, such as robotic navigation, surgical operation, video special effects, multimedia, and lately even in internet commerce. From image sequences of a real world scene, the problem is to calculate the directions of the camera translations. The presence of rotations makes this problem very hard. This is because rotations and translations can have similar effects on the images, and are thus hard to tell apart. However, the visual angles between the projection rays of point pairs are unaffected by rotations, and their changes over time contain sufficient information to determine the direction of camera translation. We developed a new formulation of the visual angle disparity approach, first introduced by Tomasi, to the camera heading problem. Our new derivation makes theoretical analysis possible. Most notably, a theorem is obtained that locates all possible singularities of the residual function for the underlying optimization problem. This allows identifying all computation trouble spots beforehand, and to design reliable and accurate computational optimization methods. A bootstrap-jackknife resampling method simultaneously reduces complexity and tolerates outliers well. Experiments with image sequences show accurate results when compared with the true camera motion as measured with mechanical devices.

Comparing two correlated C indices with right-censored survival outcome: a one-shot nonparametric approach

PubMed Central

Kang, Le; Chen, Weijie; Petrick, Nicholas A.; Gallas, Brandon D.

2014-01-01

The area under the receiver operating characteristic (ROC) curve (AUC) is often used as a summary index of the diagnostic ability in evaluating biomarkers when the clinical outcome (truth) is binary. When the clinical outcome is right-censored survival time, the C index, motivated as an extension of AUC, has been proposed by Harrell as a measure of concordance between a predictive biomarker and the right-censored survival outcome. In this work, we investigate methods for statistical comparison of two diagnostic or predictive systems, of which they could either be two biomarkers or two fixed algorithms, in terms of their C indices. We adopt a U-statistics based C estimator that is asymptotically normal and develop a nonparametric analytical approach to estimate the variance of the C estimator and the covariance of two C estimators. A z-score test is then constructed to compare the two C indices. We validate our one-shot nonparametric method via simulation studies in terms of the type I error rate and power. We also compare our one-shot method with resampling methods including the jackknife and the bootstrap. Simulation results show that the proposed one-shot method provides almost unbiased variance estimations and has satisfactory type I error control and power. Finally, we illustrate the use of the proposed method with an example from the Framingham Heart Study. PMID:25399736

Dose response explorer: an integrated open-source tool for exploring and modelling radiotherapy dose volume outcome relationships

NASA Astrophysics Data System (ADS)

El Naqa, I.; Suneja, G.; Lindsay, P. E.; Hope, A. J.; Alaly, J. R.; Vicic, M.; Bradley, J. D.; Apte, A.; Deasy, J. O.

2006-11-01

Radiotherapy treatment outcome models are a complicated function of treatment, clinical and biological factors. Our objective is to provide clinicians and scientists with an accurate, flexible and user-friendly software tool to explore radiotherapy outcomes data and build statistical tumour control or normal tissue complications models. The software tool, called the dose response explorer system (DREES), is based on Matlab, and uses a named-field structure array data type. DREES/Matlab in combination with another open-source tool (CERR) provides an environment for analysing treatment outcomes. DREES provides many radiotherapy outcome modelling features, including (1) fitting of analytical normal tissue complication probability (NTCP) and tumour control probability (TCP) models, (2) combined modelling of multiple dose-volume variables (e.g., mean dose, max dose, etc) and clinical factors (age, gender, stage, etc) using multi-term regression modelling, (3) manual or automated selection of logistic or actuarial model variables using bootstrap statistical resampling, (4) estimation of uncertainty in model parameters, (5) performance assessment of univariate and multivariate analyses using Spearman's rank correlation and chi-square statistics, boxplots, nomograms, Kaplan-Meier survival plots, and receiver operating characteristics curves, and (6) graphical capabilities to visualize NTCP or TCP prediction versus selected variable models using various plots. DREES provides clinical researchers with a tool customized for radiotherapy outcome modelling. DREES is freely distributed. We expect to continue developing DREES based on user feedback.

Evaluation of dynamically downscaled extreme temperature using a spatially-aggregated generalized extreme value (GEV) model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Jiali; Han, Yuefeng; Stein, Michael L.

2016-02-10

The Weather Research and Forecast (WRF) model downscaling skill in extreme maximum daily temperature is evaluated by using the generalized extreme value (GEV) distribution. While the GEV distribution has been used extensively in climatology and meteorology for estimating probabilities of extreme events, accurately estimating GEV parameters based on data from a single pixel can be difficult, even with fairly long data records. This work proposes a simple method assuming that the shape parameter, the most difficult of the three parameters to estimate, does not vary over a relatively large region. This approach is applied to evaluate 31-year WRF-downscaled extreme maximummore » temperature through comparison with North American Regional Reanalysis (NARR) data. Uncertainty in GEV parameter estimates and the statistical significance in the differences of estimates between WRF and NARR are accounted for by conducting bootstrap resampling. Despite certain biases over parts of the United States, overall, WRF shows good agreement with NARR in the spatial pattern and magnitudes of GEV parameter estimates. Both WRF and NARR show a significant increase in extreme maximum temperature over the southern Great Plains and southeastern United States in January and over the western United States in July. The GEV model shows clear benefits from the regionally constant shape parameter assumption, for example, leading to estimates of the location and scale parameters of the model that show coherent spatial patterns.« less

Personalized cumulative UV tracking on mobiles & wearables.

PubMed

Dey, S; Sahoo, S; Agrawal, H; Mondal, A; Bhowmik, T; Tiwari, V N

2017-07-01

Maintaining a balanced Ultra Violet (UV) exposure level is vital for a healthy living as the excess of UV dose can lead to critical diseases such as skin cancer while the absence can cause vitamin D deficiency which has recently been linked to onset of cardiac abnormalities. Here, we propose a personalized cumulative UV dose (CUVD) estimation system for smartwatch and smartphone devices having the following novelty factors; (a) sensor orientation invariant measurement of UV exposure using a bootstrap resampling technique, (b) estimation of UV exposure using only light intensity (lux) sensor (c) optimal UV exposure dose estimation. Our proposed method will eliminate the need for a dedicated UV sensor thus widen the user base of the proposed solution, render it unobtrusive by eliminating the critical requirement of orienting the device in a direction facing the sun. The system is implemented on android mobile platform and validated on 1200 minutes of lux and UV index (UVI) data collected across several days covering morning to evening time frames. The result shows very impressive final UVI estimation accuracy. We believe our proposed solution will enable the future wearable and smartphone users to obtain a seamless personalized UV exposure dose across a day paving a way for simple yet very useful recommendations such as right skin protective measure for reducing risk factors of long term UV exposure related diseases like skin cancer and, cardiac abnormality.

Plate tectonics and continental basaltic geochemistry throughout Earth history

NASA Astrophysics Data System (ADS)

Keller, Brenhin; Schoene, Blair

2018-01-01

Basaltic magmas constitute the primary mass flux from Earth's mantle to its crust, carrying information about the conditions of mantle melting through which they were generated. As such, changes in the average basaltic geochemistry through time reflect changes in underlying parameters such as mantle potential temperature and the geodynamic setting of mantle melting. However, sampling bias, preservation bias, and geological heterogeneity complicate the calculation of representative average compositions. Here we use weighted bootstrap resampling to minimize sampling bias over the heterogeneous rock record and obtain maximally representative average basaltic compositions through time. Over the approximately 4 Ga of the continental rock record, the average composition of preserved continental basalts has evolved along a generally continuous trajectory, with decreasing compatible element concentrations and increasing incompatible element concentrations, punctuated by a comparatively rapid transition in some variables such as La/Yb ratios and Zr, Nb, and Ti abundances approximately 2.5 Ga ago. Geochemical modeling of mantle melting systematics and trace element partitioning suggests that these observations can be explained by discontinuous changes in the mineralogy of mantle partial melting driven by a gradual decrease in mantle potential temperature, without appealing to any change in tectonic process. This interpretation is supported by the geochemical record of slab fluid input to continental basalts, which indicates no long-term change in the global proportion of arc versus non-arc basaltic magmatism at any time in the preserved rock record.

Gaetbulicola byunsanensis gen. nov., sp. nov., isolated from tidal flat sediment.

PubMed

Yoon, Jung-Hoon; Kang, So-Jung; Jung, Yong-Taek; Oh, Tae-Kwang

2010-01-01

A Gram-negative, non-motile and pleomorphic bacterial strain, SMK-114(T), which belongs to the class Alphaproteobacteria, was isolated from a tidal flat sample collected in Byunsan, Korea. Strain SMK-114(T) grew optimally at pH 7.0-8.0 and 25-30 degrees C and in the presence of 2 % (w/v) NaCl. A neighbour-joining phylogenetic tree based on 16S rRNA gene sequences showed that strain SMK-114(T) formed a cluster with Octadecabacter species, with which it exhibited 16S rRNA gene sequence similarity values of 95.2-95.4 %. This cluster was part of the clade comprising Thalassobius species with a bootstrap resampling value of 76.3 %. Strain SMK-114(T) exhibited 16S rRNA gene sequence similarity values of 95.1-96.3 % to members of the genus Thalassobius. It contained Q-10 as the predominant ubiquinone and C(18 : 1)omega7c as the major fatty acid. The DNA G+C content was 60.0 mol%. On the basis of phenotypic, chemotaxonomic and phylogenetic data, strain SMK-114(T) is considered to represent a novel species in a new genus for which the name Gaetbulicola byunsanensis gen. nov., sp. nov. is proposed. The type strain of Gaetbulicola byunsanensis is SMK-114(T) (=KCTC 22632(T) =CCUG 57612(T)).

EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis.

PubMed

Delorme, Arnaud; Makeig, Scott

2004-03-15

We have developed a toolbox and graphic user interface, EEGLAB, running under the crossplatform MATLAB environment (The Mathworks, Inc.) for processing collections of single-trial and/or averaged EEG data of any number of channels. Available functions include EEG data, channel and event information importing, data visualization (scrolling, scalp map and dipole model plotting, plus multi-trial ERP-image plots), preprocessing (including artifact rejection, filtering, epoch selection, and averaging), independent component analysis (ICA) and time/frequency decompositions including channel and component cross-coherence supported by bootstrap statistical methods based on data resampling. EEGLAB functions are organized into three layers. Top-layer functions allow users to interact with the data through the graphic interface without needing to use MATLAB syntax. Menu options allow users to tune the behavior of EEGLAB to available memory. Middle-layer functions allow users to customize data processing using command history and interactive 'pop' functions. Experienced MATLAB users can use EEGLAB data structures and stand-alone signal processing functions to write custom and/or batch analysis scripts. Extensive function help and tutorial information are included. A 'plug-in' facility allows easy incorporation of new EEG modules into the main menu. EEGLAB is freely available (http://www.sccn.ucsd.edu/eeglab/) under the GNU public license for noncommercial use and open source development, together with sample data, user tutorial and extensive documentation.

Evaluating the efficiency of environmental monitoring programs

USGS Publications Warehouse

Levine, Carrie R.; Yanai, Ruth D.; Lampman, Gregory G.; Burns, Douglas A.; Driscoll, Charles T.; Lawrence, Gregory B.; Lynch, Jason; Schoch, Nina

2014-01-01

Statistical uncertainty analyses can be used to improve the efficiency of environmental monitoring, allowing sampling designs to maximize information gained relative to resources required for data collection and analysis. In this paper, we illustrate four methods of data analysis appropriate to four types of environmental monitoring designs. To analyze a long-term record from a single site, we applied a general linear model to weekly stream chemistry data at Biscuit Brook, NY, to simulate the effects of reducing sampling effort and to evaluate statistical confidence in the detection of change over time. To illustrate a detectable difference analysis, we analyzed a one-time survey of mercury concentrations in loon tissues in lakes in the Adirondack Park, NY, demonstrating the effects of sampling intensity on statistical power and the selection of a resampling interval. To illustrate a bootstrapping method, we analyzed the plot-level sampling intensity of forest inventory at the Hubbard Brook Experimental Forest, NH, to quantify the sampling regime needed to achieve a desired confidence interval. Finally, to analyze time-series data from multiple sites, we assessed the number of lakes and the number of samples per year needed to monitor change over time in Adirondack lake chemistry using a repeated-measures mixed-effects model. Evaluations of time series and synoptic long-term monitoring data can help determine whether sampling should be re-allocated in space or time to optimize the use of financial and human resources.

Applying causal mediation analysis to personality disorder research.

PubMed

Walters, Glenn D

2018-01-01

This article is designed to address fundamental issues in the application of causal mediation analysis to research on personality disorders. Causal mediation analysis is used to identify mechanisms of effect by testing variables as putative links between the independent and dependent variables. As such, it would appear to have relevance to personality disorder research. It is argued that proper implementation of causal mediation analysis requires that investigators take several factors into account. These factors are discussed under 5 headings: variable selection, model specification, significance evaluation, effect size estimation, and sensitivity testing. First, care must be taken when selecting the independent, dependent, mediator, and control variables for a mediation analysis. Some variables make better mediators than others and all variables should be based on reasonably reliable indicators. Second, the mediation model needs to be properly specified. This requires that the data for the analysis be prospectively or historically ordered and possess proper causal direction. Third, it is imperative that the significance of the identified pathways be established, preferably with a nonparametric bootstrap resampling approach. Fourth, effect size estimates should be computed or competing pathways compared. Finally, investigators employing the mediation method are advised to perform a sensitivity analysis. Additional topics covered in this article include parallel and serial multiple mediation designs, moderation, and the relationship between mediation and moderation. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Regional intensity attenuation models for France and the estimation of magnitude and location of historical earthquakes

USGS Publications Warehouse

Bakun, W.H.; Scotti, O.

2006-01-01

Intensity assignments for 33 calibration earthquakes were used to develop intensity attenuation models for the Alps, Armorican, Provence, Pyrenees and Rhine regions of France. Intensity decreases with ?? most rapidly in the French Alps, Provence and Pyrenees regions, and least rapidly in the Armorican and Rhine regions. The comparable Armorican and Rhine region attenuation models are aggregated into a French stable continental region model and the comparable Provence and Pyrenees region models are aggregated into a Southern France model. We analyse MSK intensity assignments using the technique of Bakun & Wentworth, which provides an objective method for estimating epicentral location and intensity magnitude MI. MI for the 1356 October 18 earthquake in the French stable continental region is 6.6 for a location near Basle, Switzerland, and moment magnitude M is 5.9-7.2 at the 95 per cent (??2??) confidence level. MI for the 1909 June 11 Trevaresse (Lambesc) earthquake near Marseilles in the Southern France region is 5.5, and M is 4.9-6.0 at the 95 per cent confidence level. Bootstrap resampling techniques are used to calculate objective, reproducible 67 per cent and 95 per cent confidence regions for the locations of historical earthquakes. These confidence regions for location provide an attractive alternative to the macroseismic epicentre and qualitative location uncertainties used heretofore. ?? 2006 The Authors Journal compilation ?? 2006 RAS.

Examining mediators of child sexual abuse and sexually transmitted infections.

PubMed

Sutherland, Melissa A

2011-01-01

Interpersonal violence has increasingly been identified as a risk factor for sexually transmitted infections. Understanding the pathways between violence and sexually transmitted infections is essential to designing effective interventions. The aim of this study was to examine dissociative symptoms, alcohol use, and intimate partner physical violence and sexual coercion as mediators of child sexual abuse and lifetime sexually transmitted infection diagnosis among a sample of women. A convenience sample of 202 women was recruited from healthcare settings, with 189 complete cases for analysis. A multiple mediation model tested the proposed mediators of child sexual abuse and lifetime sexually transmitted infection diagnosis. Bootstrapping, a resampling method, was used to test for mediation. Key variables included child sexual abuse, dissociative symptoms, alcohol use, and intimate partner violence. Child sexual abuse was reported by 46% of the study participants (n = 93). Child sexual abuse was found to have an indirect effect on lifetime sexually transmitted infection diagnosis, with the effect occurring through dissociative symptoms (95% confidence interval = 0.0033-0.4714) and sexual coercion (95% confidence interval = 0.0359-0.7694). Alcohol use and physical violence were not found to be significant mediators. This study suggests that dissociation and intimate partner sexual coercion are important mediators of child sexual abuse and sexually transmitted infection diagnosis. Therefore, interventions that consider the roles of dissociative symptoms and interpersonal violence may be effective in preventing sexually transmitted infections among women.

Performance evaluation of dispersion parameterization schemes in the plume simulation of FFT-07 diffusion experiment

NASA Astrophysics Data System (ADS)

Pandey, Gavendra; Sharan, Maithili

2018-01-01

Application of atmospheric dispersion models in air quality analysis requires a proper representation of the vertical and horizontal growth of the plume. For this purpose, various schemes for the parameterization of dispersion parameters σ‧s are described in both stable and unstable conditions. These schemes differ on the use of (i) extent of availability of on-site measurements (ii) formulations developed for other sites and (iii) empirical relations. The performance of these schemes is evaluated in an earlier developed IIT (Indian Institute of Technology) dispersion model with the data set in single and multiple releases conducted at Fusion Field Trials, Dugway Proving Ground, Utah 2007. Qualitative and quantitative evaluation of the relative performance of all the schemes is carried out in both stable and unstable conditions in the light of (i) peak/maximum concentrations, and (ii) overall concentration distribution. The blocked bootstrap resampling technique is adopted to investigate the statistical significance of the differences in performances of each of the schemes by computing 95% confidence limits on the parameters FB and NMSE. The various analysis based on some selected statistical measures indicated consistency in the qualitative and quantitative performances of σ schemes. The scheme which is based on standard deviation of wind velocity fluctuations and Lagrangian time scales exhibits a relatively better performance in predicting the peak as well as the lateral spread.

Cost analysis of Omega-3 supplementation in critically ill patients with sepsis.

PubMed

Kyeremanteng, Kwadwo; Shen, Jennifer; Thavorn, Kednapa; Fernando, Shannon M; Herritt, Brent; Chaudhuri, Dipayan; Tanuseputro, Peter

2018-06-01

Nutritional supplement of omega-3 fatty acids have been proposed to improve clinical outcomes in critically ill patients. While previous work have demonstrated that omega-3 supplementation in patients with sepsis is associated with reduced ICU and hospital length of stay, the financial impact of this intervention is unknown. Perform a cost analysis to evaluate the impact of omega-3 supplementation on ICU and hospital costs. We extracted data related to ICU and hospital length of stay from the individual studies reported in a recent systematic review. The Cochrane Collaboration tool was used to assess the risk of bias in these studies. Average daily ICU and hospital costs per patient were obtained from a cost study by Kahn et al. We estimated the ICU and hospital costs by multiplying the mean length of stay by the average daily cost per patient in ICU or Hospital. Adjustments for inflation were made according to the USD annual consumer price index. We calculated the difference between the direct variable cost of patients with omega-3 supplementation and patients without omega-3 supplementation. 95% confidence intervals were estimated using bootstrap re-sampling procedures with 1000 iterations. A total of 12 RCT involving 925 patients were included in this cost analysis. Septic patients supplemented with omega-3 had both lower mean ICU costs ($15,274 vs. $18,172) resulting in $2897 in ICU savings per patient and overall hospital costs ($17,088 vs. $19,778), resulting in $2690 in hospital savings per patient. Sensitivity analyses were conducted to investigate the impact of different study methods on the LOS. The results were still consistent with the overall findings. Patients with sepsis who received omega-3 supplementation had significantly shorter LOS in the ICU and hospital, and were associated with lower direct variable costs than control patients. The 12 RCTs used in this analysis had a high risk of bias. Large-scaled, high-quality, multi-centered RCTs on the effectiveness of this intervention is recommended to improve the quality of the existing evidence. Copyright © 2018 European Society for Clinical Nutrition and Metabolism. Published by Elsevier Ltd. All rights reserved.

Nonexercise Equations to Estimate Fitness in White European and South Asian Men.

PubMed

O'Donovan, Gary; Bakrania, Kishan; Ghouri, Nazim; Yates, Thomas; Gray, Laura J; Hamer, Mark; Stamatakis, Emmanuel; Khunti, Kamlesh; Davies, Melanie; Sattar, Naveed; Gill, Jason M R

2016-05-01

Cardiorespiratory fitness is a strong, independent predictor of health, whether it is measured in an exercise test or estimated in an equation. The purpose of this study was to develop and validate equations to estimate fitness in middle-age white European and South Asian men. Multiple linear regression models (n = 168, including 83 white European and 85 South Asian men) were created using variables that are thought to be important in predicting fitness (V˙O2max, mL·kg⁻¹·min⁻¹): age (yr), body mass index (kg·m⁻²), resting HR (bpm); smoking status (0, never smoked; 1, ex or current smoker), physical activity expressed as quintiles (0, quintile 1; 1, quintile 2; 2, quintile 3; 3, quintile 4; 4, quintile 5), categories of moderate- to-vigorous intensity physical activity (MVPA) (0, <75 min·wk⁻¹; 1, 75-150 min·wk⁻¹; 2, >150-225 min·wk⁻¹; 3, >225-300 min·wk⁻¹; 4, >300 min·wk⁻¹), or minutes of MVPA (min·wk⁻¹); and, ethnicity (0, South Asian; 1, white). The leave-one-out cross-validation procedure was used to assess the generalizability, and the bootstrap and jackknife resampling techniques were used to estimate the variance and bias of the models. Around 70% of the variance in fitness was explained in models with an ethnicity variable, such as: V˙O2max = 77.409 - (age × 0.374) - (body mass index × 0.906) - (ex or current smoker × 1.976) + (physical activity quintile coefficient) - (resting HR × 0.066) + (white ethnicity × 8.032), where physical activity quintile 1 is 0, 2 is 1.127, 3 is 1.869, 4 is 3.793, and 5 is 3.029. Only around 50% of the variance was explained in models without an ethnicity variable. All models with an ethnicity variable were generalizable and had low variance and bias. These data demonstrate the importance of incorporating ethnicity in nonexercise equations to estimate cardiorespiratory fitness in multiethnic populations.

A comparison of bootstrap methods and an adjusted bootstrap approach for estimating the prediction error in microarray classification.

PubMed

Jiang, Wenyu; Simon, Richard

2007-12-20

This paper first provides a critical review on some existing methods for estimating the prediction error in classifying microarray data where the number of genes greatly exceeds the number of specimens. Special attention is given to the bootstrap-related methods. When the sample size n is small, we find that all the reviewed methods suffer from either substantial bias or variability. We introduce a repeated leave-one-out bootstrap (RLOOB) method that predicts for each specimen in the sample using bootstrap learning sets of size ln. We then propose an adjusted bootstrap (ABS) method that fits a learning curve to the RLOOB estimates calculated with different bootstrap learning set sizes. The ABS method is robust across the situations we investigate and provides a slightly conservative estimate for the prediction error. Even with small samples, it does not suffer from large upward bias as the leave-one-out bootstrap and the 0.632+ bootstrap, and it does not suffer from large variability as the leave-one-out cross-validation in microarray applications. Copyright (c) 2007 John Wiley & Sons, Ltd.

The conditional resampling model STARS: weaknesses of the modeling concept and development

NASA Astrophysics Data System (ADS)

Menz, Christoph

2016-04-01

The Statistical Analogue Resampling Scheme (STARS) is based on a modeling concept of Werner and Gerstengarbe (1997). The model uses a conditional resampling technique to create a simulation time series from daily observations. Unlike other time series generators (such as stochastic weather generators) STARS only needs a linear regression specification of a single variable as the target condition for the resampling. Since its first implementation the algorithm was further extended in order to allow for a spatially distributed trend signal, to preserve the seasonal cycle and the autocorrelation of the observation time series (Orlovsky, 2007; Orlovsky et al., 2008). This evolved version was successfully used in several climate impact studies. However a detaild evaluation of the simulations revealed two fundamental weaknesses of the utilized resampling technique. 1. The restriction of the resampling condition on a single individual variable can lead to a misinterpretation of the change signal of other variables when the model is applied to a mulvariate time series. (F. Wechsung and M. Wechsung, 2014). As one example, the short-term correlations between precipitation and temperature (cooling of the near-surface air layer after a rainfall event) can be misinterpreted as a climatic change signal in the simulation series. 2. The model restricts the linear regression specification to the annual mean time series, refusing the specification of seasonal varying trends. To overcome these fundamental weaknesses a redevelopment of the whole algorithm was done. The poster discusses the main weaknesses of the earlier model implementation and the methods applied to overcome these in the new version. Based on the new model idealized simulations were conducted to illustrate the enhancement.

Resampling-based Methods in Single and Multiple Testing for Equality of Covariance/Correlation Matrices

PubMed Central

Yang, Yang; DeGruttola, Victor

2016-01-01

Traditional resampling-based tests for homogeneity in covariance matrices across multiple groups resample residuals, that is, data centered by group means. These residuals do not share the same second moments when the null hypothesis is false, which makes them difficult to use in the setting of multiple testing. An alternative approach is to resample standardized residuals, data centered by group sample means and standardized by group sample covariance matrices. This approach, however, has been observed to inflate type I error when sample size is small or data are generated from heavy-tailed distributions. We propose to improve this approach by using robust estimation for the first and second moments. We discuss two statistics: the Bartlett statistic and a statistic based on eigen-decomposition of sample covariance matrices. Both statistics can be expressed in terms of standardized errors under the null hypothesis. These methods are extended to test homogeneity in correlation matrices. Using simulation studies, we demonstrate that the robust resampling approach provides comparable or superior performance, relative to traditional approaches, for single testing and reasonable performance for multiple testing. The proposed methods are applied to data collected in an HIV vaccine trial to investigate possible determinants, including vaccine status, vaccine-induced immune response level and viral genotype, of unusual correlation pattern between HIV viral load and CD4 count in newly infected patients. PMID:22740584

Resampling-based methods in single and multiple testing for equality of covariance/correlation matrices.

PubMed

Yang, Yang; DeGruttola, Victor

2012-06-22

Traditional resampling-based tests for homogeneity in covariance matrices across multiple groups resample residuals, that is, data centered by group means. These residuals do not share the same second moments when the null hypothesis is false, which makes them difficult to use in the setting of multiple testing. An alternative approach is to resample standardized residuals, data centered by group sample means and standardized by group sample covariance matrices. This approach, however, has been observed to inflate type I error when sample size is small or data are generated from heavy-tailed distributions. We propose to improve this approach by using robust estimation for the first and second moments. We discuss two statistics: the Bartlett statistic and a statistic based on eigen-decomposition of sample covariance matrices. Both statistics can be expressed in terms of standardized errors under the null hypothesis. These methods are extended to test homogeneity in correlation matrices. Using simulation studies, we demonstrate that the robust resampling approach provides comparable or superior performance, relative to traditional approaches, for single testing and reasonable performance for multiple testing. The proposed methods are applied to data collected in an HIV vaccine trial to investigate possible determinants, including vaccine status, vaccine-induced immune response level and viral genotype, of unusual correlation pattern between HIV viral load and CD4 count in newly infected patients.

Relationships between the Definition of the Hyperplane Width to the Fidelity of Principal Component Loading Patterns.

NASA Astrophysics Data System (ADS)

Richman, Michael B.; Gong, Xiaofeng

1999-06-01

When applying eigenanalysis, one decision analysts make is the determination of what magnitude an eigenvector coefficient (e.g., principal component (PC) loading) must achieve to be considered as physically important. Such coefficients can be displayed on maps or in a time series or tables to gain a fuller understanding of a large array of multivariate data. Previously, such a decision on what value of loading designates a useful signal (hereafter called the loading `cutoff') for each eigenvector has been purely subjective. The importance of selecting such a cutoff is apparent since those loading elements in the range of zero to the cutoff are ignored in the interpretation and naming of PCs since only the absolute values of loadings greater than the cutoff are physically analyzed. This research sets out to objectify the problem of best identifying the cutoff by application of matching between known correlation/covariance structures and their corresponding eigenpatterns, as this cutoff point (known as the hyperplane width) is varied.A Monte Carlo framework is used to resample at five sample sizes. Fourteen different hyperplane cutoff widths are tested, bootstrap resampled 50 times to obtain stable results. The key findings are that the location of an optimal hyperplane cutoff width (one which maximized the information content match between the eigenvector and the parent dispersion matrix from which it was derived) is a well-behaved unimodal function. On an individual eigenvector, this enables the unique determination of a hyperplane cutoff value to be used to separate those loadings that best reflect the relationships from those that do not. The effects of sample size on the matching accuracy are dramatic as the values for all solutions (i.e., unrotated, rotated) rose steadily from 25 through 250 observations and then weakly thereafter. The specific matching coefficients are useful to assess the penalties incurred when one analyzes eigenvector coefficients of a lower absolute value than the cutoff (termed coefficient in the hyperplane) or, alternatively, chooses not to analyze coefficients that contain useful physical signal outside of the hyperplane. Therefore, this study enables the analyst to make the best use of the information available in their PCs to shed light on complicated data structures.

Resampling soil profiles can constrain large-scale changes in the C cycle: obtaining robust information from radiocarbon measurements

NASA Astrophysics Data System (ADS)

Baisden, W. T.; Prior, C.; Lambie, S.; Tate, K.; Bruhn, F.; Parfitt, R.; Schipper, L.; Wilde, R. H.; Ross, C.

2006-12-01

Soil organic matter contains more C than terrestrial biomass and atmospheric CO2 combined, and reacts to climate and land-use change on timescales requiring long-term experiments or monitoring. The direction and uncertainty of soil C stock changes has been difficult to predict and incorporate in decision support tools for climate change policies. Moreover, standardization of approaches has been difficult because historic methods of soil sampling have varied regionally, nationally and temporally. The most common and uniform type of historic sampling is soil profiles, which have commonly been collected, described and archived in the course of both soil survey studies and research. Resampling soil profiles has considerable utility in carbon monitoring and in parameterizing models to understand the ecosystem responses to global change. Recent work spanning seven soil orders in New Zealand's grazed pastures has shown that, averaged over approximately 20 years, 31 soil profiles lost 106 g C m-2 y-1 (p=0.01) and 9.1 g N m{^-2} y-1 (p=0.002). These losses are unexpected and appear to extend well below the upper 30 cm of soil. Following on these recent results, additional advantages of resampling soil profiles can be emphasized. One of the most powerful applications afforded by resampling archived soils is the use of the pulse label of radiocarbon injected into the atmosphere by thermonuclear weapons testing circa 1963 as a tracer of soil carbon dynamics. This approach allows estimation of the proportion of soil C that is `passive' or `inert' and therefore unlikely to respond to global change. Evaluation of resampled soil horizons in a New Zealand soil chronosequence confirms that the approach yields consistent values for the proportion of `passive' soil C, reaching 25% of surface horizon soil C over 12,000 years. Across whole profiles, radiocarbon data suggest that the proportion of `passive' C in New Zealand grassland soil can be less than 40% of total soil C. Below 30 cm, 1 kg C m-2 or more may be reactive on decadal timescales, supporting evidence of soil C losses from throughout the soil profiles. Information from resampled soil profiles can be combined with additional contemporary measurements to test hypotheses about mechanisms for soil C changes. For example, Δ14C in excess of 200‰ in water extractable dissolved organic C (DOC) from surface soil horizons supports the hypothesis that decadal movement of DOC represents an important translocation of soil C. These preliminary results demonstrate that resampling whole soil profiles can support substantial progress in C cycle science, ranging from updating operational C accounting systems to the frontiers of research. Resampling can be complementary or superior to fixed-depth interval sampling of surface soil layers. Resampling must however be undertaken with relative urgency to maximize the potential interpretive power of bomb-derived radiocarbon.

Coefficient Omega Bootstrap Confidence Intervals: Nonnormal Distributions

ERIC Educational Resources Information Center

Padilla, Miguel A.; Divers, Jasmin

2013-01-01

The performance of the normal theory bootstrap (NTB), the percentile bootstrap (PB), and the bias-corrected and accelerated (BCa) bootstrap confidence intervals (CIs) for coefficient omega was assessed through a Monte Carlo simulation under conditions not previously investigated. Of particular interests were nonnormal Likert-type and binary items.…

Tests of Independence for Ordinal Data Using Bootstrap.

ERIC Educational Resources Information Center

Chan, Wai; Yung, Yiu-Fai; Bentler, Peter M.; Tang, Man-Lai

1998-01-01

Two bootstrap tests are proposed to test the independence hypothesis in a two-way cross table. Monte Carlo studies are used to compare the traditional asymptotic test with these bootstrap methods, and the bootstrap methods are found superior in two ways: control of Type I error and statistical power. (SLD)

Introduction to Permutation and Resampling-Based Hypothesis Tests

ERIC Educational Resources Information Center

LaFleur, Bonnie J.; Greevy, Robert A.

2009-01-01

A resampling-based method of inference--permutation tests--is often used when distributional assumptions are questionable or unmet. Not only are these methods useful for obvious departures from parametric assumptions (e.g., normality) and small sample sizes, but they are also more robust than their parametric counterparts in the presences of…

Inversion of marine gravity anomalies over southeastern China seas from multi-satellite altimeter vertical deflections

NASA Astrophysics Data System (ADS)

Zhang, Shengjun; Sandwell, David T.; Jin, Taoyong; Li, Dawei

2017-02-01

The accuracy and resolution of marine gravity field derived from satellite altimetry mainly depends on the range precision and dense spatial distribution. This paper aims at modeling a regional marine gravity field with improved accuracy and higher resolution (1‧ × 1‧) over Southeastern China Seas using additional data from CryoSat-2 as well as new data from AltiKa. Three approaches are used to enhance the precision level of satellite-derived gravity anomalies. Firstly we evaluate a suite of published retracking algorithms and find the two-step retracker is optimal for open ocean waveforms. Secondly, we evaluate the filtering and resampling procedure used to reduce the full 20 or 40 Hz data to a lower rate having lower noise. We adopt a uniform low-pass filter for all altimeter missions and resample at 5 Hz and then perform a second editing based on sea surface slope estimates from previous models. Thirdly, we selected WHU12 model to update the corrections provided in geophysical data record. We finally calculated the 1‧ × 1‧ marine gravity field model by using EGM2008 model as reference field during the remove/restore procedure. The root mean squares of the discrepancies between the new result and DTU10, DTU13, V23.1, EGM2008 are within the range of 1.8- 3.9 mGal, while the verification with respect to shipboard gravity data shows that the accuracy of the new result reached a comparable level with DTU13 and was slightly superior to V23.1, DTU10 and EGM2008 models. Moreover, the new result has a 2 mGal better accuracy over open seas than coastal areas with shallow water depth.

Damage Detection in Flexible Plates through Reduced-Order Modeling and Hybrid Particle-Kalman Filtering

PubMed Central

Capellari, Giovanni; Eftekhar Azam, Saeed; Mariani, Stefano

2015-01-01

Health monitoring of lightweight structures, like thin flexible plates, is of interest in several engineering fields. In this paper, a recursive Bayesian procedure is proposed to monitor the health of such structures through data collected by a network of optimally placed inertial sensors. As a main drawback of standard monitoring procedures is linked to the computational costs, two remedies are jointly considered: first, an order-reduction of the numerical model used to track the structural dynamics, enforced with proper orthogonal decomposition; and, second, an improved particle filter, which features an extended Kalman updating of each evolving particle before the resampling stage. The former remedy can reduce the number of effective degrees-of-freedom of the structural model to a few only (depending on the excitation), whereas the latter one allows to track the evolution of damage and to locate it thanks to an intricate formulation. To assess the effectiveness of the proposed procedure, the case of a plate subject to bending is investigated; it is shown that, when the procedure is appropriately fed by measurements, damage is efficiently and accurately estimated. PMID:26703615

A Robust Kalman Framework with Resampling and Optimal Smoothing

PubMed Central

Kautz, Thomas; Eskofier, Bjoern M.

2015-01-01

The Kalman filter (KF) is an extremely powerful and versatile tool for signal processing that has been applied extensively in various fields. We introduce a novel Kalman-based analysis procedure that encompasses robustness towards outliers, Kalman smoothing and real-time conversion from non-uniformly sampled inputs to a constant output rate. These features have been mostly treated independently, so that not all of their benefits could be exploited at the same time. Here, we present a coherent analysis procedure that combines the aforementioned features and their benefits. To facilitate utilization of the proposed methodology and to ensure optimal performance, we also introduce a procedure to calculate all necessary parameters. Thereby, we substantially expand the versatility of one of the most widely-used filtering approaches, taking full advantage of its most prevalent extensions. The applicability and superior performance of the proposed methods are demonstrated using simulated and real data. The possible areas of applications for the presented analysis procedure range from movement analysis over medical imaging, brain-computer interfaces to robot navigation or meteorological studies. PMID:25734647

A PRIM approach to predictive-signature development for patient stratification.

PubMed

Chen, Gong; Zhong, Hua; Belousov, Anton; Devanarayan, Viswanath

2015-01-30

Patients often respond differently to a treatment because of individual heterogeneity. Failures of clinical trials can be substantially reduced if, prior to an investigational treatment, patients are stratified into responders and nonresponders based on biological or demographic characteristics. These characteristics are captured by a predictive signature. In this paper, we propose a procedure to search for predictive signatures based on the approach of patient rule induction method. Specifically, we discuss selection of a proper objective function for the search, present its algorithm, and describe a resampling scheme that can enhance search performance. Through simulations, we characterize conditions under which the procedure works well. To demonstrate practical uses of the procedure, we apply it to two real-world data sets. We also compare the results with those obtained from a recent regression-based approach, Adaptive Index Models, and discuss their respective advantages. In this study, we focus on oncology applications with survival responses. © 2014 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

Confidence intervals in Flow Forecasting by using artificial neural networks

NASA Astrophysics Data System (ADS)

Panagoulia, Dionysia; Tsekouras, George

2014-05-01

One of the major inadequacies in implementation of Artificial Neural Networks (ANNs) for flow forecasting is the development of confidence intervals, because the relevant estimation cannot be implemented directly, contrasted to the classical forecasting methods. The variation in the ANN output is a measure of uncertainty in the model predictions based on the training data set. Different methods for uncertainty analysis, such as bootstrap, Bayesian, Monte Carlo, have already proposed for hydrologic and geophysical models, while methods for confidence intervals, such as error output, re-sampling, multi-linear regression adapted to ANN have been used for power load forecasting [1-2]. The aim of this paper is to present the re-sampling method for ANN prediction models and to develop this for flow forecasting of the next day. The re-sampling method is based on the ascending sorting of the errors between real and predicted values for all input vectors. The cumulative sample distribution function of the prediction errors is calculated and the confidence intervals are estimated by keeping the intermediate value, rejecting the extreme values according to the desired confidence levels, and holding the intervals symmetrical in probability. For application of the confidence intervals issue, input vectors are used from the Mesochora catchment in western-central Greece. The ANN's training algorithm is the stochastic training back-propagation process with decreasing functions of learning rate and momentum term, for which an optimization process is conducted regarding the crucial parameters values, such as the number of neurons, the kind of activation functions, the initial values and time parameters of learning rate and momentum term etc. Input variables are historical data of previous days, such as flows, nonlinearly weather related temperatures and nonlinearly weather related rainfalls based on correlation analysis between the under prediction flow and each implicit input variable of different ANN structures [3]. The performance of each ANN structure is evaluated by the voting analysis based on eleven criteria, which are the root mean square error (RMSE), the correlation index (R), the mean absolute percentage error (MAPE), the mean percentage error (MPE), the mean percentage error (ME), the percentage volume in errors (VE), the percentage error in peak (MF), the normalized mean bias error (NMBE), the normalized root mean bias error (NRMSE), the Nash-Sutcliffe model efficiency coefficient (E) and the modified Nash-Sutcliffe model efficiency coefficient (E1). The next day flow for the test set is calculated using the best ANN structure's model. Consequently, the confidence intervals of various confidence levels for training, evaluation and test sets are compared in order to explore the generalisation dynamics of confidence intervals from training and evaluation sets. [1] H.S. Hippert, C.E. Pedreira, R.C. Souza, "Neural networks for short-term load forecasting: A review and evaluation," IEEE Trans. on Power Systems, vol. 16, no. 1, 2001, pp. 44-55. [2] G. J. Tsekouras, N.E. Mastorakis, F.D. Kanellos, V.T. Kontargyri, C.D. Tsirekis, I.S. Karanasiou, Ch.N. Elias, A.D. Salis, P.A. Kontaxis, A.A. Gialketsi: "Short term load forecasting in Greek interconnected power system using ANN: Confidence Interval using a novel re-sampling technique with corrective Factor", WSEAS International Conference on Circuits, Systems, Electronics, Control & Signal Processing, (CSECS '10), Vouliagmeni, Athens, Greece, December 29-31, 2010. [3] D. Panagoulia, I. Trichakis, G. J. Tsekouras: "Flow Forecasting via Artificial Neural Networks - A Study for Input Variables conditioned on atmospheric circulation", European Geosciences Union, General Assembly 2012 (NH1.1 / AS1.16 - Extreme meteorological and hydrological events induced by severe weather and climate change), Vienna, Austria, 22-27 April 2012.

Using the Descriptive Bootstrap to Evaluate Result Replicability (Because Statistical Significance Doesn't)

ERIC Educational Resources Information Center

Spinella, Sarah

2011-01-01

As result replicability is essential to science and difficult to achieve through external replicability, the present paper notes the insufficiency of null hypothesis statistical significance testing (NHSST) and explains the bootstrap as a plausible alternative, with a heuristic example to illustrate the bootstrap method. The bootstrap relies on…

Anomalous change detection in imagery

DOEpatents

Theiler, James P [Los Alamos, NM; Perkins, Simon J [Santa Fe, NM

2011-05-31

A distribution-based anomaly detection platform is described that identifies a non-flat background that is specified in terms of the distribution of the data. A resampling approach is also disclosed employing scrambled resampling of the original data with one class specified by the data and the other by the explicit distribution, and solving using binary classification.

De-Dopplerization of Acoustic Measurements

DTIC Science & Technology

2017-08-10

band energy obtained from fractional octave band digital filters generates a de-Dopplerized spectrum without complex resampling algorithms. An...energy obtained from fractional octave band digital filters generates a de-Dopplerized spectrum without complex resampling algorithms. An equation...fractional octave representation and smearing that occurs within the spectrum11, digital filtering techniques were not considered by these earlier

Thematic mapper design parameter investigation

NASA Technical Reports Server (NTRS)

Colby, C. P., Jr.; Wheeler, S. G.

1978-01-01

This study simulated the multispectral data sets to be expected from three different Thematic Mapper configurations, and the ground processing of these data sets by three different resampling techniques. The simulated data sets were then evaluated by processing them for multispectral classification, and the Thematic Mapper configuration, and resampling technique which provided the best classification accuracy were identified.

Effects of magnetic islands on bootstrap current in toroidal plasmas

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dong, G.; Lin, Z.

The effects of magnetic islands on electron bootstrap current in toroidal plasmas are studied using gyrokinetic simulations. The magnetic islands cause little changes of the bootstrap current level in the banana regime because of trapped electron effects. In the plateau regime, the bootstrap current is completely suppressed at the island centers due to the destruction of trapped electron orbits by collisions and the flattening of pressure profiles by the islands. In the collisional regime, small but finite bootstrap current can exist inside the islands because of the pressure gradients created by large collisional transport across the islands. Lastly, simulation resultsmore » show that the bootstrap current level increases near the island separatrix due to steeper local density gradients.« less

Effects of magnetic islands on bootstrap current in toroidal plasmas

DOE PAGES

Dong, G.; Lin, Z.

2016-12-19

The effects of magnetic islands on electron bootstrap current in toroidal plasmas are studied using gyrokinetic simulations. The magnetic islands cause little changes of the bootstrap current level in the banana regime because of trapped electron effects. In the plateau regime, the bootstrap current is completely suppressed at the island centers due to the destruction of trapped electron orbits by collisions and the flattening of pressure profiles by the islands. In the collisional regime, small but finite bootstrap current can exist inside the islands because of the pressure gradients created by large collisional transport across the islands. Lastly, simulation resultsmore » show that the bootstrap current level increases near the island separatrix due to steeper local density gradients.« less

A Sequential Ensemble Prediction System at Convection Permitting Scales

NASA Astrophysics Data System (ADS)

Milan, M.; Simmer, C.

2012-04-01

A Sequential Assimilation Method (SAM) following some aspects of particle filtering with resampling, also called SIR (Sequential Importance Resampling), is introduced and applied in the framework of an Ensemble Prediction System (EPS) for weather forecasting on convection permitting scales, with focus to precipitation forecast. At this scale and beyond, the atmosphere increasingly exhibits chaotic behaviour and non linear state space evolution due to convectively driven processes. One way to take full account of non linear state developments are particle filter methods, their basic idea is the representation of the model probability density function by a number of ensemble members weighted by their likelihood with the observations. In particular particle filter with resampling abandons ensemble members (particles) with low weights restoring the original number of particles adding multiple copies of the members with high weights. In our SIR-like implementation we substitute the likelihood way to define weights and introduce a metric which quantifies the "distance" between the observed atmospheric state and the states simulated by the ensemble members. We also introduce a methodology to counteract filter degeneracy, i.e. the collapse of the simulated state space. To this goal we propose a combination of resampling taking account of simulated state space clustering and nudging. By keeping cluster representatives during resampling and filtering, the method maintains the potential for non linear system state development. We assume that a particle cluster with initially low likelihood may evolve in a state space with higher likelihood in a subsequent filter time thus mimicking non linear system state developments (e.g. sudden convection initiation) and remedies timing errors for convection due to model errors and/or imperfect initial condition. We apply a simplified version of the resampling, the particles with highest weights in each cluster are duplicated; for the model evolution for each particle pair one particle evolves using the forward model; the second particle, however, is nudged to the radar and satellite observation during its evolution based on the forward model.

Bootstrapping Least Squares Estimates in Biochemical Reaction Networks

PubMed Central

Linder, Daniel F.

2015-01-01

The paper proposes new computational methods of computing confidence bounds for the least squares estimates (LSEs) of rate constants in mass-action biochemical reaction network and stochastic epidemic models. Such LSEs are obtained by fitting the set of deterministic ordinary differential equations (ODEs), corresponding to the large volume limit of a reaction network, to network’s partially observed trajectory treated as a continuous-time, pure jump Markov process. In the large volume limit the LSEs are asymptotically Gaussian, but their limiting covariance structure is complicated since it is described by a set of nonlinear ODEs which are often ill-conditioned and numerically unstable. The current paper considers two bootstrap Monte-Carlo procedures, based on the diffusion and linear noise approximations for pure jump processes, which allow one to avoid solving the limiting covariance ODEs. The results are illustrated with both in-silico and real data examples from the LINE 1 gene retrotranscription model and compared with those obtained using other methods. PMID:25898769

Percolation in education and application in the 21st century

NASA Astrophysics Data System (ADS)

Adler, Joan; Elfenbaum, Shaked; Sharir, Liran

2017-03-01

Percolation, "so simple you could teach it to your wife" (Chuck Newman, last century) is an ideal system to introduce young students to phase transitions. Two recent projects in the Computational Physics group at the Technion make this easy. One is a set of analog models to be mounted on our walls and enable visitors to switch between samples to see which mixtures of glass and metal objects have a percolating current. The second is a website enabling the creation of stereo samples of two and three dimensional clusters (suited for viewing with Oculus rift) on desktops, tablets and smartphones. Although there have been many physical applications for regular percolation in the past, for Bootstrap Percolation, where only sites with sufficient occupied neighbours remain active, there have not been a surfeit of condensed matter applications. We have found that the creation of diamond membranes for quantum computers can be modeled with a bootstrap process of graphitization in diamond, enabling prediction of optimal processing procedures.

Maturity associated variance in physical activity and health-related quality of life in adolescent females: a mediated effects model.

PubMed

Smart, Joan E Hunter; Cumming, Sean P; Sherar, Lauren B; Standage, Martyn; Neville, Helen; Malina, Robert M

2012-01-01

This study tested a mediated effects model of psychological and behavioral adaptation to puberty within the context of physical activity (PA). Biological maturity status, physical self-concept, PA, and health-related quality of life (HRQoL) were assessed in 222 female British year 7 to 9 pupils (mean age = 12.7 years, SD = .8). Structural equation modeling using maximum likelihood estimation and bootstrapping procedures supported the hypothesized model. Maturation status was inversely related to perceptions of sport competence, body attractiveness, and physical condition; and indirectly and inversely related to physical self-worth, PA, and HRQoL. Examination of the bootstrap-generated bias-corrected confidence intervals representing the direct and indirect paths between suggested that physical self-concept partially mediated the relations between maturity status and PA, and maturity status and HRQoL. Evidence supports the contention that perceptions of the physical self partially mediate relations maturity, PA, and HRQoL in adolescent females.

System health monitoring using multiple-model adaptive estimation techniques

NASA Astrophysics Data System (ADS)

Sifford, Stanley Ryan

Monitoring system health for fault detection and diagnosis by tracking system parameters concurrently with state estimates is approached using a new multiple-model adaptive estimation (MMAE) method. This novel method is called GRid-based Adaptive Parameter Estimation (GRAPE). GRAPE expands existing MMAE methods by using new techniques to sample the parameter space. GRAPE expands on MMAE with the hypothesis that sample models can be applied and resampled without relying on a predefined set of models. GRAPE is initially implemented in a linear framework using Kalman filter models. A more generalized GRAPE formulation is presented using extended Kalman filter (EKF) models to represent nonlinear systems. GRAPE can handle both time invariant and time varying systems as it is designed to track parameter changes. Two techniques are presented to generate parameter samples for the parallel filter models. The first approach is called selected grid-based stratification (SGBS). SGBS divides the parameter space into equally spaced strata. The second approach uses Latin Hypercube Sampling (LHS) to determine the parameter locations and minimize the total number of required models. LHS is particularly useful when the parameter dimensions grow. Adding more parameters does not require the model count to increase for LHS. Each resample is independent of the prior sample set other than the location of the parameter estimate. SGBS and LHS can be used for both the initial sample and subsequent resamples. Furthermore, resamples are not required to use the same technique. Both techniques are demonstrated for both linear and nonlinear frameworks. The GRAPE framework further formalizes the parameter tracking process through a general approach for nonlinear systems. These additional methods allow GRAPE to either narrow the focus to converged values within a parameter range or expand the range in the appropriate direction to track the parameters outside the current parameter range boundary. Customizable rules define the specific resample behavior when the GRAPE parameter estimates converge. Convergence itself is determined from the derivatives of the parameter estimates using a simple moving average window to filter out noise. The system can be tuned to match the desired performance goals by making adjustments to parameters such as the sample size, convergence criteria, resample criteria, initial sampling method, resampling method, confidence in prior sample covariances, sample delay, and others.

Exchangeability, extreme returns and Value-at-Risk forecasts

NASA Astrophysics Data System (ADS)

Huang, Chun-Kai; North, Delia; Zewotir, Temesgen

2017-07-01

In this paper, we propose a new approach to extreme value modelling for the forecasting of Value-at-Risk (VaR). In particular, the block maxima and the peaks-over-threshold methods are generalised to exchangeable random sequences. This caters for the dependencies, such as serial autocorrelation, of financial returns observed empirically. In addition, this approach allows for parameter variations within each VaR estimation window. Empirical prior distributions of the extreme value parameters are attained by using resampling procedures. We compare the results of our VaR forecasts to that of the unconditional extreme value theory (EVT) approach and the conditional GARCH-EVT model for robust conclusions.

The Success of Linear Bootstrapping Models: Decision Domain-, Expertise-, and Criterion-Specific Meta-Analysis

PubMed Central

Kaufmann, Esther; Wittmann, Werner W.

2016-01-01

The success of bootstrapping or replacing a human judge with a model (e.g., an equation) has been demonstrated in Paul Meehl’s (1954) seminal work and bolstered by the results of several meta-analyses. To date, however, analyses considering different types of meta-analyses as well as the potential dependence of bootstrapping success on the decision domain, the level of expertise of the human judge, and the criterion for what constitutes an accurate decision have been missing from the literature. In this study, we addressed these research gaps by conducting a meta-analysis of lens model studies. We compared the results of a traditional (bare-bones) meta-analysis with findings of a meta-analysis of the success of bootstrap models corrected for various methodological artifacts. In line with previous studies, we found that bootstrapping was more successful than human judgment. Furthermore, bootstrapping was more successful in studies with an objective decision criterion than in studies with subjective or test score criteria. We did not find clear evidence that the success of bootstrapping depended on the decision domain (e.g., education or medicine) or on the judge’s level of expertise (novice or expert). Correction of methodological artifacts increased the estimated success of bootstrapping, suggesting that previous analyses without artifact correction (i.e., traditional meta-analyses) may have underestimated the value of bootstrapping models. PMID:27327085

Resampling and Distribution of the Product Methods for Testing Indirect Effects in Complex Models

ERIC Educational Resources Information Center

Williams, Jason; MacKinnon, David P.

2008-01-01

Recent advances in testing mediation have found that certain resampling methods and tests based on the mathematical distribution of 2 normal random variables substantially outperform the traditional "z" test. However, these studies have primarily focused only on models with a single mediator and 2 component paths. To address this limitation, a…

Gaussian Process Interpolation for Uncertainty Estimation in Image Registration

PubMed Central

Wachinger, Christian; Golland, Polina; Reuter, Martin; Wells, William

2014-01-01

Intensity-based image registration requires resampling images on a common grid to evaluate the similarity function. The uncertainty of interpolation varies across the image, depending on the location of resampled points relative to the base grid. We propose to perform Bayesian inference with Gaussian processes, where the covariance matrix of the Gaussian process posterior distribution estimates the uncertainty in interpolation. The Gaussian process replaces a single image with a distribution over images that we integrate into a generative model for registration. Marginalization over resampled images leads to a new similarity measure that includes the uncertainty of the interpolation. We demonstrate that our approach increases the registration accuracy and propose an efficient approximation scheme that enables seamless integration with existing registration methods. PMID:25333127

Statistical methods for detecting periodic fragments in DNA sequence data

PubMed Central

2011-01-01

Background Period 10 dinucleotides are structurally and functionally validated factors that influence the ability of DNA to form nucleosomes, histone core octamers. Robust identification of periodic signals in DNA sequences is therefore required to understand nucleosome organisation in genomes. While various techniques for identifying periodic components in genomic sequences have been proposed or adopted, the requirements for such techniques have not been considered in detail and confirmatory testing for a priori specified periods has not been developed. Results We compared the estimation accuracy and suitability for confirmatory testing of autocorrelation, discrete Fourier transform (DFT), integer period discrete Fourier transform (IPDFT) and a previously proposed Hybrid measure. A number of different statistical significance procedures were evaluated but a blockwise bootstrap proved superior. When applied to synthetic data whose period-10 signal had been eroded, or for which the signal was approximately period-10, the Hybrid technique exhibited superior properties during exploratory period estimation. In contrast, confirmatory testing using the blockwise bootstrap procedure identified IPDFT as having the greatest statistical power. These properties were validated on yeast sequences defined from a ChIP-chip study where the Hybrid metric confirmed the expected dominance of period-10 in nucleosome associated DNA but IPDFT identified more significant occurrences of period-10. Application to the whole genomes of yeast and mouse identified ~ 21% and ~ 19% respectively of these genomes as spanned by period-10 nucleosome positioning sequences (NPS). Conclusions For estimating the dominant period, we find the Hybrid period estimation method empirically to be the most effective for both eroded and approximate periodicity. The blockwise bootstrap was found to be effective as a significance measure, performing particularly well in the problem of period detection in the presence of eroded periodicity. The autocorrelation method was identified as poorly suited for use with the blockwise bootstrap. Application of our methods to the genomes of two model organisms revealed a striking proportion of the yeast and mouse genomes are spanned by NPS. Despite their markedly different sizes, roughly equivalent proportions (19-21%) of the genomes lie within period-10 spans of the NPS dinucleotides {AA, TT, TA}. The biological significance of these regions remains to be demonstrated. To facilitate this, the genomic coordinates are available as Additional files 1, 2, and 3 in a format suitable for visualisation as tracks on popular genome browsers. Reviewers This article was reviewed by Prof Tomas Radivoyevitch, Dr Vsevolod Makeev (nominated by Dr Mikhail Gelfand), and Dr Rob D Knight. PMID:21527008

A Pragmatic Cognitive System Engineering Approach to Model Dynamic Human Decision-Making Activities in Intelligent and Automated Systems

DTIC Science & Technology

2003-10-01

Among the procedures developed to identify cognitive processes, there are the Cognitive Task Analysis (CTA) and the Cognitive Work Analysis (CWA...of Cognitive Task Design. [11] Potter, S.S., Roth, E.M., Woods, D.D., and Elm, W.C. (2000). Cognitive Task Analysis as Bootstrapping Multiple...Converging Techniques, In Schraagen, Chipman, and Shalin (Eds.). Cognitive Task Analysis . Mahwah, NJ: Lawrence Erlbaum Associates. [12] Roth, E.M

Isotropic source terms of San Jacinto fault zone earthquakes based on waveform inversions with a generalized CAP method

NASA Astrophysics Data System (ADS)

Ross, Z. E.; Ben-Zion, Y.; Zhu, L.

2015-02-01

We analyse source tensor properties of seven Mw > 4.2 earthquakes in the complex trifurcation area of the San Jacinto Fault Zone, CA, with a focus on isotropic radiation that may be produced by rock damage in the source volumes. The earthquake mechanisms are derived with generalized `Cut and Paste' (gCAP) inversions of three-component waveforms typically recorded by >70 stations at regional distances. The gCAP method includes parameters ζ and χ representing, respectively, the relative strength of the isotropic and CLVD source terms. The possible errors in the isotropic and CLVD components due to station variability is quantified with bootstrap resampling for each event. The results indicate statistically significant explosive isotropic components for at least six of the events, corresponding to ˜0.4-8 per cent of the total potency/moment of the sources. In contrast, the CLVD components for most events are not found to be statistically significant. Trade-off and correlation between the isotropic and CLVD components are studied using synthetic tests with realistic station configurations. The associated uncertainties are found to be generally smaller than the observed isotropic components. Two different tests with velocity model perturbation are conducted to quantify the uncertainty due to inaccuracies in the Green's functions. Applications of the Mann-Whitney U test indicate statistically significant explosive isotropic terms for most events consistent with brittle damage production at the source.

Factors that Affect Willingness to Donate Blood for the Purpose of Biospecimen Research in the Korean American Community

PubMed Central

Yen, Glorian P.; Davey, Adam

2015-01-01

Objective: Biorepositories have been key resources in examining genetically-linked diseases, particularly cancer. Asian Americans contribute to biorepositories at lower rates than other racial groups, but the reasons for this are unclear. We hypothesized that attitudes toward biospecimen research mediate the relationship between demographic and healthcare access factors, and willingness to donate blood for research purposes among individuals of Korean heritage. Methods: Descriptive statistics and bivariate analyses were utilized to characterize the sample with respect to demographic, psychosocial, and behavioral variables. Structural equation modeling with 5000 re-sample bootstrapping was used to assess each component of the proposed simple mediation models. Results: Attitudes towards biospecimen research fully mediate associations between age, income, number of years lived in the United States, and having a regular physician and willingness to donate blood for the purpose of research. Conclusion: Participants were willing to donate blood for the purpose of research despite having neutral feelings towards biospecimen research as a whole. Participants reported higher willingness to donate blood for research purposes when they were older, had lived in the United States longer, had higher income, and had a regular doctor that they visited. Many of the significant relationships between demographic and health care access factors, attitudes towards biospecimen research, and willingness to donate blood for the purpose of research may be explained by the extent of acculturation of the participants in the United States. PMID:25853387

Complex relationship between seasonal streamflow forecast skill and value in reservoir operations

NASA Astrophysics Data System (ADS)

Turner, Sean W. D.; Bennett, James C.; Robertson, David E.; Galelli, Stefano

2017-09-01

Considerable research effort has recently been directed at improving and operationalising ensemble seasonal streamflow forecasts. Whilst this creates new opportunities for improving the performance of water resources systems, there may also be associated risks. Here, we explore these potential risks by examining the sensitivity of forecast value (improvement in system performance brought about by adopting forecasts) to changes in the forecast skill for a range of hypothetical reservoir designs with contrasting operating objectives. Forecast-informed operations are simulated using rolling horizon, adaptive control and then benchmarked against optimised control rules to assess performance improvements. Results show that there exists a strong relationship between forecast skill and value for systems operated to maintain a target water level. But this relationship breaks down when the reservoir is operated to satisfy a target demand for water; good forecast accuracy does not necessarily translate into performance improvement. We show that the primary cause of this behaviour is the buffering role played by storage in water supply reservoirs, which renders the forecast superfluous for long periods of the operation. System performance depends primarily on forecast accuracy when critical decisions are made - namely during severe drought. As it is not possible to know in advance if a forecast will perform well at such moments, we advocate measuring the consistency of forecast performance, through bootstrap resampling, to indicate potential usefulness in storage operations. Our results highlight the need for sensitivity assessment in value-of-forecast studies involving reservoirs with supply objectives.

Using logistic regression modeling to predict sexual recidivism: the Minnesota Sex Offender Screening Tool-3 (MnSOST-3).

PubMed

Duwe, Grant; Freske, Pamela J

2012-08-01

This study presents the results from efforts to revise the Minnesota Sex Offender Screening Tool-Revised (MnSOST-R), one of the most widely used sex offender risk-assessment tools. The updated instrument, the MnSOST-3, contains nine individual items, six of which are new. The population for this study consisted of the cross-validation sample for the MnSOST-R (N = 220) and a contemporary sample of 2,315 sex offenders released from Minnesota prisons between 2003 and 2006. To score and select items for the MnSOST-3, we used predicted probabilities generated from a multiple logistic regression model. We used bootstrap resampling to not only refine our selection of predictors but also internally validate the model. The results indicate the MnSOST-3 has a relatively high level of predictive discrimination, as evidenced by an apparent AUC of .821 and an optimism-corrected AUC of .796. The findings show the MnSOST-3 is well calibrated with actual recidivism rates for all but the highest risk offenders. Although estimating a penalized maximum likelihood model did not improve the overall calibration, the results suggest the MnSOST-3 may still be useful in helping identify high-risk offenders whose sexual recidivism risk exceeds 50%. Results from an interrater reliability assessment indicate the instrument, which is scored in a Microsoft Excel application, has an adequate degree of consistency across raters (ICC = .83 for both consistency and absolute agreement).

Complex relationship between seasonal streamflow forecast skill and value in reservoir operations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Turner, Sean W. D.; Bennett, James C.; Robertson, David E.

Considerable research effort has recently been directed at improving and operationalising ensemble seasonal streamflow forecasts. Whilst this creates new opportunities for improving the performance of water resources systems, there may also be associated risks. Here, we explore these potential risks by examining the sensitivity of forecast value (improvement in system performance brought about by adopting forecasts) to changes in the forecast skill for a range of hypothetical reservoir designs with contrasting operating objectives. Forecast-informed operations are simulated using rolling horizon, adaptive control and then benchmarked against optimised control rules to assess performance improvements. Results show that there exists a strongmore » relationship between forecast skill and value for systems operated to maintain a target water level. But this relationship breaks down when the reservoir is operated to satisfy a target demand for water; good forecast accuracy does not necessarily translate into performance improvement. We show that the primary cause of this behaviour is the buffering role played by storage in water supply reservoirs, which renders the forecast superfluous for long periods of the operation. System performance depends primarily on forecast accuracy when critical decisions are made – namely during severe drought. As it is not possible to know in advance if a forecast will perform well at such moments, we advocate measuring the consistency of forecast performance, through bootstrap resampling, to indicate potential usefulness in storage operations. Our results highlight the need for sensitivity assessment in value-of-forecast studies involving reservoirs with supply objectives.« less

Identifying reliable independent components via split-half comparisons

PubMed Central

Groppe, David M.; Makeig, Scott; Kutas, Marta

2011-01-01

Independent component analysis (ICA) is a family of unsupervised learning algorithms that have proven useful for the analysis of the electroencephalogram (EEG) and magnetoencephalogram (MEG). ICA decomposes an EEG/MEG data set into a basis of maximally temporally independent components (ICs) that are learned from the data. As with any statistic, a concern with using ICA is the degree to which the estimated ICs are reliable. An IC may not be reliable if ICA was trained on insufficient data, if ICA training was stopped prematurely or at a local minimum (for some algorithms), or if multiple global minima were present. Consequently, evidence of ICA reliability is critical for the credibility of ICA results. In this paper, we present a new algorithm for assessing the reliability of ICs based on applying ICA separately to split-halves of a data set. This algorithm improves upon existing methods in that it considers both IC scalp topographies and activations, uses a probabilistically interpretable threshold for accepting ICs as reliable, and requires applying ICA only three times per data set. As evidence of the method’s validity, we show that the method can perform comparably to more time intensive bootstrap resampling and depends in a reasonable manner on the amount of training data. Finally, using the method we illustrate the importance of checking the reliability of ICs by demonstrating that IC reliability is dramatically increased by removing the mean EEG at each channel for each epoch of data rather than the mean EEG in a prestimulus baseline. PMID:19162199

Aestuariicola saemankumensis gen. nov., sp. nov., a member of the family Flavobacteriaceae, isolated from tidal flat sediment.

PubMed

Yoon, Jung-Hoon; Kang, So-Jung; Jung, Yong-Taek; Oh, Tae-Kwang

2008-09-01

A Gram-negative, non-motile, pleomorphic bacterial strain, designated SMK-142(T), was isolated from a tidal flat of the Yellow Sea, Korea, and was subjected to a polyphasic taxonomic study. Strain SMK-142(T) grew optimally at pH 7.0-8.0, 25 degrees C and in the presence of 2% (w/v) NaCl. Phylogenetic analyses based on 16S rRNA gene sequences showed that strain SMK-142(T) clustered with Lutibacter litoralis with which it exhibited a 16S rRNA gene sequence similarity value of 91.2%. This cluster joined the clade comprising the genera Tenacibaculum and Polaribacter at a high bootstrap resampling value. Strain SMK-142(T) contained MK-6 as the predominant menaquinone and iso-C(15:0), iso-C(15:1) and iso-C(17:0) 3-OH as the major fatty acids. The DNA G+C content was 37.2 mol%. Strain SMK-142(T) was differentiated from three phylogenetically related genera, Lutibacter, Tenacibaculum and Polaribacter, on the basis of low 16S rRNA gene sequence similarity values and differences in fatty acid profiles and in some phenotypic properties. On the basis of phenotypic, chemotaxonomic and phylogenetic data, strain SMK-142(T) represents a novel genus and species for which the name Aestuariicola saemankumensis gen. nov., sp. nov. is proposed (phylum Bacteroidetes, family Flavobacteriaceae). The type strain of the type species, Aestuariicola saemankumensis sp. nov., is SMK-142(T) (=KCTC 22171(T)=CCUG 55329(T)).

Association of Long-Duration Breastfeeding and Dental Caries Estimated with Marginal Structural Models

PubMed Central

Chaffee, Benjamin W.; Feldens, Carlos Alberto; Vítolo, Márcia Regina

2014-01-01

Purpose Estimate the association between breastfeeding ≥24 months and severe early childhood caries (ECC). Methods Within a birth cohort (n=715) from low-income families in Porto Alegre, Brazil, the age 38-month prevalence of severe-ECC (≥4 affected tooth surfaces or ≥1 affected maxillary anterior teeth) was compared over breastfeeding duration categories using marginal structural models to account for time-dependent confounding by other feeding habits and child growth. Additional analyses assessed whether daily breastfeeding frequency modified the association of breastfeeding duration and severe-ECC. Multiple imputation and censoring weights were used to address incomplete covariate information and missing outcomes, respectively. Confidence intervals (CI) were estimated using bootstrap re-sampling. Results Breastfeeding ≥24 months was associated with the highest adjusted population-average severe-ECC prevalence (0.45, 95% CI: 0.36, 0.54) compared with breastfeeding <6 months (0.22, 95% CI: 0.15, 0.28), 6–11 months (0.38, 95% CI: 0.25, 0.53), or 12–23 months (0.39, 95% CI: 0.20, 0.56). High frequency breastfeeding enhanced the association between long-duration breastfeeding and caries (excess prevalence due to interaction: 0.13, 80% CI: −0.03, 0.30). Conclusions In this population, breastfeeding ≥24 months, particularly if frequent, was associated with severe-ECC. Dental health should be one consideration, among many, in evaluating health outcomes associated with breastfeeding ≥24 months. PMID:24636616

Childhood maltreatment severity and alcohol use in adult psychiatric inpatients: The mediating role of emotion regulation difficulties.

PubMed

Dutcher, Christina D; Vujanovic, Anka A; Paulus, Daniel J; Bartlett, Brooke A

2017-09-01

Emotion regulation difficulties are a potentially key mechanism underlying the association between childhood maltreatment and alcohol use in adulthood. The current study examined the mediating role of emotion regulation difficulties in the association between childhood maltreatment severity (i.e., Childhood Trauma Questionnaire total score) and past-month alcohol use severity, including alcohol consumption frequency and alcohol-related problems (i.e., number of days of alcohol problems, ratings of "bother" caused by alcohol problems, ratings of treatment importance for alcohol problems). Participants included 111 acute-care psychiatric inpatients (45.0% female; Mage=33.5, SD=10.6), who reported at least one DSM-5 posttraumatic stress disorder Criterion A traumatic event, indexed via the Life Events Checklist for DSM-5. Participants completed questionnaires regarding childhood maltreatment, emotion regulation difficulties, and alcohol use. A significant indirect effect of childhood maltreatment severity via emotion regulation difficulties in relation to alcohol use severity (β=0.07, SE=0.04, 99% CI [0.01, 0.21]) was documented. Specifically, significant indirect effects were found for childhood maltreatment severity via emotion regulation difficulties in relation to alcohol problems (β's between 0.05 and 0.12; all 99% bootstrapped CIs with 10,000 resamples did not include 0) but not alcohol consumption. Emotion regulation difficulties may play a significant role in the association between childhood maltreatment severity and alcohol outcomes. Clinical implications are discussed. Copyright © 2017 Elsevier Inc. All rights reserved.

Novel method to construct large-scale design space in lubrication process utilizing Bayesian estimation based on a small-scale design-of-experiment and small sets of large-scale manufacturing data.

PubMed

Maeda, Jin; Suzuki, Tatsuya; Takayama, Kozo

2012-12-01

A large-scale design space was constructed using a Bayesian estimation method with a small-scale design of experiments (DoE) and small sets of large-scale manufacturing data without enforcing a large-scale DoE. The small-scale DoE was conducted using various Froude numbers (X(1)) and blending times (X(2)) in the lubricant blending process for theophylline tablets. The response surfaces, design space, and their reliability of the compression rate of the powder mixture (Y(1)), tablet hardness (Y(2)), and dissolution rate (Y(3)) on a small scale were calculated using multivariate spline interpolation, a bootstrap resampling technique, and self-organizing map clustering. The constant Froude number was applied as a scale-up rule. Three experiments under an optimal condition and two experiments under other conditions were performed on a large scale. The response surfaces on the small scale were corrected to those on a large scale by Bayesian estimation using the large-scale results. Large-scale experiments under three additional sets of conditions showed that the corrected design space was more reliable than that on the small scale, even if there was some discrepancy in the pharmaceutical quality between the manufacturing scales. This approach is useful for setting up a design space in pharmaceutical development when a DoE cannot be performed at a commercial large manufacturing scale.

Genetic stock identification of immature chum salmon ( Oncorhynchus keta) in the western Bering Sea, 2004

NASA Astrophysics Data System (ADS)

Kang, Minho; Kim, Suam; Low, Loh-Lee

2016-03-01

Genetic stock identification studies have been widely applied to Pacific salmon species to estimate stock composition of complex mixed-stock fisheries. In a September-October 2004 survey, 739 chum salmon ( Oncorhynchus keta) specimens were collected from 23 stations in the western Bering Sea. We determined the genetic stock composition of immature chum salmon based on the previous mitochondria DNA baseline. Each regional estimate was computed based on the conditional maximum likelihood method using 1,000 bootstrap resampling and then pooled to the major regional groups: Korea - Japan - Primorie (KJP) / Russia (RU) / Northwest Alaska (NWA) / Alaska Peninsula - Southcentral Alaska - Southeast Alaska - British Columbia - Washington (ONA). The stock composition of immature chum salmon in the western Bering Sea was a mix of 0.424 KJP, 0.421 RU, 0.116 NWA, and 0.039 ONA stocks. During the study period, the contribution of Asian chum salmon stocks gradually changed from RU to KJP stock. In addition, North American populations from NWA and ONA were small but present near the vicinity of the Russian coast and the Commander Islands, suggesting that the study areas in the western Bering Sea were an important migration route for Pacific chum salmon originating both from Asia and North America during the months of September and October. These results make it possible to better understand the chum salmon stock composition of the mixed-stock fisheries in the western Bering Sea and the stock-specific distribution pattern of chum salmon on the high-seas.

Internet use, social networks, loneliness, and quality of life among adults aged 50 and older: mediating and moderating effects.

PubMed

Khalaila, Rabia; Vitman-Schorr, Adi

2018-02-01

The increase in longevity of people on one hand, and on the other hand the fact that the social networks in later life become increasingly narrower, highlights the importance of Internet use to enhance quality of life (QoL). However, whether Internet use increases or decreases social networks, loneliness, and quality of life is not clear-cut. To explore the direct and/or indirect effects of Internet use on QoL, and to examine whether ethnicity and time the elderly spent with family moderate the mediation effect of Internet use on quality of life throughout loneliness. This descriptive-correlational study was carried out in 2016 by structured interviews with a convenience sample of 502 respondents aged 50 and older, living in northern Israel. Bootstrapping with resampling strategies was used for testing mediation a model. Use of the Internet was found to be positively associated with QoL. However, this relationship was mediated by loneliness, and moderated by the time the elderly spent with family members. In addition, respondents' ethnicity significantly moderated the mediation effect between Internet use and loneliness. Internet use can enhance QoL of older adults directly or indirectly by reducing loneliness. However, these effects are conditional on other variables. The indirect effect moderated by ethnicity, and the direct effect moderated by the time the elderly spend with their families. Researchers and practitioners should be aware of these interactions which can impact loneliness and quality of life of older persons differently.

Complex relationship between seasonal streamflow forecast skill and value in reservoir operations

DOE PAGES

Turner, Sean W. D.; Bennett, James C.; Robertson, David E.; ...

2017-09-28

Considerable research effort has recently been directed at improving and operationalising ensemble seasonal streamflow forecasts. Whilst this creates new opportunities for improving the performance of water resources systems, there may also be associated risks. Here, we explore these potential risks by examining the sensitivity of forecast value (improvement in system performance brought about by adopting forecasts) to changes in the forecast skill for a range of hypothetical reservoir designs with contrasting operating objectives. Forecast-informed operations are simulated using rolling horizon, adaptive control and then benchmarked against optimised control rules to assess performance improvements. Results show that there exists a strongmore » relationship between forecast skill and value for systems operated to maintain a target water level. But this relationship breaks down when the reservoir is operated to satisfy a target demand for water; good forecast accuracy does not necessarily translate into performance improvement. We show that the primary cause of this behaviour is the buffering role played by storage in water supply reservoirs, which renders the forecast superfluous for long periods of the operation. System performance depends primarily on forecast accuracy when critical decisions are made – namely during severe drought. As it is not possible to know in advance if a forecast will perform well at such moments, we advocate measuring the consistency of forecast performance, through bootstrap resampling, to indicate potential usefulness in storage operations. Our results highlight the need for sensitivity assessment in value-of-forecast studies involving reservoirs with supply objectives.« less

Gridless, pattern-driven point cloud completion and extension

NASA Astrophysics Data System (ADS)

Gravey, Mathieu; Mariethoz, Gregoire

2016-04-01

While satellites offer Earth observation with a wide coverage, other remote sensing techniques such as terrestrial LiDAR can acquire very high-resolution data on an area that is limited in extension and often discontinuous due to shadow effects. Here we propose a numerical approach to merge these two types of information, thereby reconstructing high-resolution data on a continuous large area. It is based on a pattern matching process that completes the areas where only low-resolution data is available, using bootstrapped high-resolution patterns. Currently, the most common approach to pattern matching is to interpolate the point data on a grid. While this approach is computationally efficient, it presents major drawbacks for point clouds processing because a significant part of the information is lost in the point-to-grid resampling, and that a prohibitive amount of memory is needed to store large grids. To address these issues, we propose a gridless method that compares point clouds subsets without the need to use a grid. On-the-fly interpolation involves a heavy computational load, which is met by using a GPU high-optimized implementation and a hierarchical pattern searching strategy. The method is illustrated using data from the Val d'Arolla, Swiss Alps, where high-resolution terrestrial LiDAR data are fused with lower-resolution Landsat and WorldView-3 acquisitions, such that the density of points is homogeneized (data completion) and that it is extend to a larger area (data extension).

Design space construction of multiple dose-strength tablets utilizing bayesian estimation based on one set of design-of-experiments.

PubMed

Maeda, Jin; Suzuki, Tatsuya; Takayama, Kozo

2012-01-01

Design spaces for multiple dose strengths of tablets were constructed using a Bayesian estimation method with one set of design of experiments (DoE) of only the highest dose-strength tablet. The lubricant blending process for theophylline tablets with dose strengths of 100, 50, and 25 mg is used as a model manufacturing process in order to construct design spaces. The DoE was conducted using various Froude numbers (X(1)) and blending times (X(2)) for theophylline 100-mg tablet. The response surfaces, design space, and their reliability of the compression rate of the powder mixture (Y(1)), tablet hardness (Y(2)), and dissolution rate (Y(3)) of the 100-mg tablet were calculated using multivariate spline interpolation, a bootstrap resampling technique, and self-organizing map clustering. Three experiments under an optimal condition and two experiments under other conditions were performed using 50- and 25-mg tablets, respectively. The response surfaces of the highest-strength tablet were corrected to those of the lower-strength tablets by Bayesian estimation using the manufacturing data of the lower-strength tablets. Experiments under three additional sets of conditions of lower-strength tablets showed that the corrected design space made it possible to predict the quality of lower-strength tablets more precisely than the design space of the highest-strength tablet. This approach is useful for constructing design spaces of tablets with multiple strengths.

Tumor gene expression and prognosis in breast cancer patients with 10 or more positive lymph nodes.

PubMed

Cobleigh, Melody A; Tabesh, Bita; Bitterman, Pincas; Baker, Joffre; Cronin, Maureen; Liu, Mei-Lan; Borchik, Russell; Mosquera, Juan-Miguel; Walker, Michael G; Shak, Steven

2005-12-15

This study, along with two others, was done to develop the 21-gene Recurrence Score assay (Oncotype DX) that was validated in a subsequent independent study and is used to aid decision making about chemotherapy in estrogen receptor (ER)-positive, node-negative breast cancer patients. Patients with >or=10 nodes diagnosed from 1979 to 1999 were identified. RNA was extracted from paraffin blocks, and expression of 203 candidate genes was quantified using reverse transcription-PCR (RT-PCR). Seventy-eight patients were studied. As of August 2002, 77% of patients had distant recurrence or breast cancer death. Univariate Cox analysis of clinical and immunohistochemistry variables indicated that HER2/immunohistochemistry, number of involved nodes, progesterone receptor (PR)/immunohistochemistry (% cells), and ER/immunohistochemistry (% cells) were significantly associated with distant recurrence-free survival (DRFS). Univariate Cox analysis identified 22 genes associated with DRFS. Higher expression correlated with shorter DRFS for the HER2 adaptor GRB7 and the macrophage marker CD68. Higher expression correlated with longer DRFS for tumor protein p53-binding protein 2 (TP53BP2) and the ER axis genes PR and Bcl2. Multivariate methods, including stepwise variable selection and bootstrap resampling of the Cox proportional hazards regression model, identified several genes, including TP53BP2 and Bcl2, as significant predictors of DRFS. Tumor gene expression profiles of archival tissues, some more than 20 years old, provide significant information about risk of distant recurrence even among patients with 10 or more nodes.

Environmental and Hydroclimatic Sensitivities of Greenhouse Gas (GHG) Fluxes from Coastal Wetlands

NASA Astrophysics Data System (ADS)

Abdul-Aziz, O. I.; Ishtiaq, K. S.

2016-12-01

We computed the reference environmental and hydroclimatic sensitivities of the greenhouse gas (GHG) fluxes (CO2 and CH4) from coastal salt marshes. Non-linear partial least squares regression models of CO2 (net uptake) and CH4 (net emissions) fluxes were developed with a bootstrap resampling approach using the photosynthetically active radiation (PAR), air and soil temperatures, water height, soil moisture, porewater salinity, and pH as predictors. Analytical sensitivity coefficients of different predictors were then analytically derived from the estimated models. The numerical sensitivities of the dominant drivers were determined by perturbing the variables individually and simultaneously to compute their individual and combined (respectively) effects on the GHG fluxes. Four tidal wetlands of Waquoit Bay, MA — incorporating a gradient in land-use, salinity and hydrology — were considered as the case study sites. The wetlands were dominated by native Spartina Alterniflora, and characterized by high salinity and frequent flooding. Results indicated a high sensitivity of CO2 fluxes to temperature and PAR, a moderate sensitivity to soil salinity and water height, and a weak sensitivity to pH and soil moisture. In contrast, the CH4 fluxes were more sensitive to temperature and salinity, compared to that of PAR, pH, and hydrologic variables. The estimated sensitivities and mechanistic insights can aid the management of coastal carbon under a changing climate and environment. The sensitivity coefficients also indicated the most dominant drivers of GHG fluxes for the development of a parsimonious predictive model.

Comparing two correlated C indices with right-censored survival outcome: a one-shot nonparametric approach.

PubMed

Kang, Le; Chen, Weijie; Petrick, Nicholas A; Gallas, Brandon D

2015-02-20

The area under the receiver operating characteristic curve is often used as a summary index of the diagnostic ability in evaluating biomarkers when the clinical outcome (truth) is binary. When the clinical outcome is right-censored survival time, the C index, motivated as an extension of area under the receiver operating characteristic curve, has been proposed by Harrell as a measure of concordance between a predictive biomarker and the right-censored survival outcome. In this work, we investigate methods for statistical comparison of two diagnostic or predictive systems, of which they could either be two biomarkers or two fixed algorithms, in terms of their C indices. We adopt a U-statistics-based C estimator that is asymptotically normal and develop a nonparametric analytical approach to estimate the variance of the C estimator and the covariance of two C estimators. A z-score test is then constructed to compare the two C indices. We validate our one-shot nonparametric method via simulation studies in terms of the type I error rate and power. We also compare our one-shot method with resampling methods including the jackknife and the bootstrap. Simulation results show that the proposed one-shot method provides almost unbiased variance estimations and has satisfactory type I error control and power. Finally, we illustrate the use of the proposed method with an example from the Framingham Heart Study. Copyright © 2014 John Wiley & Sons, Ltd.

Reduced ion bootstrap current drive on NTM instability

NASA Astrophysics Data System (ADS)

Qu, Hongpeng; Wang, Feng; Wang, Aike; Peng, Xiaodong; Li, Jiquan

2018-05-01

The loss of bootstrap current inside magnetic island plays a dominant role in driving the neoclassical tearing mode (NTM) instability in tokamak plasmas. In this work, we investigate the finite-banana-width (FBW) effect on the profile of ion bootstrap current in the island vicinity via an analytical approach. The results show that even if the pressure gradient vanishes inside the island, the ion bootstrap current can partly survive due to the FBW effect. The efficiency of the FBW effect is higher when the island width becomes smaller. Nevertheless, even when the island width is comparable to the ion FBW, the unperturbed ion bootstrap current inside the island cannot be largely recovered by the FBW effect, and thus the current loss still exists. This suggests that FBW effect alone cannot dramatically reduce the ion bootstrap current drive on NTMs.

Bootstrap Percolation on Homogeneous Trees Has 2 Phase Transitions

NASA Astrophysics Data System (ADS)

Fontes, L. R. G.; Schonmann, R. H.

2008-09-01

We study the threshold θ bootstrap percolation model on the homogeneous tree with degree b+1, 2≤ θ≤ b, and initial density p. It is known that there exists a nontrivial critical value for p, which we call p f , such that a) for p> p f , the final bootstrapped configuration is fully occupied for almost every initial configuration, and b) if p< p f , then for almost every initial configuration, the final bootstrapped configuration has density of occupied vertices less than 1. In this paper, we establish the existence of a distinct critical value for p, p c , such that 0< p c < p f , with the following properties: 1) if p≤ p c , then for almost every initial configuration there is no infinite cluster of occupied vertices in the final bootstrapped configuration; 2) if p> p c , then for almost every initial configuration there are infinite clusters of occupied vertices in the final bootstrapped configuration. Moreover, we show that 3) for p< p c , the distribution of the occupied cluster size in the final bootstrapped configuration has an exponential tail; 4) at p= p c , the expected occupied cluster size in the final bootstrapped configuration is infinite; 5) the probability of percolation of occupied vertices in the final bootstrapped configuration is continuous on [0, p f ] and analytic on ( p c , p f ), admitting an analytic continuation from the right at p c and, only in the case θ= b, also from the left at p f .

On the nature of data collection for soft-tissue image-to-physical organ registration: a noise characterization study

NASA Astrophysics Data System (ADS)

Collins, Jarrod A.; Heiselman, Jon S.; Weis, Jared A.; Clements, Logan W.; Simpson, Amber L.; Jarnagin, William R.; Miga, Michael I.

2017-03-01

In image-guided liver surgery (IGLS), sparse representations of the anterior organ surface may be collected intraoperatively to drive image-to-physical space registration. Soft tissue deformation represents a significant source of error for IGLS techniques. This work investigates the impact of surface data quality on current surface based IGLS registration methods. In this work, we characterize the robustness of our IGLS registration methods to noise in organ surface digitization. We study this within a novel human-to-phantom data framework that allows a rapid evaluation of clinically realistic data and noise patterns on a fully characterized hepatic deformation phantom. Additionally, we implement a surface data resampling strategy that is designed to decrease the impact of differences in surface acquisition. For this analysis, n=5 cases of clinical intraoperative data consisting of organ surface and salient feature digitizations from open liver resection were collected and analyzed within our human-to-phantom validation framework. As expected, results indicate that increasing levels of noise in surface acquisition cause registration fidelity to deteriorate. With respect to rigid registration using the raw and resampled data at clinically realistic levels of noise (i.e. a magnitude of 1.5 mm), resampling improved TRE by 21%. In terms of nonrigid registration, registrations using resampled data outperformed the raw data result by 14% at clinically realistic levels and were less susceptible to noise across the range of noise investigated. These results demonstrate the types of analyses our novel human-to-phantom validation framework can provide and indicate the considerable benefits of resampling strategies.

An optical systems analysis approach to image resampling

NASA Technical Reports Server (NTRS)

Lyon, Richard G.

1997-01-01

All types of image registration require some type of resampling, either during the registration or as a final step in the registration process. Thus the image(s) must be regridded into a spatially uniform, or angularly uniform, coordinate system with some pre-defined resolution. Frequently the ending resolution is not the resolution at which the data was observed with. The registration algorithm designer and end product user are presented with a multitude of possible resampling methods each of which modify the spatial frequency content of the data in some way. The purpose of this paper is threefold: (1) to show how an imaging system modifies the scene from an end to end optical systems analysis approach, (2) to develop a generalized resampling model, and (3) empirically apply the model to simulated radiometric scene data and tabulate the results. A Hanning windowed sinc interpolator method will be developed based upon the optical characterization of the system. It will be discussed in terms of the effects and limitations of sampling, aliasing, spectral leakage, and computational complexity. Simulated radiometric scene data will be used to demonstrate each of the algorithms. A high resolution scene will be "grown" using a fractal growth algorithm based on mid-point recursion techniques. The result scene data will be convolved with a point spread function representing the optical response. The resultant scene will be convolved with the detection systems response and subsampled to the desired resolution. The resultant data product will be subsequently resampled to the correct grid using the Hanning windowed sinc interpolator and the results and errors tabulated and discussed.

Surface Fitting for Quasi Scattered Data from Coordinate Measuring Systems.

PubMed

Mao, Qing; Liu, Shugui; Wang, Sen; Ma, Xinhui

2018-01-13

Non-uniform rational B-spline (NURBS) surface fitting from data points is wildly used in the fields of computer aided design (CAD), medical imaging, cultural relic representation and object-shape detection. Usually, the measured data acquired from coordinate measuring systems is neither gridded nor completely scattered. The distribution of this kind of data is scattered in physical space, but the data points are stored in a way consistent with the order of measurement, so it is named quasi scattered data in this paper. Therefore they can be organized into rows easily but the number of points in each row is random. In order to overcome the difficulty of surface fitting from this kind of data, a new method based on resampling is proposed. It consists of three major steps: (1) NURBS curve fitting for each row, (2) resampling on the fitted curve and (3) surface fitting from the resampled data. Iterative projection optimization scheme is applied in the first and third step to yield advisable parameterization and reduce the time cost of projection. A resampling approach based on parameters, local peaks and contour curvature is proposed to overcome the problems of nodes redundancy and high time consumption in the fitting of this kind of scattered data. Numerical experiments are conducted with both simulation and practical data, and the results show that the proposed method is fast, effective and robust. What's more, by analyzing the fitting results acquired form data with different degrees of scatterness it can be demonstrated that the error introduced by resampling is negligible and therefore it is feasible.

Factors predicting aggressiveness of non-hypervascular hepatic nodules detected on hepatobiliary phase of gadolinium ethoxybenzyl diethylene-triamine-pentaacetic-acid magnetic resonance imaging.

PubMed

Kanefuji, Tsutomu; Takano, Toru; Suda, Takeshi; Akazawa, Kouhei; Yokoo, Takeshi; Kamimura, Hiroteru; Kamimura, Kenya; Tsuchiya, Atsunori; Takamura, Masaaki; Kawai, Hirokazu; Yamagiwa, Satoshi; Aoyama, Hidefumi; Nomoto, Minoru; Terai, Shuji

2015-04-21

To establish a prognostic formula that distinguishes non-hypervascular hepatic nodules (NHNs) with higher aggressiveness from less hazardous one. Seventy-three NHNs were detected in gadolinium ethoxybenzyl diethylene-triamine-pentaacetic-acid magnetic resonance imaging (Gd-EOB-DTPA-MRI) study and confirmed to change 2 mm or more in size and/or to gain hypervascularity. All images were interpreted independently by an experienced, board-certified abdominal radiologist and hepatologist; both knew that the patients were at risk for hepatocellular carcinoma development but were blinded to the clinical information. A formula predicting NHN destiny was developed using a generalized estimating equation model with thirteen explanatory variables: age, gender, background liver diseases, Child-Pugh class, NHN diameter, T1-weighted imaging/T2-weighted imaging detectability, fat deposition, lower signal intensity in arterial phase, lower signal intensity in equilibrium phase, α-fetoprotein, des-γ-carboxy prothrombin, α-fetoprotein-L3, and coexistence of classical hepatocellular carcinoma. The accuracy of the formula was validated in bootstrap samples that were created by resampling of 1000 iterations. During a median follow-up period of 504 d, 73 NHNs with a median diameter of 9 mm (interquartile range: 8-12 mm) grew or shrank by 68.5% (fifty nodules) or 20.5% (fifteen nodules), respectively, whereas hypervascularity developed in 38.4% (twenty eight nodules). In the fifteen shrank nodules, twelve nodules disappeared, while 11.0% (eight nodules) were stable in size but acquired vascularity. A generalized estimating equation analysis selected five explanatories from the thirteen variables as significant factors to predict NHN progression. The estimated regression coefficients were 0.36 for age, 6.51 for lower signal intensity in arterial phase, 8.70 or 6.03 for positivity of hepatitis B virus or hepatitis C virus, 9.37 for des-γ-carboxy prothrombin, and -4.05 for fat deposition. A formula incorporating the five coefficients revealed sensitivity, specificity, and accuracy of 88.0%, 86.7%, and 87.7% in the formulating cohort, whereas these of 87.2% ± 5.7%, 83.8% ± 13.6%, and 87.3% ± 4.5% in the bootstrap samples. These data suggest that the formula helps Gd-EOB-DTPA-MRI detect a trend toward hepatocyte transformation by predicting NHN destiny.

Prediction of outcome following early salvage radiotherapy among patients with biochemical recurrence after radical prostatectomy.

PubMed

Briganti, Alberto; Karnes, R Jeffrey; Joniau, Steven; Boorjian, Stephen A; Cozzarini, Cesare; Gandaglia, Giorgio; Hinkelbein, Wolfgang; Haustermans, Karin; Tombal, Bertrand; Shariat, Shahrokh; Sun, Maxine; Karakiewicz, Pierre I; Montorsi, Francesco; Van Poppel, Hein; Wiegel, Thomas

2014-09-01

Early salvage radiotherapy (eSRT) represents the only curative option for prostate cancer patients experiencing biochemical recurrence (BCR) for local recurrence after radical prostatectomy (RP). To develop and internally validate a novel nomogram predicting BCR after eSRT in patients treated with RP. Using a multi-institutional cohort, 472 node-negative patients who experienced BCR after RP were identified. All patients received eSRT, defined as local radiation to the prostate and seminal vesicle bed, delivered at prostate-specific antigen (PSA) ≤ 0.5 ng/ml. BCR after eSRT was defined as two consecutive PSA values ≥ 0.2 ng/ml. Uni- and multivariable Cox regression models predicting BCR after eSRT were fitted. Regression-based coefficients were used to develop a nomogram predicting the risk of 5-yr BCR after eSRT. The discrimination of the nomogram was quantified with the Harrell concordance index and the calibration plot method. Two hundred bootstrap resamples were used for internal validation. Mean follow-up was 58 mo (median: 48 mo). Overall, 5-yr BCR-free survival rate after eSRT was 73.4%. In univariable analyses, pathologic stage, Gleason score, and positive surgical margins were associated with the risk of BCR after eSRT (all p ≤ 0.04). These results were confirmed in multivariable analysis, where all the previously mentioned covariates as well as pre-RT PSA were significantly associated with BCR after eSRT (all p ≤ 0.04). A coefficient-based nomogram demonstrated a bootstrap-corrected discrimination of 0.74. Our study is limited by its retrospective nature and use of BCR as an end point. eSRT leads to excellent cancer control in patients with BCR for presumed local failure after RP. We developed the first nomogram to predict outcome after eSRT. Our model facilitates risk stratification and patient counselling regarding the use of secondary therapy for individuals experiencing BCR after RP. Salvage radiotherapy leads to optimal cancer control in patients who experience recurrence after radical prostatectomy. We developed a novel tool to identify the best candidates for salvage treatment and to allow selection of patients to be considered for additional forms of therapy. Copyright © 2013 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Accelerated spike resampling for accurate multiple testing controls.

PubMed

Harrison, Matthew T

2013-02-01

Controlling for multiple hypothesis tests using standard spike resampling techniques often requires prohibitive amounts of computation. Importance sampling techniques can be used to accelerate the computation. The general theory is presented, along with specific examples for testing differences across conditions using permutation tests and for testing pairwise synchrony and precise lagged-correlation between many simultaneously recorded spike trains using interval jitter.

Cellular neural network-based hybrid approach toward automatic image registration

NASA Astrophysics Data System (ADS)

Arun, Pattathal VijayaKumar; Katiyar, Sunil Kumar

2013-01-01

Image registration is a key component of various image processing operations that involve the analysis of different image data sets. Automatic image registration domains have witnessed the application of many intelligent methodologies over the past decade; however, inability to properly model object shape as well as contextual information has limited the attainable accuracy. A framework for accurate feature shape modeling and adaptive resampling using advanced techniques such as vector machines, cellular neural network (CNN), scale invariant feature transform (SIFT), coreset, and cellular automata is proposed. CNN has been found to be effective in improving feature matching as well as resampling stages of registration and complexity of the approach has been considerably reduced using coreset optimization. The salient features of this work are cellular neural network approach-based SIFT feature point optimization, adaptive resampling, and intelligent object modelling. Developed methodology has been compared with contemporary methods using different statistical measures. Investigations over various satellite images revealed that considerable success was achieved with the approach. This system has dynamically used spectral and spatial information for representing contextual knowledge using CNN-prolog approach. This methodology is also illustrated to be effective in providing intelligent interpretation and adaptive resampling.

Partition resampling and extrapolation averaging: approximation methods for quantifying gene expression in large numbers of short oligonucleotide arrays.

PubMed

Goldstein, Darlene R

2006-10-01

Studies of gene expression using high-density short oligonucleotide arrays have become a standard in a variety of biological contexts. Of the expression measures that have been proposed to quantify expression in these arrays, multi-chip-based measures have been shown to perform well. As gene expression studies increase in size, however, utilizing multi-chip expression measures is more challenging in terms of computing memory requirements and time. A strategic alternative to exact multi-chip quantification on a full large chip set is to approximate expression values based on subsets of chips. This paper introduces an extrapolation method, Extrapolation Averaging (EA), and a resampling method, Partition Resampling (PR), to approximate expression in large studies. An examination of properties indicates that subset-based methods can perform well compared with exact expression quantification. The focus is on short oligonucleotide chips, but the same ideas apply equally well to any array type for which expression is quantified using an entire set of arrays, rather than for only a single array at a time. Software implementing Partition Resampling and Extrapolation Averaging is under development as an R package for the BioConductor project.

Visuospatial bootstrapping: implicit binding of verbal working memory to visuospatial representations in children and adults.

PubMed

Darling, Stephen; Parker, Mary-Jane; Goodall, Karen E; Havelka, Jelena; Allen, Richard J

2014-03-01

When participants carry out visually presented digit serial recall, their performance is better if they are given the opportunity to encode extra visuospatial information at encoding-a phenomenon that has been termed visuospatial bootstrapping. This bootstrapping is the result of integration of information from different modality-specific short-term memory systems and visuospatial knowledge in long term memory, and it can be understood in the context of recent models of working memory that address multimodal binding (e.g., models incorporating an episodic buffer). Here we report a cross-sectional developmental study that demonstrated visuospatial bootstrapping in adults (n=18) and 9-year-old children (n=15) but not in 6-year-old children (n=18). This is the first developmental study addressing visuospatial bootstrapping, and results demonstrate that the developmental trajectory of bootstrapping is different from that of basic verbal and visuospatial working memory. This pattern suggests that bootstrapping (and hence integrative functions such as those associated with the episodic buffer) emerge independent of the development of basic working memory slave systems during childhood. Copyright © 2013 Elsevier Inc. All rights reserved.

A bootstrap based space-time surveillance model with an application to crime occurrences

NASA Astrophysics Data System (ADS)

Kim, Youngho; O'Kelly, Morton

2008-06-01

This study proposes a bootstrap-based space-time surveillance model. Designed to find emerging hotspots in near-real time, the bootstrap based model is characterized by its use of past occurrence information and bootstrap permutations. Many existing space-time surveillance methods, using population at risk data to generate expected values, have resulting hotspots bounded by administrative area units and are of limited use for near-real time applications because of the population data needed. However, this study generates expected values for local hotspots from past occurrences rather than population at risk. Also, bootstrap permutations of previous occurrences are used for significant tests. Consequently, the bootstrap-based model, without the requirement of population at risk data, (1) is free from administrative area restriction, (2) enables more frequent surveillance for continuously updated registry database, and (3) is readily applicable to criminology and epidemiology surveillance. The bootstrap-based model performs better for space-time surveillance than the space-time scan statistic. This is shown by means of simulations and an application to residential crime occurrences in Columbus, OH, year 2000.

Pesticides in Wyoming Groundwater, 2008-10

USGS Publications Warehouse

Eddy-Miller, Cheryl A.; Bartos, Timothy T.; Taylor, Michelle L.

2013-01-01

Groundwater samples were collected from 296 wells during 1995-2006 as part of a baseline study of pesticides in Wyoming groundwater. In 2009, a previous report summarized the results of the baseline sampling and the statistical evaluation of the occurrence of pesticides in relation to selected natural and anthropogenic (human-related) characteristics. During 2008-10, the U.S. Geological Survey, in cooperation with the Wyoming Department of Agriculture, resampled a subset (52) of the 296 wells sampled during 1995-2006 baseline study in order to compare detected compounds and respective concentrations between the two sampling periods and to evaluate the detections of new compounds. The 52 wells were distributed similarly to sites used in the 1995-2006 baseline study with respect to geographic area and land use within the geographic area of interest. Because of the use of different types of reporting levels and variability in reporting-level values during both the 1995-2006 baseline study and the 2008-10 resampling study, analytical results received from the laboratory were recensored. Two levels of recensoring were used to compare pesticides—a compound-specific assessment level (CSAL) that differed by compound and a common assessment level (CAL) of 0.07 microgram per liter. The recensoring techniques and values used for both studies, with the exception of the pesticide 2,4-D methyl ester, were the same. Twenty-eight different pesticides were detected in samples from the 52 wells during the 2008-10 resampling study. Pesticide concentrations were compared with several U.S. Environmental Protection Agency drinking-water standards or health advisories for finished (treated) water established under the Safe Drinking Water Act. All detected pesticides were measured at concentrations smaller than U.S. Environmental Protection Agency drinking-water standards or health advisories where applicable (many pesticides did not have standards or advisories). One or more pesticides were detected at concentrations greater than the CAL in water from 16 of 52 wells sampled (about 31 percent) during the resampling study. Detected pesticides were classified into one of six types: herbicides, herbicide degradates, insecticides, insecticide degradates, fungicides, or fungicide degradates. At least 95 percent of detected pesticides were classified as herbicides or herbicide degradates. The number of different pesticides detected in samples from the 52 wells was similar between the 1995-2006 baseline study (30 different pesticides) and 2008-2010 resampling study (28 different pesticides). Thirteen pesticides were detected during both studies. The change in the number of pesticides detected (without regard to which pesticide was detected) in groundwater samples from each of the 52 wells was evaluated and the number of pesticides detected in groundwater did not change for most of the wells (32). Of those that did have a difference between the two studies, 17 wells had more pesticide detections in groundwater during the 1995-2006 baseline study, whereas only 3 wells had more detections during the 2008-2010 resampling study. The difference in pesticide concentrations in groundwater samples from each of the 52 wells was determined. Few changes in concentration between the 1995-2006 baseline study and the 2008-2010 resampling study were seen for most detected pesticides. Seven pesticides had a greater concentration detected in the groundwater from the same well during the baseline sampling compared to the resampling study. Concentrations of prometon, which was detected in 17 wells, were greater in the baseline study sample compared to the resampling study sample from the same well 100 percent of the time. The change in the number of pesticides detected (without regard to which pesticide was detected) in groundwater samples from each of the 52 wells with respect to land use and geographic area was calculated. All wells with land use classified as agricultural had the same or a smaller number of pesticides detected in the resampling study compared to the baseline study. All wells in the Bighorn Basin geographic area also had the same or a smaller number of pesticides detected in the resampling study compared to the baseline study.

Experimental study of digital image processing techniques for LANDSAT data

NASA Technical Reports Server (NTRS)

Rifman, S. S. (Principal Investigator); Allendoerfer, W. B.; Caron, R. H.; Pemberton, L. J.; Mckinnon, D. M.; Polanski, G.; Simon, K. W.

1976-01-01

The author has identified the following significant results. Results are reported for: (1) subscene registration, (2) full scene rectification and registration, (3) resampling techniques, (4) and ground control point (GCP) extraction. Subscenes (354 pixels x 234 lines) were registered to approximately 1/4 pixel accuracy and evaluated by change detection imagery for three cases: (1) bulk data registration, (2) precision correction of a reference subscene using GCP data, and (3) independently precision processed subscenes. Full scene rectification and registration results were evaluated by using a correlation technique to measure registration errors of 0.3 pixel rms thoughout the full scene. Resampling evaluations of nearest neighbor and TRW cubic convolution processed data included change detection imagery and feature classification. Resampled data were also evaluated for an MSS scene containing specular solar reflections.

An SAS Macro for Implementing the Modified Bollen-Stine Bootstrap for Missing Data: Implementing the Bootstrap Using Existing Structural Equation Modeling Software

ERIC Educational Resources Information Center

Enders, Craig K.

2005-01-01

The Bollen-Stine bootstrap can be used to correct for standard error and fit statistic bias that occurs in structural equation modeling (SEM) applications due to nonnormal data. The purpose of this article is to demonstrate the use of a custom SAS macro program that can be used to implement the Bollen-Stine bootstrap with existing SEM software.…

Coupling of PIES 3-D Equilibrium Code and NIFS Bootstrap Code with Applications to the Computation of Stellarator Equilibria

NASA Astrophysics Data System (ADS)

Monticello, D. A.; Reiman, A. H.; Watanabe, K. Y.; Nakajima, N.; Okamoto, M.

1997-11-01

The existence of bootstrap currents in both tokamaks and stellarators was confirmed, experimentally, more than ten years ago. Such currents can have significant effects on the equilibrium and stability of these MHD devices. In addition, stellarators, with the notable exception of W7-X, are predicted to have such large bootstrap currents that reliable equilibrium calculations require the self-consistent evaluation of bootstrap currents. Modeling of discharges which contain islands requires an algorithm that does not assume good surfaces. Only one of the two 3-D equilibrium codes that exist, PIES( Reiman, A. H., Greenside, H. S., Compt. Phys. Commun. 43), (1986)., can easily be modified to handle bootstrap current. Here we report on the coupling of the PIES 3-D equilibrium code and NIFS bootstrap code(Watanabe, K., et al., Nuclear Fusion 35) (1995), 335.

Development of a copula-based particle filter (CopPF) approach for hydrologic data assimilation under consideration of parameter interdependence

NASA Astrophysics Data System (ADS)

Fan, Y. R.; Huang, G. H.; Baetz, B. W.; Li, Y. P.; Huang, K.

2017-06-01

In this study, a copula-based particle filter (CopPF) approach was developed for sequential hydrological data assimilation by considering parameter correlation structures. In CopPF, multivariate copulas are proposed to reflect parameter interdependence before the resampling procedure with new particles then being sampled from the obtained copulas. Such a process can overcome both particle degeneration and sample impoverishment. The applicability of CopPF is illustrated with three case studies using a two-parameter simplified model and two conceptual hydrologic models. The results for the simplified model indicate that model parameters are highly correlated in the data assimilation process, suggesting a demand for full description of their dependence structure. Synthetic experiments on hydrologic data assimilation indicate that CopPF can rejuvenate particle evolution in large spaces and thus achieve good performances with low sample size scenarios. The applicability of CopPF is further illustrated through two real-case studies. It is shown that, compared with traditional particle filter (PF) and particle Markov chain Monte Carlo (PMCMC) approaches, the proposed method can provide more accurate results for both deterministic and probabilistic prediction with a sample size of 100. Furthermore, the sample size would not significantly influence the performance of CopPF. Also, the copula resampling approach dominates parameter evolution in CopPF, with more than 50% of particles sampled by copulas in most sample size scenarios.

Application of microarray analysis on computer cluster and cloud platforms.

PubMed

Bernau, C; Boulesteix, A-L; Knaus, J

2013-01-01

Analysis of recent high-dimensional biological data tends to be computationally intensive as many common approaches such as resampling or permutation tests require the basic statistical analysis to be repeated many times. A crucial advantage of these methods is that they can be easily parallelized due to the computational independence of the resampling or permutation iterations, which has induced many statistics departments to establish their own computer clusters. An alternative is to rent computing resources in the cloud, e.g. at Amazon Web Services. In this article we analyze whether a selection of statistical projects, recently implemented at our department, can be efficiently realized on these cloud resources. Moreover, we illustrate an opportunity to combine computer cluster and cloud resources. In order to compare the efficiency of computer cluster and cloud implementations and their respective parallelizations we use microarray analysis procedures and compare their runtimes on the different platforms. Amazon Web Services provide various instance types which meet the particular needs of the different statistical projects we analyzed in this paper. Moreover, the network capacity is sufficient and the parallelization is comparable in efficiency to standard computer cluster implementations. Our results suggest that many statistical projects can be efficiently realized on cloud resources. It is important to mention, however, that workflows can change substantially as a result of a shift from computer cluster to cloud computing.

Application of the Bootstrap Methods in Factor Analysis.

ERIC Educational Resources Information Center

Ichikawa, Masanori; Konishi, Sadanori

1995-01-01

A Monte Carlo experiment was conducted to investigate the performance of bootstrap methods in normal theory maximum likelihood factor analysis when the distributional assumption was satisfied or unsatisfied. Problems arising with the use of bootstrap methods are highlighted. (SLD)

Quantitative trait Loci analysis using the false discovery rate.

PubMed

Benjamini, Yoav; Yekutieli, Daniel

2005-10-01

False discovery rate control has become an essential tool in any study that has a very large multiplicity problem. False discovery rate-controlling procedures have also been found to be very effective in QTL analysis, ensuring reproducible results with few falsely discovered linkages and offering increased power to discover QTL, although their acceptance has been slower than in microarray analysis, for example. The reason is partly because the methodological aspects of applying the false discovery rate to QTL mapping are not well developed. Our aim in this work is to lay a solid foundation for the use of the false discovery rate in QTL mapping. We review the false discovery rate criterion, the appropriate interpretation of the FDR, and alternative formulations of the FDR that appeared in the statistical and genetics literature. We discuss important features of the FDR approach, some stemming from new developments in FDR theory and methodology, which deem it especially useful in linkage analysis. We review false discovery rate-controlling procedures--the BH, the resampling procedure, and the adaptive two-stage procedure-and discuss the validity of these procedures in single- and multiple-trait QTL mapping. Finally we argue that the control of the false discovery rate has an important role in suggesting, indicating the significance of, and confirming QTL and present guidelines for its use.

Anisotropic scene geometry resampling with occlusion filling for 3DTV applications

NASA Astrophysics Data System (ADS)

Kim, Jangheon; Sikora, Thomas

2006-02-01

Image and video-based rendering technologies are receiving growing attention due to their photo-realistic rendering capability in free-viewpoint. However, two major limitations are ghosting and blurring due to their sampling-based mechanism. The scene geometry which supports to select accurate sampling positions is proposed using global method (i.e. approximate depth plane) and local method (i.e. disparity estimation). This paper focuses on the local method since it can yield more accurate rendering quality without large number of cameras. The local scene geometry has two difficulties which are the geometrical density and the uncovered area including hidden information. They are the serious drawback to reconstruct an arbitrary viewpoint without aliasing artifacts. To solve the problems, we propose anisotropic diffusive resampling method based on tensor theory. Isotropic low-pass filtering accomplishes anti-aliasing in scene geometry and anisotropic diffusion prevents filtering from blurring the visual structures. Apertures in coarse samples are estimated following diffusion on the pre-filtered space, the nonlinear weighting of gradient directions suppresses the amount of diffusion. Aliasing artifacts from low density are efficiently removed by isotropic filtering and the edge blurring can be solved by the anisotropic method at one process. Due to difference size of sampling gap, the resampling condition is defined considering causality between filter-scale and edge. Using partial differential equation (PDE) employing Gaussian scale-space, we iteratively achieve the coarse-to-fine resampling. In a large scale, apertures and uncovered holes can be overcoming because only strong and meaningful boundaries are selected on the resolution. The coarse-level resampling with a large scale is iteratively refined to get detail scene structure. Simulation results show the marked improvements of rendering quality.

A multistate dynamic site occupancy model for spatially aggregated sessile communities

USGS Publications Warehouse

Fukaya, Keiichi; Royle, J. Andrew; Okuda, Takehiro; Nakaoka, Masahiro; Noda, Takashi

2017-01-01

Estimation of transition probabilities of sessile communities seems easy in principle but may still be difficult in practice because resampling error (i.e. a failure to resample exactly the same location at fixed points) may cause significant estimation bias. Previous studies have developed novel analytical methods to correct for this estimation bias. However, they did not consider the local structure of community composition induced by the aggregated distribution of organisms that is typically observed in sessile assemblages and is very likely to affect observations.We developed a multistate dynamic site occupancy model to estimate transition probabilities that accounts for resampling errors associated with local community structure. The model applies a nonparametric multivariate kernel smoothing methodology to the latent occupancy component to estimate the local state composition near each observation point, which is assumed to determine the probability distribution of data conditional on the occurrence of resampling error.By using computer simulations, we confirmed that an observation process that depends on local community structure may bias inferences about transition probabilities. By applying the proposed model to a real data set of intertidal sessile communities, we also showed that estimates of transition probabilities and of the properties of community dynamics may differ considerably when spatial dependence is taken into account.Results suggest the importance of accounting for resampling error and local community structure for developing management plans that are based on Markovian models. Our approach provides a solution to this problem that is applicable to broad sessile communities. It can even accommodate an anisotropic spatial correlation of species composition, and may also serve as a basis for inferring complex nonlinear ecological dynamics.

Decadal Time Scale change in terrestrial plant communities in North American arctic and alpine tundra: A contribution to the International Polar Year Back to the Future Project (Invited)

NASA Astrophysics Data System (ADS)

Tweedie, C. E.; Ebert-May, D.; Hollister, R. D.; Johnson, D. R.; Lara, M. J.; Villarreal, S.; Spasojevic, M.; Webber, P.

2010-12-01

The International Polar Year-Back to the Future (IPY-BTF) is an endorsed International Polar Year project (IPY project #214). The overarching goal of this program is to determine how key structural and functional characteristics of high latitude/altitude terrestrial ecosystems have changed over the past 25 or more years and assess if such trajectories of change are likely to continue in the future. By rescuing data, revisiting, re-sampling historic research sites and assessing environmental change over time, we aim to provide greater understanding of how tundra is changing and what the possible drivers of these changes are. Resampling of sites established by Patrick J. Webber between 1964 and 1975 in northern Baffin Island, Northern Alaska and in the Rocky Mountains form a key contribution to the BTF project. Here we report on resampling efforts at each of these locations and initial results of a synthesis effort that finds similarities and differences in change between sites. Results suggest that although shifts in plant community composition are detectable at each location, the magnitude and direction of change differ among locations. Vegetation shifts along soil moisture gradients is apparent at most of the sites resampled. Interestingly, however, wet communities seem to have changed more than dry communities in the Arctic locations, while plant communities at the alpine site appear to be becoming more distinct regardless of soil moisture status. Ecosystem function studies performed in conjunction with plant community change suggest that there has been an increase in plant productivity at most sites resampled, especially in wet and mesic land cover types.

Bootstrap confidence levels for phylogenetic trees.

PubMed

Efron, B; Halloran, E; Holmes, S

1996-07-09

Evolutionary trees are often estimated from DNA or RNA sequence data. How much confidence should we have in the estimated trees? In 1985, Felsenstein [Felsenstein, J. (1985) Evolution 39, 783-791] suggested the use of the bootstrap to answer this question. Felsenstein's method, which in concept is a straightforward application of the bootstrap, is widely used, but has been criticized as biased in the genetics literature. This paper concerns the use of the bootstrap in the tree problem. We show that Felsenstein's method is not biased, but that it can be corrected to better agree with standard ideas of confidence levels and hypothesis testing. These corrections can be made by using the more elaborate bootstrap method presented here, at the expense of considerably more computation.

Coefficient Alpha Bootstrap Confidence Interval under Nonnormality

ERIC Educational Resources Information Center

Padilla, Miguel A.; Divers, Jasmin; Newton, Matthew

2012-01-01

Three different bootstrap methods for estimating confidence intervals (CIs) for coefficient alpha were investigated. In addition, the bootstrap methods were compared with the most promising coefficient alpha CI estimation methods reported in the literature. The CI methods were assessed through a Monte Carlo simulation utilizing conditions…

Spatial Quality Evaluation of Resampled Unmanned Aerial Vehicle-Imagery for Weed Mapping.

PubMed

Borra-Serrano, Irene; Peña, José Manuel; Torres-Sánchez, Jorge; Mesas-Carrascosa, Francisco Javier; López-Granados, Francisca

2015-08-12

Unmanned aerial vehicles (UAVs) combined with different spectral range sensors are an emerging technology for providing early weed maps for optimizing herbicide applications. Considering that weeds, at very early phenological stages, are similar spectrally and in appearance, three major components are relevant: spatial resolution, type of sensor and classification algorithm. Resampling is a technique to create a new version of an image with a different width and/or height in pixels, and it has been used in satellite imagery with different spatial and temporal resolutions. In this paper, the efficiency of resampled-images (RS-images) created from real UAV-images (UAV-images; the UAVs were equipped with two types of sensors, i.e., visible and visible plus near-infrared spectra) captured at different altitudes is examined to test the quality of the RS-image output. The performance of the object-based-image-analysis (OBIA) implemented for the early weed mapping using different weed thresholds was also evaluated. Our results showed that resampling accurately extracted the spectral values from high spatial resolution UAV-images at an altitude of 30 m and the RS-image data at altitudes of 60 and 100 m, was able to provide accurate weed cover and herbicide application maps compared with UAV-images from real flights.

Spatial Quality Evaluation of Resampled Unmanned Aerial Vehicle-Imagery for Weed Mapping

PubMed Central

Borra-Serrano, Irene; Peña, José Manuel; Torres-Sánchez, Jorge; Mesas-Carrascosa, Francisco Javier; López-Granados, Francisca

2015-01-01

Unmanned aerial vehicles (UAVs) combined with different spectral range sensors are an emerging technology for providing early weed maps for optimizing herbicide applications. Considering that weeds, at very early phenological stages, are similar spectrally and in appearance, three major components are relevant: spatial resolution, type of sensor and classification algorithm. Resampling is a technique to create a new version of an image with a different width and/or height in pixels, and it has been used in satellite imagery with different spatial and temporal resolutions. In this paper, the efficiency of resampled-images (RS-images) created from real UAV-images (UAV-images; the UAVs were equipped with two types of sensors, i.e., visible and visible plus near-infrared spectra) captured at different altitudes is examined to test the quality of the RS-image output. The performance of the object-based-image-analysis (OBIA) implemented for the early weed mapping using different weed thresholds was also evaluated. Our results showed that resampling accurately extracted the spectral values from high spatial resolution UAV-images at an altitude of 30 m and the RS-image data at altitudes of 60 and 100 m, was able to provide accurate weed cover and herbicide application maps compared with UAV-images from real flights. PMID:26274960

Model selection for semiparametric marginal mean regression accounting for within-cluster subsampling variability and informative cluster size.

PubMed

Shen, Chung-Wei; Chen, Yi-Hau

2018-03-13

We propose a model selection criterion for semiparametric marginal mean regression based on generalized estimating equations. The work is motivated by a longitudinal study on the physical frailty outcome in the elderly, where the cluster size, that is, the number of the observed outcomes in each subject, is "informative" in the sense that it is related to the frailty outcome itself. The new proposal, called Resampling Cluster Information Criterion (RCIC), is based on the resampling idea utilized in the within-cluster resampling method (Hoffman, Sen, and Weinberg, 2001, Biometrika 88, 1121-1134) and accommodates informative cluster size. The implementation of RCIC, however, is free of performing actual resampling of the data and hence is computationally convenient. Compared with the existing model selection methods for marginal mean regression, the RCIC method incorporates an additional component accounting for variability of the model over within-cluster subsampling, and leads to remarkable improvements in selecting the correct model, regardless of whether the cluster size is informative or not. Applying the RCIC method to the longitudinal frailty study, we identify being female, old age, low income and life satisfaction, and chronic health conditions as significant risk factors for physical frailty in the elderly. © 2018, The International Biometric Society.

Estimating source parameters from deformation data, with an application to the March 1997 earthquake swarm off the Izu Peninsula, Japan

NASA Astrophysics Data System (ADS)

Cervelli, P.; Murray, M. H.; Segall, P.; Aoki, Y.; Kato, T.

2001-06-01

We have applied two Monte Carlo optimization techniques, simulated annealing and random cost, to the inversion of deformation data for fault and magma chamber geometry. These techniques involve an element of randomness that permits them to escape local minima and ultimately converge to the global minimum of misfit space. We have tested the Monte Carlo algorithms on two synthetic data sets. We have also compared them to one another in terms of their efficiency and reliability. We have applied the bootstrap method to estimate confidence intervals for the source parameters, including the correlations inherent in the data. Additionally, we present methods that use the information from the bootstrapping procedure to visualize the correlations between the different model parameters. We have applied these techniques to GPS, tilt, and leveling data from the March 1997 earthquake swarm off of the Izu Peninsula, Japan. Using the two Monte Carlo algorithms, we have inferred two sources, a dike and a fault, that fit the deformation data and the patterns of seismicity and that are consistent with the regional stress field.

Bootstrap Estimates of Standard Errors in Generalizability Theory

ERIC Educational Resources Information Center

Tong, Ye; Brennan, Robert L.

2007-01-01

Estimating standard errors of estimated variance components has long been a challenging task in generalizability theory. Researchers have speculated about the potential applicability of the bootstrap for obtaining such estimates, but they have identified problems (especially bias) in using the bootstrap. Using Brennan's bias-correcting procedures…

A simple test of association for contingency tables with multiple column responses.

PubMed

Decady, Y J; Thomas, D R

2000-09-01

Loughin and Scherer (1998, Biometrics 54, 630-637) investigated tests of association in two-way tables when one of the categorical variables allows for multiple-category responses from individual respondents. Standard chi-squared tests are invalid in this case, and they developed a bootstrap test procedure that provides good control of test levels under the null hypothesis. This procedure and some others that have been proposed are computationally involved and are based on techniques that are relatively unfamiliar to many practitioners. In this paper, the methods introduced by Rao and Scott (1981, Journal of the American Statistical Association 76, 221-230) for analyzing complex survey data are used to develop a simple test based on a corrected chi-squared statistic.

Filipino Americans and racism: A multiple mediation model of coping.

PubMed

Alvarez, Alvin N; Juang, Linda P

2010-04-01

Although the literature on Asian Americans and racism has been emerging, few studies have examined how coping influences one's encounters with racism. To advance the literature, the present study focused on the psychological impact of Filipino Americans' experiences with racism and the role of coping as a mediator using a community-based sample of adults (N = 199). Two multiple mediation models were used to examine the mediating effects of active, avoidance, support-seeking, and forbearance coping on the relationship between perceived racism and psychological distress and self-esteem, respectively. Separate analyses were also conducted for men and women given differences in coping utilization. For men, a bootstrap procedure indicated that active, support-seeking, and avoidance coping were mediators of the relationship between perceived racism and psychological distress. Active coping was negatively associated with psychological distress, whereas both support seeking and avoidance were positively associated with psychological distress. A second bootstrap procedure for men indicated that active and avoidance coping mediated the relationship between perceived racism and self-esteem such that active coping was positively associated with self-esteem, and avoidance was negatively associated with self-esteem. For women, only avoidance coping had a significant mediating effect that was associated with elevations in psychological distress and decreases in self-esteem. The results highlight the importance of examining the efficacy of specific coping responses to racism and the need to differentiate between the experiences of men and women. PsycINFO Database Record (c) 2010 APA, all rights reserved.

Percentile-Based ETCCDI Temperature Extremes Indices for CMIP5 Model Output: New Results through Semiparametric Quantile Regression Approach

NASA Astrophysics Data System (ADS)

Li, L.; Yang, C.

2017-12-01

Climate extremes often manifest as rare events in terms of surface air temperature and precipitation with an annual reoccurrence period. In order to represent the manifold characteristics of climate extremes for monitoring and analysis, the Expert Team on Climate Change Detection and Indices (ETCCDI) had worked out a set of 27 core indices based on daily temperature and precipitation data, describing extreme weather and climate events on an annual basis. The CLIMDEX project (http://www.climdex.org) had produced public domain datasets of such indices for data from a variety of sources, including output from global climate models (GCM) participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5). Among the 27 ETCCDI indices, there are six percentile-based temperature extremes indices that may fall into two groups: exceedance rates (ER) (TN10p, TN90p, TX10p and TX90p) and durations (CSDI and WSDI). Percentiles must be estimated prior to the calculation of the indices, and could more or less be biased by the adopted algorithm. Such biases will in turn be propagated to the final results of indices. The CLIMDEX used an empirical quantile estimator combined with a bootstrap resampling procedure to reduce the inhomogeneity in the annual series of the ER indices. However, there are still some problems remained in the CLIMDEX datasets, namely the overestimated climate variability due to unaccounted autocorrelation in the daily temperature data, seasonally varying biases and inconsistency between algorithms applied to the ER indices and to the duration indices. We now present new results of the six indices through a semiparametric quantile regression approach for the CMIP5 model output. By using the base-period data as a whole and taking seasonality and autocorrelation into account, this approach successfully addressed the aforementioned issues and came out with consistent results. The new datasets cover the historical and three projected (RCP2.6, RCP4.5 and RCP8.5) emission scenarios run a multimodel ensemble of 19 members. We analyze changes in the six indices on global and regional scales over the 21st century relative to either the base period 1961-1990 or the reference period 1981-2000, and compare the results with those based on the CLIMDEX datasets.

Explorations in Statistics: the Bootstrap

ERIC Educational Resources Information Center

Curran-Everett, Douglas

2009-01-01

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This fourth installment of Explorations in Statistics explores the bootstrap. The bootstrap gives us an empirical approach to estimate the theoretical variability among possible values of a sample statistic such as the…

Impacts of fire exclusion and recent managed fire on forest structure in old growth Sierra Nevada mixed-conifer forests

Treesearch

Brandon M. Collins; Richard G. Everett; Scott L. Stephens

2011-01-01

We re-sampled areas included in an unbiased 1911 timber inventory conducted by the U.S. Forest Service over a 4000 ha study area. Over half of the re-sampled area burned in relatively recent management- and lightning-ignited fires. This allowed for comparisons of both areas that have experienced recent fire and areas with no recent fire, to the same areas historically...

Quasi-Epipolar Resampling of High Resolution Satellite Stereo Imagery for Semi Global Matching

NASA Astrophysics Data System (ADS)

Tatar, N.; Saadatseresht, M.; Arefi, H.; Hadavand, A.

2015-12-01

Semi-global matching is a well-known stereo matching algorithm in photogrammetric and computer vision society. Epipolar images are supposed as input of this algorithm. Epipolar geometry of linear array scanners is not a straight line as in case of frame camera. Traditional epipolar resampling algorithms demands for rational polynomial coefficients (RPCs), physical sensor model or ground control points. In this paper we propose a new solution for epipolar resampling method which works without the need for these information. In proposed method, automatic feature extraction algorithms are employed to generate corresponding features for registering stereo pairs. Also original images are divided into small tiles. In this way by omitting the need for extra information, the speed of matching algorithm increased and the need for high temporal memory decreased. Our experiments on GeoEye-1 stereo pair captured over Qom city in Iran demonstrates that the epipolar images are generated with sub-pixel accuracy.

Temporal distribution of favourite books, movies, and records: differential encoding and re-sampling.

PubMed

Janssen, Steve M J; Chessa, Antonio G; Murre, Jaap M J

2007-10-01

The reminiscence bump is the effect that people recall more personal events from early adulthood than from childhood or adulthood. The bump has been examined extensively. However, the question of whether the bump is caused by differential encoding or re-sampling is still unanswered. To examine this issue, participants were asked to name their three favourite books, movies, and records. Furthermore,they were asked when they first encountered them. We compared the temporal distributions and found that they all showed recency effects and reminiscence bumps. The distribution of favourite books had the largest recency effect and the distribution of favourite records had the largest reminiscence bump. We can explain these results by the difference in rehearsal. Books are read two or three times, movies are watched more frequently, whereas records are listened to numerous times. The results suggest that differential encoding initially causes the reminiscence bump and that re-sampling increases the bump further.

HEXT, a software supporting tree-based screens for hybrid taxa in multilocus data sets, and an evaluation of the homoplasy excess test.

PubMed

Schneider, Kevin; Koblmüller, Stephan; Sefc, Kristina M

2015-11-11

The homoplasy excess test (HET) is a tree-based screen for hybrid taxa in multilocus nuclear phylogenies. Homoplasy between a hybrid taxon and the clades containing the parental taxa reduces bootstrap support in the tree. The HET is based on the expectation that excluding the hybrid taxon from the data set increases the bootstrap support for the parental clades, whereas excluding non-hybrid taxa has little effect on statistical node support. To carry out a HET, bootstrap trees are calculated with taxon-jackknife data sets, that is excluding one taxon (species, population) at a time. Excess increase in bootstrap support for certain nodes upon exclusion of a particular taxon indicates the hybrid (the excluded taxon) and its parents (the clades with increased support).We introduce a new software program, hext, which generates the taxon-jackknife data sets, runs the bootstrap tree calculations, and identifies excess bootstrap increases as outlier values in boxplot graphs. hext is written in r language and accepts binary data (0/1; e.g. AFLP) as well as co-dominant SNP and genotype data.We demonstrate the usefulness of hext in large SNP data sets containing putative hybrids and their parents. For instance, using published data of the genus Vitis (~6,000 SNP loci), hext output supports V. × champinii as a hybrid between V. rupestris and V. mustangensis .With simulated SNP and AFLP data sets, excess increases in bootstrap support were not always connected with the hybrid taxon (false positives), whereas the expected bootstrap signal failed to appear on several occasions (false negatives). Potential causes for both types of spurious results are discussed.With both empirical and simulated data sets, the taxon-jackknife output generated by hext provided additional signatures of hybrid taxa, including changes in tree topology across trees, consistent effects of exclusions of the hybrid and the parent taxa, and moderate (rather than excessive) increases in bootstrap support. hext significantly facilitates the taxon-jackknife approach to hybrid taxon detection, even though the simple test for excess bootstrap increase may not reliably identify hybrid taxa in all applications.

Bootstrap Estimation of Sample Statistic Bias in Structural Equation Modeling.

ERIC Educational Resources Information Center

Thompson, Bruce; Fan, Xitao

This study empirically investigated bootstrap bias estimation in the area of structural equation modeling (SEM). Three correctly specified SEM models were used under four different sample size conditions. Monte Carlo experiments were carried out to generate the criteria against which bootstrap bias estimation should be judged. For SEM fit indices,…

A Bootstrap Generalization of Modified Parallel Analysis for IRT Dimensionality Assessment

ERIC Educational Resources Information Center

Finch, Holmes; Monahan, Patrick

2008-01-01

This article introduces a bootstrap generalization to the Modified Parallel Analysis (MPA) method of test dimensionality assessment using factor analysis. This methodology, based on the use of Marginal Maximum Likelihood nonlinear factor analysis, provides for the calculation of a test statistic based on a parametric bootstrap using the MPA…

Long multiplet bootstrap

NASA Astrophysics Data System (ADS)

Cornagliotto, Martina; Lemos, Madalena; Schomerus, Volker

2017-10-01

Applications of the bootstrap program to superconformal field theories promise unique new insights into their landscape and could even lead to the discovery of new models. Most existing results of the superconformal bootstrap were obtained form correlation functions of very special fields in short (BPS) representations of the superconformal algebra. Our main goal is to initiate a superconformal bootstrap for long multiplets, one that exploits all constraints from superprimaries and their descendants. To this end, we work out the Casimir equations for four-point correlators of long multiplets of the two-dimensional global N=2 superconformal algebra. After constructing the full set of conformal blocks we discuss two different applications. The first one concerns two-dimensional (2,0) theories. The numerical bootstrap analysis we perform serves a twofold purpose, as a feasibility study of our long multiplet bootstrap and also as an exploration of (2,0) theories. A second line of applications is directed towards four-dimensional N=3 SCFTs. In this context, our results imply a new bound c≥ 13/24 for the central charge of such models, which we argue cannot be saturated by an interacting SCFT.

Changes in frequency of recall recommendations of examinations depicting cancer with the availability of either priors or digital breast tomosynthesis

NASA Astrophysics Data System (ADS)

Hakim, Christiane M.; Bandos, Andriy I.; Ganott, Marie A.; Catullo, Victor J.; Chough, Denise M.; Kelly, Amy E.; Shinde, Dilip D.; Sumkin, Jules H.; Wallace, Luisa P.; Nishikawa, Robert M.; Gur, David

2016-03-01

Performance changes in a binary environment when using additional information is affected only when changes in recommendations are made due to the additional information in question. In a recent study, we have shown that, contrary to general expectation, introducing prior examinations improved recall rates, but not sensitivity. In this study, we assessed cancer detection differences when prior examinations and/or digital breast tomosynthesis (DBT) were made available to the radiologist. We identified a subset of 21 cancer cases with differences in the number of radiologists who recalled these cases after reviewing either a prior examination or DBT. For the cases with differences in recommendations after viewing either priors or DBT, separately, we evaluated the total number of readers that changed their recommendations, regardless of the specific radiologist in question. Confidence intervals for the number of readers and a test for the hypothesis of no difference was performed using the non-parameteric bootstrap approach addressing both case and reader-related sources of variability by resampling cases and readers. With the addition of priors, there were 14 cancer cases (out of 15) where the number of "recalling radiologists" decreased. With the addition of DBT, the number of "recalling radiologists" decreased in only five cases (out of 15) while increasing in the remaining 9 cases. Unlike most new approaches to breast imaging DBT seems to improve both recall rates and cancer detection rates. Changes in recommendations were noted by all radiologists for all cancers by type, size, and breast density.

The ratio of hemoglobin to red cell distribution width as a novel prognostic parameter in esophageal squamous cell carcinoma: a retrospective study from southern China

PubMed Central

Bi, Xiwen; Yang, Hang; An, Xin; Wang, Fenghua; Jiang, Wenqi

2016-01-01

Background We propose a novel prognostic parameter for esophageal squamous cell carcinoma (ESCC)—hemoglobin/red cell distribution width (HB/RDW) ratio. Its clinical prognostic value and relationship with other clinicopathological characteristics were investigated in ESCC patients. Results The optimal cut-off value was 0.989 for the HB/RDW ratio. The HB/RDW ratio (P= 0.035), tumor depth (P = 0.020) and lymph node status (P<0.001) were identified to be an independent prognostic factors of OS by multivariate analysis, which was validated by bootstrap resampling. Patients with a low HB/RDW ratio had a 1.416 times greater risk of dying during follow-up compared with those with a high HB/RDW (95% CI = 1.024–1.958, P = 0.035). Materials and Methods We retrospectively analyzed 362 patients who underwent curative treatment at a single institution between January 2007 and December 2008. The chi-square test was used to evaluate relationships between the HB/RDW ratio and other clinicopathological variables; the Kaplan–Meier method was used to analyze the 5-year overall survival (OS); and the Cox proportional hazards models were used for univariate and multivariate analyses of variables related to OS. Conclusion A significant association was found between the HB/RDW ratio and clinical characteristics and survival outcomes in ESCC patients. Based on these findings, we believe that the HB/RDW ratio is a novel and promising prognostic parameter for ESCC patients. PMID:27223088

A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking.

PubMed

Ballester, Pedro J; Mitchell, John B O

2010-05-01

Accurately predicting the binding affinities of large sets of diverse protein-ligand complexes is an extremely challenging task. The scoring functions that attempt such computational prediction are essential for analysing the outputs of molecular docking, which in turn is an important technique for drug discovery, chemical biology and structural biology. Each scoring function assumes a predetermined theory-inspired functional form for the relationship between the variables that characterize the complex, which also include parameters fitted to experimental or simulation data and its predicted binding affinity. The inherent problem of this rigid approach is that it leads to poor predictivity for those complexes that do not conform to the modelling assumptions. Moreover, resampling strategies, such as cross-validation or bootstrapping, are still not systematically used to guard against the overfitting of calibration data in parameter estimation for scoring functions. We propose a novel scoring function (RF-Score) that circumvents the need for problematic modelling assumptions via non-parametric machine learning. In particular, Random Forest was used to implicitly capture binding effects that are hard to model explicitly. RF-Score is compared with the state of the art on the demanding PDBbind benchmark. Results show that RF-Score is a very competitive scoring function. Importantly, RF-Score's performance was shown to improve dramatically with training set size and hence the future availability of more high-quality structural and interaction data is expected to lead to improved versions of RF-Score. pedro.ballester@ebi.ac.uk; jbom@st-andrews.ac.uk Supplementary data are available at Bioinformatics online.

Development of a nomogram for predicting in-hospital mortality of patients with exacerbation of chronic obstructive pulmonary disease.

PubMed

Sakamoto, Yukiyo; Yamauchi, Yasuhiro; Yasunaga, Hideo; Takeshima, Hideyuki; Hasegawa, Wakae; Jo, Taisuke; Sasabuchi, Yusuke; Matsui, Hiroki; Fushimi, Kiyohide; Nagase, Takahide

2017-01-01

Patients with chronic obstructive pulmonary disease (COPD) often experience exacerbations of their disease, sometimes requiring hospital admission and being associated with increased mortality. Although previous studies have reported mortality from exacerbations of COPD, there is limited information about prediction of individual in-hospital mortality. We therefore aimed to use data from a nationwide inpatient database in Japan to generate a nomogram for predicting in-hospital mortality from patients' characteristics on admission. We retrospectively collected data on patients with COPD who had been admitted for exacerbations and been discharged between July 1, 2010 and March 31, 2013. We performed multivariable logistic regression analysis to examine factors associated with in-hospital mortality and thereafter used these factors to develop a nomogram for predicting in-hospital prognosis. The study comprised 3,064 eligible patients. In-hospital death occurred in 209 patients (6.8%). Higher mortality was associated with older age, being male, lower body mass index, disturbance of consciousness, severe dyspnea, history of mechanical ventilation, pneumonia, and having no asthma on admission. We developed a nomogram based on these variables to predict in-hospital mortality. The concordance index of the nomogram was 0.775. Internal validation was performed by a bootstrap method with 50 resamples, and calibration plots were found to be well fitted to predict in-hospital mortality. We developed a nomogram for predicting in-hospital mortality of exacerbations of COPD. This nomogram could help clinicians to predict risk of in-hospital mortality in individual patients with COPD exacerbation.

Simultaneous multiplexed quantification of nicotine and its metabolites using surface enhanced Raman scattering.

PubMed

Alharbi, Omar; Xu, Yun; Goodacre, Royston

2014-10-07

The detection and quantification of xenobiotics and their metabolites in man is important for drug dosing, therapy and for substance abuse monitoring where longer-lived metabolic products from illicit materials can be assayed after the drug of abuse has been cleared from the system. Raman spectroscopy offers unique specificity for molecular characterization and this usually weak signal can be significantly enhanced using surface enhanced Raman scattering (SERS). We report here the novel development of SERS with chemometrics for the simultaneous analysis of the drug nicotine and its major xenometabolites cotinine and trans-3'-hydroxycotinine. Initial experiments optimized the SERS conditions and we found that when these three determinands were analysed individually that the maximum SERS signals were found at three different pH. These were pH 3 for nicotine and pH 10 and 11 for cotinine and trans-3'-hydroxycotinine, respectively. Tertiary mixtures containing nicotine, cotinine and trans-3'-hydroxycotinine were generated in the concentration range 10(-7)-10(-5) M and SERS spectra were collected at all three pH values. Chemometric analysis using kernel-partial least squares (K-PLS) and artificial neural networks (ANNs) were conducted and these models were validated using bootstrap resampling. All three analytes were accurately quantified with typical root mean squared error of prediction on the test set data being 5-9%; nicotine was most accurately predicted followed by cotinine and then trans-3'-hydroxycotinine. We believe that SERS is a powerful approach for the simultaneous analysis of multiple determinands without recourse to lengthy chromatography, as demonstrated here for the xenobiotic nicotine and its two major xenometabolites.

An improved Rosetta pedotransfer function and evaluation in earth system models

NASA Astrophysics Data System (ADS)

Zhang, Y.; Schaap, M. G.

2017-12-01

Soil hydraulic parameters are often difficult and expensive to measure, leading to the pedotransfer functions (PTFs) an alternative to predict those parameters. Rosetta (Schaap et al., 2001, denoted as Rosetta1) are widely used PTFs, which is based on artificial neural network (ANN) analysis coupled with the bootstrap re-sampling method, allowing the estimation of van Genuchten water retention parameters (van Genuchten, 1980, abbreviated here as VG), saturated hydraulic conductivity (Ks), as well as their uncertainties. We present an improved hierarchical pedotransfer functions (Rosetta3) that unify the VG water retention and Ks submodels into one, thus allowing the estimation of uni-variate and bi-variate probability distributions of estimated parameters. Results show that the estimation bias of moisture content was reduced significantly. Rosetta1 and Posetta3 were implemented in the python programming language, and the source code are available online. Based on different soil water retention equations, there are diverse PTFs used in different disciplines of earth system modelings. PTFs based on Campbell [1974] or Clapp and Hornberger [1978] are frequently used in land surface models and general circulation models, while van Genuchten [1980] based PTFs are more widely used in hydrology and soil sciences. We use an independent global scale soil database to evaluate the performance of diverse PTFs used in different disciplines of earth system modelings. PTFs are evaluated based on different soil characteristics and environmental characteristics, such as soil textural data, soil organic carbon, soil pH, as well as precipitation and soil temperature. This analysis provides more quantitative estimation error information for PTF predictions in different disciplines of earth system modelings.

CTER-rapid estimation of CTF parameters with error assessment.

PubMed

Penczek, Pawel A; Fang, Jia; Li, Xueming; Cheng, Yifan; Loerke, Justus; Spahn, Christian M T

2014-05-01

In structural electron microscopy, the accurate estimation of the Contrast Transfer Function (CTF) parameters, particularly defocus and astigmatism, is of utmost importance for both initial evaluation of micrograph quality and for subsequent structure determination. Due to increases in the rate of data collection on modern microscopes equipped with new generation cameras, it is also important that the CTF estimation can be done rapidly and with minimal user intervention. Finally, in order to minimize the necessity for manual screening of the micrographs by a user it is necessary to provide an assessment of the errors of fitted parameters values. In this work we introduce CTER, a CTF parameters estimation method distinguished by its computational efficiency. The efficiency of the method makes it suitable for high-throughput EM data collection, and enables the use of a statistical resampling technique, bootstrap, that yields standard deviations of estimated defocus and astigmatism amplitude and angle, thus facilitating the automation of the process of screening out inferior micrograph data. Furthermore, CTER also outputs the spatial frequency limit imposed by reciprocal space aliasing of the discrete form of the CTF and the finite window size. We demonstrate the efficiency and accuracy of CTER using a data set collected on a 300kV Tecnai Polara (FEI) using the K2 Summit DED camera in super-resolution counting mode. Using CTER we obtained a structure of the 80S ribosome whose large subunit had a resolution of 4.03Å without, and 3.85Å with, inclusion of astigmatism parameters. Copyright © 2014 Elsevier B.V. All rights reserved.

Drinking to cope mediates the relationship between depression and alcohol risk: Different pathways for college and non-college young adults.

PubMed

Kenney, Shannon R; Anderson, Bradley J; Stein, Michael D

2018-05-01

It is well-established that drinking to cope with negative affective states mediates the relationship between depressed mood and alcohol risk outcomes among college students. Whether non-college emerging adults exhibit a similar pathway remains unknown. In the current study, we compared the mediating role of coping motives in the relationship between depressive symptoms and drinking risk outcomes (heavy episodic drinking and alcohol problems) in college and non-college emerging adult subgroups. Participants were three hundred forty-one community-recruited 18-25year olds reporting past month alcohol use. We used a structural equation modeling (SEM) for our primary mediation analysis and bias-corrected bootstrap resampling for testing the statistical significance of mediation. Participants averaged 20.8 (±1.97) years of age, 49% were female, 67.7% were White, 34.6% were college students, and 65.4% were non-college emerging adults. College and non-college emerging adults reported similar levels of drinking, alcohol problems, and drinking to cope with negative affect, and drinking to cope was associated with alcohol-related problems in both samples. However, while drinking to cope mediated the relationship between depressed mood and alcohol problems among students, it did not mediate the pathway among non-college emerging adults. These findings caution against extending college-based findings to non-college populations and underscore the need to better understand the role of coping motives and other intervening factors in pathways linking depressed mood and alcohol-related risk in non-college emerging adults. Copyright © 2018 Elsevier Ltd. All rights reserved.

Biological Factors Contributing to the Response to Cognitive Training in Mild Cognitive Impairment.

PubMed

Peter, Jessica; Schumacher, Lena V; Landerer, Verena; Abdulkadir, Ahmed; Kaller, Christoph P; Lahr, Jacob; Klöppel, Stefan

2018-01-01

In mild cognitive impairment (MCI), small benefits from cognitive training were observed for memory functions but there appears to be great variability in the response to treatment. Our study aimed to improve the characterization and selection of those participants who will benefit from cognitive intervention. We evaluated the predictive value of disease-specific biological factors for the outcome after cognitive training in MCI (n = 25) and also considered motivation of the participants. We compared the results of the cognitive intervention group with two independent control groups of MCI patients (local memory clinic, n = 20; ADNI cohort, n = 302). The primary outcome measure was episodic memory as measured by verbal delayed recall of a 10-word list. Episodic memory remained stable after treatment and slightly increased 6 months after the intervention. In contrast, in MCI patients who did not receive an intervention, episodic memory significantly decreased during the same time interval. A larger left entorhinal cortex predicted more improvement in episodic memory after treatment and so did higher levels of motivation. Adding disease-specific biological factors significantly improved the prediction of training-related change compared to a model based simply on age and baseline performance. Bootstrapping with resampling (n = 1000) verified the stability of our finding. Cognitive training might be particularly helpful in individuals with a bigger left entorhinal cortex as individuals who did not benefit from intervention showed 17% less volume in this area. When extended to alternative treatment options, stratification based on disease-specific biological factors is a useful step towards individualized medicine.

Cost-effectiveness of telephonic disease management in heart failure.

PubMed

Smith, Brad; Hughes-Cromwick, Paul F; Forkner, Emma; Galbreath, Autumn Dawn

2008-02-01

To evaluate the cost-effectiveness of a telephonic disease management (DM) intervention in heart failure (HF). Randomized controlled trial of telephonic DM among 1069 community-dwelling patients with systolic HF (SHF) and diastolic HF performed between 1999 and 2003. The enrollment period was 18 months per subject. Bootstrap-resampled incremental cost-effectiveness ratios (ICERs) were computed and compared across groups. Direct medical costs were obtained from a medical record review that collected records from 92% of patients; 66% of records requested were obtained. Disease management produced statistically significant survival advantages among all patients (17.4 days, P = .04), among patients with New York Heart Association (NYHA) class III/IV symptoms (47.7 days, P = .02), and among patients with SHF (24.2 days, P = .01). Analyses of direct medical and intervention costs showed no cost savings associated with the intervention. For all patients and considering all-cause medical care, the ICER was $146 870 per quality-adjusted life-year (QALY) gained, while for patients with NYHA class III/IV symptoms and patients with SHF, the ICERs were $67 784 and $95 721 per QALY gained, respectively. Costs per QALY gained were $101 120 for all patients, $72 501 for patients with SHF, and $41 348 for patients with NYHA class III/IV symptoms. The intervention was effective but costly to implement and did not reduce utilization. It may not be cost-effective in other broadly representative samples of patients. However, with program cost reductions and proper targeting, this program may produce life-span increases at costs that are less than $100 000 per QALY gained.

Illustrating, Quantifying, and Correcting for Bias in Post-hoc Analysis of Gene-Based Rare Variant Tests of Association

PubMed Central

Grinde, Kelsey E.; Arbet, Jaron; Green, Alden; O'Connell, Michael; Valcarcel, Alessandra; Westra, Jason; Tintle, Nathan

2017-01-01

To date, gene-based rare variant testing approaches have focused on aggregating information across sets of variants to maximize statistical power in identifying genes showing significant association with diseases. Beyond identifying genes that are associated with diseases, the identification of causal variant(s) in those genes and estimation of their effect is crucial for planning replication studies and characterizing the genetic architecture of the locus. However, we illustrate that straightforward single-marker association statistics can suffer from substantial bias introduced by conditioning on gene-based test significance, due to the phenomenon often referred to as “winner's curse.” We illustrate the ramifications of this bias on variant effect size estimation and variant prioritization/ranking approaches, outline parameters of genetic architecture that affect this bias, and propose a bootstrap resampling method to correct for this bias. We find that our correction method significantly reduces the bias due to winner's curse (average two-fold decrease in bias, p < 2.2 × 10−6) and, consequently, substantially improves mean squared error and variant prioritization/ranking. The method is particularly helpful in adjustment for winner's curse effects when the initial gene-based test has low power and for relatively more common, non-causal variants. Adjustment for winner's curse is recommended for all post-hoc estimation and ranking of variants after a gene-based test. Further work is necessary to continue seeking ways to reduce bias and improve inference in post-hoc analysis of gene-based tests under a wide variety of genetic architectures. PMID:28959274

The index gage method to develop a flow duration curve from short-term streamflow records

NASA Astrophysics Data System (ADS)

Zhang, Zhenxing

2017-10-01

The flow duration curve (FDC) is one of the most commonly used graphical tools in hydrology and provides a comprehensive graphical view of streamflow variability at a particular site. For a gaged site, an FDC can be easily estimated with frequency analysis. When no streamflow records are available, regional FDCs are used to synthesize FDCs. However, studies on how to develop FDCs for sites with short-term records have been very limited. Deriving representative FDC when there are short-term hydrologic records is important. For instance, 43% of the 394 streamflow gages in Illinois have records of 20 years or fewer, and these short-term gages are often distributed in headwaters and contain valuable hydrologic information. In this study, the index gage method is proposed to develop FDCs using short-term hydrologic records via an information transfer technique from a nearby hydrologically similar index gage. There are three steps: (1) select an index gage; (2) determine changes of FDC; and (3) develop representative FDCs. The approach is tested using records from 92 U.S. Geological Survey streamflow gages in Illinois. A jackknife experiment is conducted to assess the performance. Bootstrap resampling is used to simulate various periods of records, i.e., 1, 2, 5, 10, 15, and 20 years of records. The results demonstrated that the index gage method is capable of developing a representative FDC using short-term records. Generally, the approach performance is improved when more hydrologic records are available, but the improvement appears to level off when the short-term gage has 10 years or more records.

Comparison of the risk factors effects between two populations: two alternative approaches illustrated by the analysis of first and second kidney transplant recipients

PubMed Central

2013-01-01

Background Whereas the prognosis of second kidney transplant recipients (STR) compared to the first ones has been frequently analyzed, no study has addressed the issue of comparing the risk factor effects on graft failure between both groups. Methods Here, we propose two alternative strategies to study the heterogeneity of risk factors between two groups of patients: (i) a multiplicative-regression model for relative survival (MRS) and (ii) a stratified Cox model (SCM) specifying the graft rank as strata and assuming subvectors of the explicatives variables. These developments were motivated by the analysis of factors associated with time to graft failure (return-to-dialysis or patient death) in second kidney transplant recipients (STR) compared to the first ones. Estimation of the parameters was based on partial likelihood maximization. Monte-Carlo simulations associated with bootstrap re-sampling was performed to calculate the standard deviations for the MRS. Results We demonstrate, for the first time in renal transplantation, that: (i) male donor gender is a specific risk factor for STR, (ii) the adverse effect of recipient age is enhanced for STR and (iii) the graft failure risk related to donor age is attenuated for STR. Conclusion While the traditional Cox model did not provide original results based on the renal transplantation literature, the proposed relative and stratified models revealed new findings that are useful for clinicians. These methodologies may be of interest in other medical fields when the principal objective is the comparison of risk factors between two populations. PMID:23915191

Pain catastrophizing mediates the relationship between self-reported strenuous exercise involvement and pain ratings: moderating role of anxiety sensitivity.

PubMed

Goodin, Burel R; McGuire, Lynanne M; Stapleton, Laura M; Quinn, Noel B; Fabian, Lacy A; Haythornthwaite, Jennifer A; Edwards, Robert R

2009-11-01

To investigate the cross-sectional associations among self-reported weekly strenuous exercise bouts, anxiety sensitivity, and their interaction with pain catastrophizing and pain responses to the cold pressor task (CPT) in healthy, ethnically diverse young adults (n = 79). Exercise involvement has been shown to have hypoalgesic effects and cognitive factors may partially explain this effect. Particularly, alterations in pain catastrophizing have been found to mediate the positive pain outcomes of multidisciplinary treatments incorporating exercise. Further, recent evidence suggests that exercise involvement and anxiety sensitivity may act together, as interacting factors, to exert an effect on catastrophizing and pain outcomes; however, further research is needed to clarify the nature of this interaction. Before the CPT, participants were asked to complete the Godin Leisure-Time Exercise Questionnaire, the Beck Depression Inventory, and the Anxiety Sensitivity Index. After the CPT, participants completed a modified version of the Pain Catastrophizing Scale and the Short Form-McGill Pain Questionnaire. At a high level of anxiety sensitivity, controlling for depressive symptoms, CPT immersion time, and sex differences, a bias-corrected (BC), bootstrapped confidence interval revealed that pain catastrophizing significantly mediated the relationship between self-reported weekly strenuous exercise bouts and pain response (95% BC Confidence Interval = -9.558, -0.800 with 1000 resamples). At intermediate and low levels of anxiety sensitivity, no significant mediation effects were found. These findings support that, for pain catastrophizing to mediate the strenuous exercise-pain response relation, individuals must possess a high level of anxiety sensitivity.

Exemplary Care as a Mediator of the Effects of Caregiver Subjective Appraisal and Emotional Outcomes

PubMed Central

Harris, Grant M.; Durkin, Daniel W.; Allen, Rebecca S.; DeCoster, Jamie; Burgio, Louis D.

2011-01-01

Purpose: Exemplary care (EC) is a new construct encompassing care behaviors that warrants further study within stress process models of dementia caregiving. Previous research has examined EC within the context of cognitively intact older adult care recipients (CRs) and their caregivers (CGs). This study sought to expand our knowledge of quality of care by investigating EC within a diverse sample of dementia CGs. Design and Methods: We examined the relation between CG subjective appraisal (daily care bother, burden, and behavioral bother), EC, and CG emotional outcomes (depression and positive aspects of caregiving [PAC]). Specifically, EC was examined as a possible mediator of the effects of CG subjective appraisals on emotional outcomes. Using a bootstrapping method and an SPSS macro developed by Preacher and Hayes (2008 Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models), we tested the indirect effect of EC on the relation between CG subjective appraisals and CG emotional outcomes. Results: Overall, EC partially mediates the relation between the subjective appraisal variables (daily care bother, burden, and behavioral bother) and PAC. Results for depression were similar except that EC did not mediate the relation between burden and depression. This pattern of results varied by race/ethnicity. Implications: Overall, CGs’ perception of providing EC to individuals with dementia partially explains the relation between subjective appraisal and symptoms of depression and PAC. Results of this study suggest that interventions may benefit from training CGs to engage in EC to improve their emotional outcomes and quality of care. PMID:21350038

The relationship of sociodemographic and psychological variables with chronic pain variables in a low-income population.

PubMed

Newman, Andrea K; Van Dyke, Benjamin P; Torres, Calia A; Baxter, Jacob W; Eyer, Joshua C; Kapoor, Shweta; Thorn, Beverly E

2017-09-01

Chronic pain is a pervasive condition that is complicated by economic, educational, and racial disparities. This study analyzes key factors associated with chronic pain within an understudied and underserved population. The sample is characterized by a triple disparity with respect to income, education/literacy, and racial barriers that substantially increase the vulnerability to the negative consequences of chronic pain. The study examined the pretreatment data of 290 participants enrolled in the Learning About My Pain trial, a randomized controlled comparative effectiveness trial of psychosocial interventions (B.E.T., Principal Investigator, Patient-Centered Outcomes Research Institute Contract No. 941; clinicaltrials.gov identifier NCT01967342) for chronic pain. Hierarchical multiple regression analyses evaluated the relationships among sociodemographic (sex, age, race, poverty status, literacy, and education level) and psychological (depressive symptoms and pain catastrophizing) variables and pain interference, pain severity, and disability. The indirect effects of depressive symptoms and pain catastrophizing on the sociodemographic and pain variables were investigated using bootstrap resampling. Reversed mediation models were also examined. Results suggested that the experience of chronic pain within this low-income sample is better accounted for by psychological factors than sex, age, race, poverty status, literacy, and education level. Depressive symptoms and pain catastrophizing mediated the relationships between age and pain variables, whereas pain catastrophizing mediated the effects of primary literacy and poverty status. Some reversed models were equivalent to the hypothesized models, suggesting the possibility of bidirectionality. Although cross-sectional findings cannot establish causality, our results highlight the critical role psychological factors play in individuals with chronic pain and multiple health disparities.

New constraints on Lyman-α opacity with a sample of 62 quasars at z > 5.7

NASA Astrophysics Data System (ADS)

Bosman, Sarah E. I.; Fan, Xiaohui; Jiang, Linhua; Reed, Sophie; Matsuoka, Yoshiki; Becker, George; Haehnelt, Martin

2018-05-01

We present measurements of the mean and scatter of the IGM Lyman-α opacity at 4.9 < z < 6.1 along the lines of sight of 62 quasars at zsource > 5.7, the largest sample assembled at these redshifts to date by a factor of two. The sample size enables us to sample cosmic variance at these redshifts more robustly than ever before. The spectra used here were obtained by the SDSS, DES-VHS and SHELLQs collaborations, drawn from the ESI and X-Shooter archives, reused from previous studies or observed specifically for this work. We measure the effective optical depth of Lyman-α in bins of 10, 30, 50 and 70 cMpc h-1, construct cumulative distribution functions under two treatments of upper limits on flux and explore an empirical analytic fit to residual Lyman-α transmission. We verify the consistency of our results with those of previous studies via bootstrap re-sampling and confirm the existence of tails towards high values in the opacity distributions, which may persist down to z ˜ 5.2. Comparing our results with predictions from cosmological simulations, we find further strong evidence against models that include a spatially uniform ionizing background and temperature-density relation. We also compare to IGM models that include either a fluctuating UVB dominated by rare quasars or temperature fluctuations due to patchy reionization. Although both models produce better agreement with the observations, neither fully captures the observed scatter in IGM opacity. Our sample of 62 z > 5.7 quasar spectra opens many avenues for future study of the reionisation epoch.

Development of a prediction model for residual disease in newly diagnosed advanced ovarian cancer.

PubMed

Janco, Jo Marie Tran; Glaser, Gretchen; Kim, Bohyun; McGree, Michaela E; Weaver, Amy L; Cliby, William A; Dowdy, Sean C; Bakkum-Gamez, Jamie N

2015-07-01

To construct a tool, using computed tomography (CT) imaging and preoperative clinical variables, to estimate successful primary cytoreduction for advanced epithelial ovarian cancer (EOC). Women who underwent primary cytoreductive surgery for stage IIIC/IV EOC at Mayo Clinic between 1/2/2003 and 12/30/2011 and had preoperative CT images of the abdomen and pelvis within 90days prior to their surgery available for review were included. CT images were reviewed for large-volume ascites, diffuse peritoneal thickening (DPT), omental cake, lymphadenopathy (LP), and spleen or liver involvement. Preoperative factors included age, body mass index (BMI), Eastern Cooperative Oncology Group performance status (ECOG PS), American Society of Anesthesiologists (ASA) score, albumin, CA-125, and thrombocytosis. Two prediction models were developed to estimate the probability of (i) complete and (ii) suboptimal cytoreduction (residual disease (RD) >1cm) using multivariable logistic analysis with backward and stepwise variable selection methods. Internal validation was assessed using bootstrap resampling to derive an optimism-corrected estimate of the c-index. 279 patients met inclusion criteria: 143 had complete cytoreduction, 26 had suboptimal cytoreduction (RD>1cm), and 110 had measurable RD ≤1cm. On multivariable analysis, age, absence of ascites, omental cake, and DPT on CT imaging independently predicted complete cytoreduction (c-index=0.748). Conversely, predictors of suboptimal cytoreduction were ECOG PS, DPT, and LP on preoperative CT imaging (c-index=0.685). The generated models serve as preoperative evaluation tools that may improve counseling and selection for primary surgery, but need to be externally validated. Copyright © 2015 Elsevier Inc. All rights reserved.

Economic Evaluation of Manitoba Health Lines in the Management of Congestive Heart Failure

PubMed Central

Cui, Yang; Doupe, Malcolm; Katz, Alan; Nyhof, Paul; Forget, Evelyn L.

2013-01-01

Objective: This one-year study investigated whether the Manitoba Provincial Health Contact program for congestive heart failure (CHF) is a cost-effective intervention relative to the standard treatment. Design: Individual patient-level, randomized clinical trial of cost-effective model using data from the Health Research Data Repository at the Manitoba Centre for Health Policy, University of Manitoba. Methods: A total of 179 patients aged 40 and over with a diagnosis of CHF levels II to IV were recruited from Winnipeg and Central Manitoba and randomized into three treatment groups: one receiving standard care, a second receiving Health Lines (HL) intervention and a third receiving Health Lines intervention plus in-house monitoring (HLM). A cost-effectiveness study was conducted in which outcomes were measured in terms of QALYs derived from the SF-36 and costs using 2005 Canadian dollars. Costs included intervention and healthcare utilization. Bootstrap-resampled incremental cost-effectiveness ratios were computed to take into account the uncertainty related to small sample size. Results: The total per-patient mean costs (including intervention cost) were not significantly different between study groups. Both interventions (HL and HLM) cost less and are more effective than standard care, with HL able to produce an additional QALY relative to HLM for $2,975. The sensitivity analysis revealed that there is an 85.8% probability that HL is cost-effective if decision-makers are willing to pay $50,000. Conclusion: Findings demonstrate that the HL intervention from the Manitoba Provincial Health Contact program for CHF is an optimal intervention strategy for CHF management compared to standard care and HLM. PMID:24359716

A search for evidence of solar rotation in Super-Kamiokande solar neutrino dataset

NASA Astrophysics Data System (ADS)

Desai, Shantanu; Liu, Dawei W.

2016-09-01

We apply the generalized Lomb-Scargle (LS) periodogram, proposed by Zechmeister and Kurster, to the solar neutrino data from Super-Kamiokande (Super-K) using data from its first five years. For each peak in the LS periodogram, we evaluate the statistical significance in two different ways. The first method involves calculating the False Alarm Probability (FAP) using non-parametric bootstrap resampling, and the second method is by calculating the difference in Bayesian Information Criterion (BIC) between the null hypothesis, viz. the data contains only noise, compared to the hypothesis that the data contains a peak at a given frequency. Using these methods, we scan the frequency range between 7-14 cycles per year to look for any peaks caused by solar rotation, since this is the proposed explanation for the statistically significant peaks found by Sturrock and collaborators in the Super-K dataset. From our analysis, we do confirm that similar to Sturrock et al, the maximum peak occurs at a frequency of 9.42/year, corresponding to a period of 38.75 days. The FAP for this peak is about 1.5% and the difference in BIC (between pure white noise and this peak) is about 4.8. We note that the significance depends on the frequency band used to search for peaks and hence it is important to use a search band appropriate for solar rotation. However, The significance of this peak based on the value of BIC is marginal and more data is needed to confirm if the peak persists and is real.

Geriatric Fever Score: a new decision rule for geriatric care.

PubMed

Chung, Min-Hsien; Huang, Chien-Cheng; Vong, Si-Chon; Yang, Tzu-Meng; Chen, Kuo-Tai; Lin, Hung-Jung; Chen, Jiann-Hwa; Su, Shih-Bin; Guo, How-Ran; Hsu, Chien-Chin

2014-01-01

Evaluating geriatric patients with fever is time-consuming and challenging. We investigated independent mortality predictors of geriatric patients with fever and developed a prediction rule for emergency care, critical care, and geriatric care physicians to classify patients into mortality risk and disposition groups. Consecutive geriatric patients (≥65 years old) visiting the emergency department (ED) of a university-affiliated medical center between June 1 and July 21, 2010, were enrolled when they met the criteria of fever: a tympanic temperature ≥37.2°C or a baseline temperature elevated ≥1.3°C. Thirty-day mortality was the primary endpoint. Internal validation with bootstrap re-sampling was done. Three hundred thirty geriatric patients were enrolled. We found three independent mortality predictors: Leukocytosis (WBC >12,000 cells/mm3), Severe coma (GCS ≤ 8), and Thrombocytopenia (platelets <150 10(3)/mm3) (LST). After assigning weights to each predictor, we developed a Geriatric Fever Score that stratifies patients into two mortality-risk and disposition groups: low (4.0%) (95% CI: 2.3-6.9%): a general ward or treatment in the ED then discharge and high (30.3%) (95% CI: 17.4-47.3%): consider the intensive care unit. The area under the curve for the rule was 0.73. We found that the Geriatric Fever Score is a simple and rapid rule for predicting 30-day mortality and classifying mortality risk and disposition in geriatric patients with fever, although external validation should be performed to confirm its usefulness in other clinical settings. It might help preserve medical resources for patients in greater need.

Testing homogeneity of proportion ratios for stratified correlated bilateral data in two-arm randomized clinical trials.

PubMed

Pei, Yanbo; Tian, Guo-Liang; Tang, Man-Lai

2014-11-10

Stratified data analysis is an important research topic in many biomedical studies and clinical trials. In this article, we develop five test statistics for testing the homogeneity of proportion ratios for stratified correlated bilateral binary data based on an equal correlation model assumption. Bootstrap procedures based on these test statistics are also considered. To evaluate the performance of these statistics and procedures, we conduct Monte Carlo simulations to study their empirical sizes and powers under various scenarios. Our results suggest that the procedure based on score statistic performs well generally and is highly recommended. When the sample size is large, procedures based on the commonly used weighted least square estimate and logarithmic transformation with Mantel-Haenszel estimate are recommended as they do not involve any computation of maximum likelihood estimates requiring iterative algorithms. We also derive approximate sample size formulas based on the recommended test procedures. Finally, we apply the proposed methods to analyze a multi-center randomized clinical trial for scleroderma patients. Copyright © 2014 John Wiley & Sons, Ltd.

Control of bootstrap current in the pedestal region of tokamaks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shaing, K. C.; Department of Engineering Physics, University of Wisconsin, Madison, Wisconsin 53796; Lai, A. L.

2013-12-15

The high confinement mode (H-mode) plasmas in the pedestal region of tokamaks are characterized by steep gradient of the radial electric field, and sonic poloidal U{sub p,m} flow that consists of poloidal components of the E×B flow and the plasma flow velocity that is parallel to the magnetic field B. Here, E is the electric field. The bootstrap current that is important for the equilibrium, and stability of the pedestal of H-mode plasmas is shown to have an expression different from that in the conventional theory. In the limit where ‖U{sub p,m}‖≫ 1, the bootstrap current is driven by themore » electron temperature gradient and inductive electric field fundamentally different from that in the conventional theory. The bootstrap current in the pedestal region can be controlled through manipulating U{sub p,m} and the gradient of the radial electric. This, in turn, can control plasma stability such as edge-localized modes. Quantitative evaluations of various coefficients are shown to illustrate that the bootstrap current remains finite when ‖U{sub p,m}‖ approaches infinite and to provide indications how to control the bootstrap current. Approximate analytic expressions for viscous coefficients that join results in the banana and plateau-Pfirsch-Schluter regimes are presented to facilitate bootstrap and neoclassical transport simulations in the pedestal region.« less

Arctic Acoustic Workshop Proceedings, 14-15 February 1989.

DTIC Science & Technology

1989-06-01

measurements. The measurements reported by Levine et al. (1987) were taken from current and temperature sensors moored in two triangular grids . The internal...requires a resampling of the data series on a uniform depth-time grid . Statistics calculated from the resampled series will be used to test numerical...from an isolated keel. Figure 2: 2-D Modeling Geometry - The model is based on a 2-D Cartesian grid with an axis of symmetry on the left. A pulsed

Multispectral Resampling of Seagrass Species Spectra: WorldView-2, Quickbird, Sentinel-2A, ASTER VNIR, and Landsat 8 OLI

NASA Astrophysics Data System (ADS)

Wicaksono, Pramaditya; Salivian Wisnu Kumara, Ignatius; Kamal, Muhammad; Afif Fauzan, Muhammad; Zhafarina, Zhafirah; Agus Nurswantoro, Dwi; Noviaris Yogyantoro, Rifka

2017-12-01

Although spectrally different, seagrass species may not be able to be mapped from multispectral remote sensing images due to the limitation of their spectral resolution. Therefore, it is important to quantitatively assess the possibility of mapping seagrass species using multispectral images by resampling seagrass species spectra to multispectral bands. Seagrass species spectra were measured on harvested seagrass leaves. Spectral resolution of multispectral images used in this research was adopted from WorldView-2, Quickbird, Sentinel-2A, ASTER VNIR, and Landsat 8 OLI. These images are widely available and can be a good representative and baseline for previous or future remote sensing images. Seagrass species considered in this research are Enhalus acoroides (Ea), Thalassodendron ciliatum (Tc), Thalassia hemprichii (Th), Cymodocea rotundata (Cr), Cymodocea serrulata (Cs), Halodule uninervis (Hu), Halodule pinifolia (Hp), Syringodum isoetifolium (Si), Halophila ovalis (Ho), and Halophila minor (Hm). Multispectral resampling analysis indicate that the resampled spectra exhibit similar shape and pattern with the original spectra but less precise, and they lose the unique absorption feature of seagrass species. Relying on spectral bands alone, multispectral image is not effective in mapping these seagrass species individually, which is shown by the poor and inconsistent result of Spectral Angle Mapper (SAM) classification technique in classifying seagrass species using seagrass species spectra as pure endmember. Only Sentinel-2A produced acceptable classification result using SAM.

Confidence Intervals for the Mean: To Bootstrap or Not to Bootstrap

ERIC Educational Resources Information Center

Calzada, Maria E.; Gardner, Holly

2011-01-01

The results of a simulation conducted by a research team involving undergraduate and high school students indicate that when data is symmetric the student's "t" confidence interval for a mean is superior to the studied non-parametric bootstrap confidence intervals. When data is skewed and for sample sizes n greater than or equal to 10,…

Evaluating the Invariance of Cognitive Profile Patterns Derived from Profile Analysis via Multidimensional Scaling (PAMS): A Bootstrapping Approach

ERIC Educational Resources Information Center

Kim, Se-Kang

2010-01-01

The aim of the current study is to validate the invariance of major profile patterns derived from multidimensional scaling (MDS) by bootstrapping. Profile Analysis via Multidimensional Scaling (PAMS) was employed to obtain profiles and bootstrapping was used to construct the sampling distributions of the profile coordinates and the empirical…

Comparison of Parametric and Nonparametric Bootstrap Methods for Estimating Random Error in Equipercentile Equating

ERIC Educational Resources Information Center

Cui, Zhongmin; Kolen, Michael J.

2008-01-01

This article considers two methods of estimating standard errors of equipercentile equating: the parametric bootstrap method and the nonparametric bootstrap method. Using a simulation study, these two methods are compared under three sample sizes (300, 1,000, and 3,000), for two test content areas (the Iowa Tests of Basic Skills Maps and Diagrams…

Test of bootstrap current models using high- β p EAST-demonstration plasmas on DIII-D

DOE PAGES

Ren, Qilong; Lao, Lang L.; Garofalo, Andrea M.; ...

2015-01-12

Magnetic measurements together with kinetic profile and motional Stark effect measurements are used in full kinetic equilibrium reconstructions to test the Sauter and NEO bootstrap current models in a DIII-D high-more » $${{\\beta}_{\\text{p}}}$$ EAST-demonstration experiment. This aims at developing on DIII-D a high bootstrap current scenario to be extended on EAST for a demonstration of true steady-state at high performance and uses EAST-similar operational conditions: plasma shape, plasma current, toroidal magnetic field, total heating power and current ramp-up rate. It is found that the large edge bootstrap current in these high-$${{\\beta}_{\\text{p}}}$$ plasmas allows the use of magnetic measurements to clearly distinguish the two bootstrap current models. In these high collisionality and high-$${{\\beta}_{\\text{p}}}$$ plasmas, the Sauter model overpredicts the peak of the edge current density by about 30%, while the first-principle kinetic NEO model is in close agreement with the edge current density of the reconstructed equilibrium. Furthermore, these results are consistent with recent work showing that the Sauter model largely overestimates the edge bootstrap current at high collisionality.« less

Modelisation de l'historique d'operation de groupes turbine-alternateur

NASA Astrophysics Data System (ADS)

Szczota, Mickael

Because of their ageing fleet, the utility managers are increasingly in needs of tools that can help them to plan efficiently maintenance operations. Hydro-Quebec started a project that aim to foresee the degradation of their hydroelectric runner, and use that information to classify the generating unit. That classification will help to know which generating unit is more at risk to undergo a major failure. Cracks linked to the fatigue phenomenon are a predominant degradation mode and the loading sequences applied to the runner is a parameter impacting the crack growth. So, the aim of this memoir is to create a generator able to generate synthetic loading sequences that are statistically equivalent to the observed history. Those simulated sequences will be used as input in a life assessment model. At first, we describe how the generating units are operated by Hydro-Quebec and analyse the available data, the analysis shows that the data are non-stationnary. Then, we review modelisation and validation methods. In the following chapter a particular attention is given to a precise description of the validation and comparison procedure. Then, we present the comparison of three kind of model : Discrete Time Markov Chains, Discrete Time Semi-Markov Chains and the Moving Block Bootstrap. For the first two models, we describe how to take account for the non-stationnarity. Finally, we show that the Markov Chain is not adapted for our case, and that the Semi-Markov chains are better when they include the non-stationnarity. The final choice between Semi-Markov Chains and the Moving Block Bootstrap depends of the user. But, with a long term vision we recommend the use of Semi-Markov chains for their flexibility. Keywords: Stochastic models, Models validation, Reliability, Semi-Markov Chains, Markov Chains, Bootstrap

Electron transport fluxes in potato plateau regime

NASA Astrophysics Data System (ADS)

Shaing, K. C.; Hazeltine, R. D.

1997-12-01

Electron transport fluxes in the potato plateau regime are calculated from the solutions of the drift kinetic equation and fluid equations. It is found that the bootstrap current density remains finite in the region close to the magnetic axis, although it decreases with increasing collision frequency. This finite amount of the bootstrap current in the relatively collisional regime is important in modeling tokamak startup with 100% bootstrap current.

Bootstrap current in a tokamak

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kessel, C.E.

1994-03-01

The bootstrap current in a tokamak is examined by implementing the Hirshman-Sigmar model and comparing the predicted current profiles with those from two popular approximations. The dependences of the bootstrap current profile on the plasma properties are illustrated. The implications for steady state tokamaks are presented through two constraints; the pressure profile must be peaked and {beta}{sub p} must be kept below a critical value.

Proposed hardware architectures of particle filter for object tracking

NASA Astrophysics Data System (ADS)

Abd El-Halym, Howida A.; Mahmoud, Imbaby Ismail; Habib, SED

2012-12-01

In this article, efficient hardware architectures for particle filter (PF) are presented. We propose three different architectures for Sequential Importance Resampling Filter (SIRF) implementation. The first architecture is a two-step sequential PF machine, where particle sampling, weight, and output calculations are carried out in parallel during the first step followed by sequential resampling in the second step. For the weight computation step, a piecewise linear function is used instead of the classical exponential function. This decreases the complexity of the architecture without degrading the results. The second architecture speeds up the resampling step via a parallel, rather than a serial, architecture. This second architecture targets a balance between hardware resources and the speed of operation. The third architecture implements the SIRF as a distributed PF composed of several processing elements and central unit. All the proposed architectures are captured using VHDL synthesized using Xilinx environment, and verified using the ModelSim simulator. Synthesis results confirmed the resource reduction and speed up advantages of our architectures.

Automatic bearing fault diagnosis of permanent magnet synchronous generators in wind turbines subjected to noise interference

NASA Astrophysics Data System (ADS)

Guo, Jun; Lu, Siliang; Zhai, Chao; He, Qingbo

2018-02-01

An automatic bearing fault diagnosis method is proposed for permanent magnet synchronous generators (PMSGs), which are widely installed in wind turbines subjected to low rotating speeds, speed fluctuations, and electrical device noise interferences. The mechanical rotating angle curve is first extracted from the phase current of a PMSG by sequentially applying a series of algorithms. The synchronous sampled vibration signal of the fault bearing is then resampled in the angular domain according to the obtained rotating phase information. Considering that the resampled vibration signal is still overwhelmed by heavy background noise, an adaptive stochastic resonance filter is applied to the resampled signal to enhance the fault indicator and facilitate bearing fault identification. Two types of fault bearings with different fault sizes in a PMSG test rig are subjected to experiments to test the effectiveness of the proposed method. The proposed method is fully automated and thus shows potential for convenient, highly efficient and in situ bearing fault diagnosis for wind turbines subjected to harsh environments.

Modified Polar-Format Software for Processing SAR Data

NASA Technical Reports Server (NTRS)

Chen, Curtis

2003-01-01

HMPF is a computer program that implements a modified polar-format algorithm for processing data from spaceborne synthetic-aperture radar (SAR) systems. Unlike prior polar-format processing algorithms, this algorithm is based on the assumption that the radar signal wavefronts are spherical rather than planar. The algorithm provides for resampling of SAR pulse data from slant range to radial distance from the center of a reference sphere that is nominally the local Earth surface. Then, invoking the projection-slice theorem, the resampled pulse data are Fourier-transformed over radial distance, arranged in the wavenumber domain according to the acquisition geometry, resampled to a Cartesian grid, and inverse-Fourier-transformed. The result of this process is the focused SAR image. HMPF, and perhaps other programs that implement variants of the algorithm, may give better accuracy than do prior algorithms for processing strip-map SAR data from high altitudes and may give better phase preservation relative to prior polar-format algorithms for processing spotlight-mode SAR data.

Bayesian network ensemble as a multivariate strategy to predict radiation pneumonitis risk

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, Sangkyu, E-mail: sangkyu.lee@mail.mcgill.ca; Ybarra, Norma; Jeyaseelan, Krishinima

2015-05-15

Purpose: Prediction of radiation pneumonitis (RP) has been shown to be challenging due to the involvement of a variety of factors including dose–volume metrics and radiosensitivity biomarkers. Some of these factors are highly correlated and might affect prediction results when combined. Bayesian network (BN) provides a probabilistic framework to represent variable dependencies in a directed acyclic graph. The aim of this study is to integrate the BN framework and a systems’ biology approach to detect possible interactions among RP risk factors and exploit these relationships to enhance both the understanding and prediction of RP. Methods: The authors studied 54 nonsmall-cellmore » lung cancer patients who received curative 3D-conformal radiotherapy. Nineteen RP events were observed (common toxicity criteria for adverse events grade 2 or higher). Serum concentration of the following four candidate biomarkers were measured at baseline and midtreatment: alpha-2-macroglobulin, angiotensin converting enzyme (ACE), transforming growth factor, interleukin-6. Dose-volumetric and clinical parameters were also included as covariates. Feature selection was performed using a Markov blanket approach based on the Koller–Sahami filter. The Markov chain Monte Carlo technique estimated the posterior distribution of BN graphs built from the observed data of the selected variables and causality constraints. RP probability was estimated using a limited number of high posterior graphs (ensemble) and was averaged for the final RP estimate using Bayes’ rule. A resampling method based on bootstrapping was applied to model training and validation in order to control under- and overfit pitfalls. Results: RP prediction power of the BN ensemble approach reached its optimum at a size of 200. The optimized performance of the BN model recorded an area under the receiver operating characteristic curve (AUC) of 0.83, which was significantly higher than multivariate logistic regression (0.77), mean heart dose (0.69), and a pre-to-midtreatment change in ACE (0.66). When RP prediction was made only with pretreatment information, the AUC ranged from 0.76 to 0.81 depending on the ensemble size. Bootstrap validation of graph features in the ensemble quantified confidence of association between variables in the graphs where ten interactions were statistically significant. Conclusions: The presented BN methodology provides the flexibility to model hierarchical interactions between RP covariates, which is applied to probabilistic inference on RP. The authors’ preliminary results demonstrate that such framework combined with an ensemble method can possibly improve prediction of RP under real-life clinical circumstances such as missing data or treatment plan adaptation.« less

Predicting Directly Measured Trunk and Upper Arm Postures in Paper Mill Work From Administrative Data, Workers' Ratings and Posture Observations.

PubMed

Heiden, Marina; Garza, Jennifer; Trask, Catherine; Mathiassen, Svend Erik

2017-03-01

A cost-efficient approach for assessing working postures could be to build statistical models for predicting results of direct measurements from cheaper data, and apply these models to samples in which only the latter data are available. The present study aimed to build and assess the performance of statistical models predicting inclinometer-assessed trunk and arm posture among paper mill workers. Separate models were built using administrative data, workers' ratings of their exposure, and observations of the work from video recordings as predictors. Trunk and upper arm postures were measured using inclinometry on 28 paper mill workers during three work shifts each. Simultaneously, the workers were video filmed, and their postures were assessed by observation of the videos afterwards. Workers' ratings of exposure, and administrative data on staff and production during the shifts were also collected. Linear mixed models were fitted for predicting inclinometer-assessed exposure variables (median trunk and upper arm angle, proportion of time with neutral trunk and upper arm posture, and frequency of periods in neutral trunk and upper arm inclination) from administrative data, workers' ratings, and observations, respectively. Performance was evaluated in terms of Akaike information criterion, proportion of variance explained (R2), and standard error (SE) of the model estimate. For models performing well, validity was assessed by bootstrap resampling. Models based on administrative data performed poorly (R2 ≤ 15%) and would not be useful for assessing posture in this population. Models using workers' ratings of exposure performed slightly better (8% ≤ R2 ≤ 27% for trunk posture; 14% ≤ R2 ≤ 36% for arm posture). The best model was obtained when using observational data for predicting frequency of periods with neutral arm inclination. It explained 56% of the variance in the postural exposure, and its SE was 5.6. Bootstrap validation of this model showed similar expected performance in other samples (5th-95th percentile: R2 = 45-63%; SE = 5.1-6.2). Observational data had a better ability to predict inclinometer-assessed upper arm exposures than workers' ratings or administrative data. However, observational measurements are typically more expensive to obtain. The results encourage analyses of the cost-efficiency of modeling based on administrative data, workers' ratings, and observation, compared to the performance and cost of measuring exposure directly. © The Author 2017. Published by Oxford University Press on behalf of the British Occupational Hygiene Society.

Association of Adjuvant Chemotherapy With Survival in Patients With Stage II or III Gastric Cancer

PubMed Central

Jiang, Yuming; Li, Tuanjie; Liang, Xiaoling; Hu, Yanfeng; Huang, Lei; Liao, Zhenchen; Zhao, Liying; Han, Zhen; Zhu, Shuguang; Wang, Menglan; Xu, Yangwei; Qi, Xiaolong; Liu, Hao; Yang, Yang; Yu, Jiang; Liu, Wei; Cai, Shirong

2017-01-01

Importance The current staging system of gastric cancer is not adequate for defining a prognosis and predicting the patients most likely to benefit from chemotherapy. Objective To construct a survival prediction model based on specific tumor and patient characteristics that enables individualized predictions of the net survival benefit of adjuvant chemotherapy for patients with stage II or stage III gastric cancer. Design, Setting, and Participants In this multicenter retrospective analysis, a survival prediction model was constructed using data from a training cohort of 746 patients with stage II or stage III gastric cancer who satisfied the study’s inclusion criteria and underwent surgery between January 1, 2004, and December 31, 2012, at Nanfang Hospital in Guangzhou, China. Patient and tumor characteristics were included as covariates, and their association with overall survival and disease-free survival with and without adjuvant chemotherapy was assessed. The model was internally validated for discrimination and calibration using bootstrap resampling. To externally validate the model, data were included from a validation cohort of 973 patients with stage II or stage III gastric cancer who met the inclusion criteria and underwent surgery at First Affiliated Hospital in Guangzhou, China, and at West China Hospital of Sichuan Hospital in Chendu, China, between January 1, 2000, and June 30, 2009. Data were analyzed from July 10, 2016, to September 1, 2016. Main Outcomes and Measures Concordance index and decision curve analysis for each measure associated with postoperative overall survival and disease-free survival. Results Of the 1719 patients analyzed, 1183 (68.8%) were men and 536 (31.2%) were women and the median (interquartile range) age was 57 (49-66) years. Age, location, differentiation, carcinoembryonic antigen, cancer antigen 19-9, depth of invasion, lymph node metastasis, and adjuvant chemotherapy were significantly associated with overall survival and disease-free survival, with P < .05. The survival prediction model demonstrated good calibration and discrimination, with relatively high bootstrap-corrected concordance indexes in the training and validation cohorts. In the validation cohort, the concordance index for overall survival was 0.693 (95% CI, 0.671-0.715) and for disease-free survival was 0.704 (95% CI, 0.681-0.728). Two nomograms and a calculating tool were built on the basis of specific input variables to estimate an individual’s net survival gain attributable to adjuvant chemotherapy. Conclusions and Relevance The survival prediction model can be used to make individualized predictions of the expected survival benefit from the addition of adjuvant chemotherapy for patients with stage II or stage III gastric cancer. PMID:28538950

Assessing neural activity related to decision-making through flexible odds ratio curves and their derivatives.

PubMed

Roca-Pardiñas, Javier; Cadarso-Suárez, Carmen; Pardo-Vazquez, Jose L; Leboran, Victor; Molenberghs, Geert; Faes, Christel; Acuña, Carlos

2011-06-30

It is well established that neural activity is stochastically modulated over time. Therefore, direct comparisons across experimental conditions and determination of change points or maximum firing rates are not straightforward. This study sought to compare temporal firing probability curves that may vary across groups defined by different experimental conditions. Odds-ratio (OR) curves were used as a measure of comparison, and the main goal was to provide a global test to detect significant differences of such curves through the study of their derivatives. An algorithm is proposed that enables ORs based on generalized additive models, including factor-by-curve-type interactions to be flexibly estimated. Bootstrap methods were used to draw inferences from the derivatives curves, and binning techniques were applied to speed up computation in the estimation and testing processes. A simulation study was conducted to assess the validity of these bootstrap-based tests. This methodology was applied to study premotor ventral cortex neural activity associated with decision-making. The proposed statistical procedures proved very useful in revealing the neural activity correlates of decision-making in a visual discrimination task. Copyright © 2011 John Wiley & Sons, Ltd.

One- and two-stage Arrhenius models for pharmaceutical shelf life prediction.

PubMed

Fan, Zhewen; Zhang, Lanju

2015-01-01

One of the most challenging aspects of the pharmaceutical development is the demonstration and estimation of chemical stability. It is imperative that pharmaceutical products be stable for two or more years. Long-term stability studies are required to support such shelf life claim at registration. However, during drug development to facilitate formulation and dosage form selection, an accelerated stability study with stressed storage condition is preferred to quickly obtain a good prediction of shelf life under ambient storage conditions. Such a prediction typically uses Arrhenius equation that describes relationship between degradation rate and temperature (and humidity). Existing methods usually rely on the assumption of normality of the errors. In addition, shelf life projection is usually based on confidence band of a regression line. However, the coverage probability of a method is often overlooked or under-reported. In this paper, we introduce two nonparametric bootstrap procedures for shelf life estimation based on accelerated stability testing, and compare them with a one-stage nonlinear Arrhenius prediction model. Our simulation results demonstrate that one-stage nonlinear Arrhenius method has significant lower coverage than nominal levels. Our bootstrap method gave better coverage and led to a shelf life prediction closer to that based on long-term stability data.

Multi-baseline bootstrapping at the Navy precision optical interferometer

NASA Astrophysics Data System (ADS)

Armstrong, J. T.; Schmitt, H. R.; Mozurkewich, D.; Jorgensen, A. M.; Muterspaugh, M. W.; Baines, E. K.; Benson, J. A.; Zavala, Robert T.; Hutter, D. J.

2014-07-01

The Navy Precision Optical Interferometer (NPOI) was designed from the beginning to support baseline boot- strapping with equally-spaced array elements. The motivation was the desire to image the surfaces of resolved stars with the maximum resolution possible with a six-element array. Bootstrapping two baselines together to track fringes on a third baseline has been used at the NPOI for many years, but the capabilities of the fringe tracking software did not permit us to bootstrap three or more baselines together. Recently, both a new backend (VISION; Tennessee State Univ.) and new hardware and firmware (AZ Embedded Systems and New Mexico Tech, respectively) for the current hybrid backend have made multi-baseline bootstrapping possible.

Bootstrap and fast wave current drive for tokamak reactors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ehst, D.A.

1991-09-01

Using the multi-species neoclassical treatment of Hirshman and Sigmar we study steady state bootstrap equilibria with seed currents provided by low frequency (ICRF) fast waves and with additional surface current density driven by lower hybrid waves. This study applies to reactor plasmas of arbitrary aspect ratio. IN one limit the bootstrap component can supply nearly the total equilibrium current with minimal driving power (< 20 MW). However, for larger total currents considerable driving power is required (for ITER: I{sub o} = 18 MA needs P{sub FW} = 15 MW, P{sub LH} = 75 MW). A computational survey of bootstrap fractionmore » and current drive efficiency is presented. 11 refs., 8 figs.« less

Semantic Drift in Espresso-style Bootstrapping: Graph-theoretic Analysis and Evaluation in Word Sense Disambiguation

NASA Astrophysics Data System (ADS)

Komachi, Mamoru; Kudo, Taku; Shimbo, Masashi; Matsumoto, Yuji

Bootstrapping has a tendency, called semantic drift, to select instances unrelated to the seed instances as the iteration proceeds. We demonstrate the semantic drift of Espresso-style bootstrapping has the same root as the topic drift of Kleinberg's HITS, using a simplified graph-based reformulation of bootstrapping. We confirm that two graph-based algorithms, the von Neumann kernels and the regularized Laplacian, can reduce the effect of semantic drift in the task of word sense disambiguation (WSD) on Senseval-3 English Lexical Sample Task. Proposed algorithms achieve superior performance to Espresso and previous graph-based WSD methods, even though the proposed algorithms have less parameters and are easy to calibrate.

Resampling to accelerate cross-correlation searches for continuous gravitational waves from binary systems

NASA Astrophysics Data System (ADS)

Meadors, Grant David; Krishnan, Badri; Papa, Maria Alessandra; Whelan, John T.; Zhang, Yuanhao

2018-02-01

Continuous-wave (CW) gravitational waves (GWs) call for computationally-intensive methods. Low signal-to-noise ratio signals need templated searches with long coherent integration times and thus fine parameter-space resolution. Longer integration increases sensitivity. Low-mass x-ray binaries (LMXBs) such as Scorpius X-1 (Sco X-1) may emit accretion-driven CWs at strains reachable by current ground-based observatories. Binary orbital parameters induce phase modulation. This paper describes how resampling corrects binary and detector motion, yielding source-frame time series used for cross-correlation. Compared to the previous, detector-frame, templated cross-correlation method, used for Sco X-1 on data from the first Advanced LIGO observing run (O1), resampling is about 20 × faster in the costliest, most-sensitive frequency bands. Speed-up factors depend on integration time and search setup. The speed could be reinvested into longer integration with a forecast sensitivity gain, 20 to 125 Hz median, of approximately 51%, or from 20 to 250 Hz, 11%, given the same per-band cost and setup. This paper's timing model enables future setup optimization. Resampling scales well with longer integration, and at 10 × unoptimized cost could reach respectively 2.83 × and 2.75 × median sensitivities, limited by spin-wandering. Then an O1 search could yield a marginalized-polarization upper limit reaching torque-balance at 100 Hz. Frequencies from 40 to 140 Hz might be probed in equal observing time with 2 × improved detectors.

Recommended GIS Analysis Methods for Global Gridded Population Data

NASA Astrophysics Data System (ADS)

Frye, C. E.; Sorichetta, A.; Rose, A.

2017-12-01

When using geographic information systems (GIS) to analyze gridded, i.e., raster, population data, analysts need a detailed understanding of several factors that affect raster data processing, and thus, the accuracy of the results. Global raster data is most often provided in an unprojected state, usually in the WGS 1984 geographic coordinate system. Most GIS functions and tools evaluate data based on overlay relationships (area) or proximity (distance). Area and distance for global raster data can be either calculated directly using the various earth ellipsoids or after transforming the data to equal-area/equidistant projected coordinate systems to analyze all locations equally. However, unlike when projecting vector data, not all projected coordinate systems can support such analyses equally, and the process of transforming raster data from one coordinate space to another often results unmanaged loss of data through a process called resampling. Resampling determines which values to use in the result dataset given an imperfect locational match in the input dataset(s). Cell size or resolution, registration, resampling method, statistical type, and whether the raster represents continuous or discreet information potentially influence the quality of the result. Gridded population data represent estimates of population in each raster cell, and this presentation will provide guidelines for accurately transforming population rasters for analysis in GIS. Resampling impacts the display of high resolution global gridded population data, and we will discuss how to properly handle pyramid creation using the Aggregate tool with the sum option to create overviews for mosaic datasets.

A comparison of resampling schemes for estimating model observer performance with small ensembles

NASA Astrophysics Data System (ADS)

Elshahaby, Fatma E. A.; Jha, Abhinav K.; Ghaly, Michael; Frey, Eric C.

2017-09-01

In objective assessment of image quality, an ensemble of images is used to compute the 1st and 2nd order statistics of the data. Often, only a finite number of images is available, leading to the issue of statistical variability in numerical observer performance. Resampling-based strategies can help overcome this issue. In this paper, we compared different combinations of resampling schemes (the leave-one-out (LOO) and the half-train/half-test (HT/HT)) and model observers (the conventional channelized Hotelling observer (CHO), channelized linear discriminant (CLD) and channelized quadratic discriminant). Observer performance was quantified by the area under the ROC curve (AUC). For a binary classification task and for each observer, the AUC value for an ensemble size of 2000 samples per class served as a gold standard for that observer. Results indicated that each observer yielded a different performance depending on the ensemble size and the resampling scheme. For a small ensemble size, the combination [CHO, HT/HT] had more accurate rankings than the combination [CHO, LOO]. Using the LOO scheme, the CLD and CHO had similar performance for large ensembles. However, the CLD outperformed the CHO and gave more accurate rankings for smaller ensembles. As the ensemble size decreased, the performance of the [CHO, LOO] combination seriously deteriorated as opposed to the [CLD, LOO] combination. Thus, it might be desirable to use the CLD with the LOO scheme when smaller ensemble size is available.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shaing, K.C.; Hazeltine, R.D.

Electron transport fluxes in the potato plateau regime are calculated from the solutions of the drift kinetic equation and fluid equations. It is found that the bootstrap current density remains finite in the region close to the magnetic axis, although it decreases with increasing collision frequency. This finite amount of the bootstrap current in the relatively collisional regime is important in modeling tokamak startup with 100{percent} bootstrap current. {copyright} {ital 1997 American Institute of Physics.}

Primer ID Validates Template Sampling Depth and Greatly Reduces the Error Rate of Next-Generation Sequencing of HIV-1 Genomic RNA Populations

PubMed Central

Zhou, Shuntai; Jones, Corbin; Mieczkowski, Piotr

2015-01-01

ABSTRACT Validating the sampling depth and reducing sequencing errors are critical for studies of viral populations using next-generation sequencing (NGS). We previously described the use of Primer ID to tag each viral RNA template with a block of degenerate nucleotides in the cDNA primer. We now show that low-abundance Primer IDs (offspring Primer IDs) are generated due to PCR/sequencing errors. These artifactual Primer IDs can be removed using a cutoff model for the number of reads required to make a template consensus sequence. We have modeled the fraction of sequences lost due to Primer ID resampling. For a typical sequencing run, less than 10% of the raw reads are lost to offspring Primer ID filtering and resampling. The remaining raw reads are used to correct for PCR resampling and sequencing errors. We also demonstrate that Primer ID reveals bias intrinsic to PCR, especially at low template input or utilization. cDNA synthesis and PCR convert ca. 20% of RNA templates into recoverable sequences, and 30-fold sequence coverage recovers most of these template sequences. We have directly measured the residual error rate to be around 1 in 10,000 nucleotides. We use this error rate and the Poisson distribution to define the cutoff to identify preexisting drug resistance mutations at low abundance in an HIV-infected subject. Collectively, these studies show that >90% of the raw sequence reads can be used to validate template sampling depth and to dramatically reduce the error rate in assessing a genetically diverse viral population using NGS. IMPORTANCE Although next-generation sequencing (NGS) has revolutionized sequencing strategies, it suffers from serious limitations in defining sequence heterogeneity in a genetically diverse population, such as HIV-1 due to PCR resampling and PCR/sequencing errors. The Primer ID approach reveals the true sampling depth and greatly reduces errors. Knowing the sampling depth allows the construction of a model of how to maximize the recovery of sequences from input templates and to reduce resampling of the Primer ID so that appropriate multiplexing can be included in the experimental design. With the defined sampling depth and measured error rate, we are able to assign cutoffs for the accurate detection of minority variants in viral populations. This approach allows the power of NGS to be realized without having to guess about sampling depth or to ignore the problem of PCR resampling, while also being able to correct most of the errors in the data set. PMID:26041299

Reconstruction of dynamical systems from resampled point processes produced by neuron models

NASA Astrophysics Data System (ADS)

Pavlova, Olga N.; Pavlov, Alexey N.

2018-04-01

Characterization of dynamical features of chaotic oscillations from point processes is based on embedding theorems for non-uniformly sampled signals such as the sequences of interspike intervals (ISIs). This theoretical background confirms the ability of attractor reconstruction from ISIs generated by chaotically driven neuron models. The quality of such reconstruction depends on the available length of the analyzed dataset. We discuss how data resampling improves the reconstruction for short amount of data and show that this effect is observed for different types of mechanisms for spike generation.

Early-childhood housing mobility and subsequent PTSD in adolescence: a Moving to Opportunity reanalysis.

PubMed

Norris, David C; Wilson, Andrew

2016-01-01

In a 2014 report on adolescent mental health outcomes in the Moving to Opportunity for Fair Housing Demonstration (MTO), Kessler et al. reported that, at 10- to 15-year follow-up, boys from households randomized to an experimental housing voucher intervention experienced 12-month prevalence of post-traumatic stress disorder (PTSD) at several times the rate of boys from control households. We reanalyze this finding here, bringing to light a PTSD outcome imputation procedure used in the original analysis, but not described in the study report. By bootstrapping with repeated draws from the frequentist sampling distribution of the imputation model used by Kessler et al., and by varying two pseudorandom number generator seeds that fed their analysis, we account for several purely statistical components of the uncertainty inherent in their imputation procedure. We also discuss other sources of uncertainty in this procedure that were not accessible to a formal reanalysis.

Biases in Time-Averaged Field and Paleosecular Variation Studies

NASA Astrophysics Data System (ADS)

Johnson, C. L.; Constable, C.

2009-12-01

Challenges to constructing time-averaged field (TAF) and paleosecular variation (PSV) models of Earth’s magnetic field over million year time scales are the uneven geographical and temporal distribution of paleomagnetic data and the absence of full vector records of the magnetic field variability at any given site. Recent improvements in paleomagnetic data sets now allow regional assessment of the biases introduced by irregular temporal sampling and the absence of full vector information. We investigate these effects over the past few Myr for regions with large paleomagnetic data sets, where the TAF and/or PSV have been of previous interest (e.g., significant departures of the TAF from the field predicted by a geocentric axial dipole). We calculate the effects of excluding paleointensity data from TAF calculations, and find these to be small. For example, at Hawaii, we find that for the past 50 ka, estimates of the TAF direction are minimally affected if only paleodirectional data versus the full paleofield vector are used. We use resampling techniques to investigate biases incurred by the uneven temporal distribution. Key to the latter issue is temporal information on a site-by-site basis. At Hawaii, resampling of the paleodirectional data onto a uniform temporal distribution, assuming no error in the site ages, reduces the magnitude of the inclination anomaly for the Brunhes, Gauss and Matuyama epochs. However inclusion of age errors in the sampling procedure leads to TAF estimates that are close to those reported for the original data sets. We discuss the implications of our results for global field models.

Bayesian Optimization for Neuroimaging Pre-processing in Brain Age Classification and Prediction

PubMed Central

Lancaster, Jenessa; Lorenz, Romy; Leech, Rob; Cole, James H.

2018-01-01

Neuroimaging-based age prediction using machine learning is proposed as a biomarker of brain aging, relating to cognitive performance, health outcomes and progression of neurodegenerative disease. However, even leading age-prediction algorithms contain measurement error, motivating efforts to improve experimental pipelines. T1-weighted MRI is commonly used for age prediction, and the pre-processing of these scans involves normalization to a common template and resampling to a common voxel size, followed by spatial smoothing. Resampling parameters are often selected arbitrarily. Here, we sought to improve brain-age prediction accuracy by optimizing resampling parameters using Bayesian optimization. Using data on N = 2003 healthy individuals (aged 16–90 years) we trained support vector machines to (i) distinguish between young (<22 years) and old (>50 years) brains (classification) and (ii) predict chronological age (regression). We also evaluated generalisability of the age-regression model to an independent dataset (CamCAN, N = 648, aged 18–88 years). Bayesian optimization was used to identify optimal voxel size and smoothing kernel size for each task. This procedure adaptively samples the parameter space to evaluate accuracy across a range of possible parameters, using independent sub-samples to iteratively assess different parameter combinations to arrive at optimal values. When distinguishing between young and old brains a classification accuracy of 88.1% was achieved, (optimal voxel size = 11.5 mm3, smoothing kernel = 2.3 mm). For predicting chronological age, a mean absolute error (MAE) of 5.08 years was achieved, (optimal voxel size = 3.73 mm3, smoothing kernel = 3.68 mm). This was compared to performance using default values of 1.5 mm3 and 4mm respectively, resulting in MAE = 5.48 years, though this 7.3% improvement was not statistically significant. When assessing generalisability, best performance was achieved when applying the entire Bayesian optimization framework to the new dataset, out-performing the parameters optimized for the initial training dataset. Our study outlines the proof-of-principle that neuroimaging models for brain-age prediction can use Bayesian optimization to derive case-specific pre-processing parameters. Our results suggest that different pre-processing parameters are selected when optimization is conducted in specific contexts. This potentially motivates use of optimization techniques at many different points during the experimental process, which may improve statistical sensitivity and reduce opportunities for experimenter-led bias. PMID:29483870

Photon-counting hexagonal pixel array CdTe detector: Spatial resolution characteristics for image-guided interventional applications

PubMed Central

Shrestha, Suman; Karellas, Andrew; Shi, Linxi; Gounis, Matthew J.; Bellazzini, Ronaldo; Spandre, Gloria; Brez, Alessandro; Minuti, Massimo

2016-01-01

Purpose: High-resolution, photon-counting, energy-resolved detector with fast-framing capability can facilitate simultaneous acquisition of precontrast and postcontrast images for subtraction angiography without pixel registration artifacts and can facilitate high-resolution real-time imaging during image-guided interventions. Hence, this study was conducted to determine the spatial resolution characteristics of a hexagonal pixel array photon-counting cadmium telluride (CdTe) detector. Methods: A 650 μm thick CdTe Schottky photon-counting detector capable of concurrently acquiring up to two energy-windowed images was operated in a single energy-window mode to include photons of 10 keV or higher. The detector had hexagonal pixels with apothem of 30 μm resulting in pixel pitch of 60 and 51.96 μm along the two orthogonal directions. The detector was characterized at IEC-RQA5 spectral conditions. Linear response of the detector was determined over the air kerma rate relevant to image-guided interventional procedures ranging from 1.3 nGy/frame to 91.4 μGy/frame. Presampled modulation transfer was determined using a tungsten edge test device. The edge-spread function and the finely sampled line spread function accounted for hexagonal sampling, from which the presampled modulation transfer function (MTF) was determined. Since detectors with hexagonal pixels require resampling to square pixels for distortion-free display, the optimal square pixel size was determined by minimizing the root-mean-squared-error of the aperture functions for the square and hexagonal pixels up to the Nyquist limit. Results: At Nyquist frequencies of 8.33 and 9.62 cycles/mm along the apothem and orthogonal to the apothem directions, the modulation factors were 0.397 and 0.228, respectively. For the corresponding axis, the limiting resolution defined as 10% MTF occurred at 13.3 and 12 cycles/mm, respectively. Evaluation of the aperture functions yielded an optimal square pixel size of 54 μm. After resampling to 54 μm square pixels using trilinear interpolation, the presampled MTF at Nyquist frequency of 9.26 cycles/mm was 0.29 and 0.24 along the orthogonal directions and the limiting resolution (10% MTF) occurred at approximately 12 cycles/mm. Visual analysis of a bar pattern image showed the ability to resolve close to 12 line-pairs/mm and qualitative evaluation of a neurovascular nitinol-stent showed the ability to visualize its struts at clinically relevant conditions. Conclusions: Hexagonal pixel array photon-counting CdTe detector provides high spatial resolution in single-photon counting mode. After resampling to optimal square pixel size for distortion-free display, the spatial resolution is preserved. The dual-energy capabilities of the detector could allow for artifact-free subtraction angiography and basis material decomposition. The proposed high-resolution photon-counting detector with energy-resolving capability can be of importance for several image-guided interventional procedures as well as for pediatric applications. PMID:27147324

Photon-counting hexagonal pixel array CdTe detector: Spatial resolution characteristics for image-guided interventional applications.

PubMed

Vedantham, Srinivasan; Shrestha, Suman; Karellas, Andrew; Shi, Linxi; Gounis, Matthew J; Bellazzini, Ronaldo; Spandre, Gloria; Brez, Alessandro; Minuti, Massimo

2016-05-01

High-resolution, photon-counting, energy-resolved detector with fast-framing capability can facilitate simultaneous acquisition of precontrast and postcontrast images for subtraction angiography without pixel registration artifacts and can facilitate high-resolution real-time imaging during image-guided interventions. Hence, this study was conducted to determine the spatial resolution characteristics of a hexagonal pixel array photon-counting cadmium telluride (CdTe) detector. A 650 μm thick CdTe Schottky photon-counting detector capable of concurrently acquiring up to two energy-windowed images was operated in a single energy-window mode to include photons of 10 keV or higher. The detector had hexagonal pixels with apothem of 30 μm resulting in pixel pitch of 60 and 51.96 μm along the two orthogonal directions. The detector was characterized at IEC-RQA5 spectral conditions. Linear response of the detector was determined over the air kerma rate relevant to image-guided interventional procedures ranging from 1.3 nGy/frame to 91.4 μGy/frame. Presampled modulation transfer was determined using a tungsten edge test device. The edge-spread function and the finely sampled line spread function accounted for hexagonal sampling, from which the presampled modulation transfer function (MTF) was determined. Since detectors with hexagonal pixels require resampling to square pixels for distortion-free display, the optimal square pixel size was determined by minimizing the root-mean-squared-error of the aperture functions for the square and hexagonal pixels up to the Nyquist limit. At Nyquist frequencies of 8.33 and 9.62 cycles/mm along the apothem and orthogonal to the apothem directions, the modulation factors were 0.397 and 0.228, respectively. For the corresponding axis, the limiting resolution defined as 10% MTF occurred at 13.3 and 12 cycles/mm, respectively. Evaluation of the aperture functions yielded an optimal square pixel size of 54 μm. After resampling to 54 μm square pixels using trilinear interpolation, the presampled MTF at Nyquist frequency of 9.26 cycles/mm was 0.29 and 0.24 along the orthogonal directions and the limiting resolution (10% MTF) occurred at approximately 12 cycles/mm. Visual analysis of a bar pattern image showed the ability to resolve close to 12 line-pairs/mm and qualitative evaluation of a neurovascular nitinol-stent showed the ability to visualize its struts at clinically relevant conditions. Hexagonal pixel array photon-counting CdTe detector provides high spatial resolution in single-photon counting mode. After resampling to optimal square pixel size for distortion-free display, the spatial resolution is preserved. The dual-energy capabilities of the detector could allow for artifact-free subtraction angiography and basis material decomposition. The proposed high-resolution photon-counting detector with energy-resolving capability can be of importance for several image-guided interventional procedures as well as for pediatric applications.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vedantham, Srinivasan; Shrestha, Suman; Karellas, Andrew, E-mail: andrew.karellas@umassmed.edu

Purpose: High-resolution, photon-counting, energy-resolved detector with fast-framing capability can facilitate simultaneous acquisition of precontrast and postcontrast images for subtraction angiography without pixel registration artifacts and can facilitate high-resolution real-time imaging during image-guided interventions. Hence, this study was conducted to determine the spatial resolution characteristics of a hexagonal pixel array photon-counting cadmium telluride (CdTe) detector. Methods: A 650 μm thick CdTe Schottky photon-counting detector capable of concurrently acquiring up to two energy-windowed images was operated in a single energy-window mode to include photons of 10 keV or higher. The detector had hexagonal pixels with apothem of 30 μm resulting in pixelmore » pitch of 60 and 51.96 μm along the two orthogonal directions. The detector was characterized at IEC-RQA5 spectral conditions. Linear response of the detector was determined over the air kerma rate relevant to image-guided interventional procedures ranging from 1.3 nGy/frame to 91.4 μGy/frame. Presampled modulation transfer was determined using a tungsten edge test device. The edge-spread function and the finely sampled line spread function accounted for hexagonal sampling, from which the presampled modulation transfer function (MTF) was determined. Since detectors with hexagonal pixels require resampling to square pixels for distortion-free display, the optimal square pixel size was determined by minimizing the root-mean-squared-error of the aperture functions for the square and hexagonal pixels up to the Nyquist limit. Results: At Nyquist frequencies of 8.33 and 9.62 cycles/mm along the apothem and orthogonal to the apothem directions, the modulation factors were 0.397 and 0.228, respectively. For the corresponding axis, the limiting resolution defined as 10% MTF occurred at 13.3 and 12 cycles/mm, respectively. Evaluation of the aperture functions yielded an optimal square pixel size of 54 μm. After resampling to 54 μm square pixels using trilinear interpolation, the presampled MTF at Nyquist frequency of 9.26 cycles/mm was 0.29 and 0.24 along the orthogonal directions and the limiting resolution (10% MTF) occurred at approximately 12 cycles/mm. Visual analysis of a bar pattern image showed the ability to resolve close to 12 line-pairs/mm and qualitative evaluation of a neurovascular nitinol-stent showed the ability to visualize its struts at clinically relevant conditions. Conclusions: Hexagonal pixel array photon-counting CdTe detector provides high spatial resolution in single-photon counting mode. After resampling to optimal square pixel size for distortion-free display, the spatial resolution is preserved. The dual-energy capabilities of the detector could allow for artifact-free subtraction angiography and basis material decomposition. The proposed high-resolution photon-counting detector with energy-resolving capability can be of importance for several image-guided interventional procedures as well as for pediatric applications.« less

Topical ketoprofen nanogel: artificial neural network optimization, clustered bootstrap validation, and in vivo activity evaluation based on longitudinal dose response modeling.

PubMed

Elkomy, Mohammed H; Elmenshawe, Shahira F; Eid, Hussein M; Ali, Ahmed M A

2016-11-01

This work aimed at investigating the potential of solid lipid nanoparticles (SLN) as carriers for topical delivery of Ketoprofen (KP); evaluating a novel technique incorporating Artificial Neural Network (ANN) and clustered bootstrap for optimization of KP-loaded SLN (KP-SLN); and demonstrating a longitudinal dose response (LDR) modeling-based approach to compare the activity of topical non-steroidal anti-inflammatory drug formulations. KP-SLN was fabricated by a modified emulsion/solvent evaporation method. Box-Behnken design was implemented to study the influence of glycerylpalmitostearate-to-KP ratio, Tween 80, and lecithin concentrations on particle size, entrapment efficiency, and amount of drug permeated through rat skin in 24 hours. Following clustered bootstrap ANN optimization, the optimized KP-SLN was incorporated into an aqueous gel and evaluated for rheology, in vitro release, permeability, skin irritation and in vivo activity using carrageenan-induced rat paw edema model and LDR mathematical model to analyze the time course of anti-inflammatory effect at various application durations. Lipid-to-drug ratio of 7.85 [bootstrap 95%CI: 7.63-8.51], Tween 80 of 1.27% [bootstrap 95%CI: 0.601-2.40%], and Lecithin of 0.263% [bootstrap 95%CI: 0.263-0.328%] were predicted to produce optimal characteristics. Compared with profenid® gel, the optimized KP-SLN gel exhibited slower release, faster permeability, better texture properties, greater efficacy, and similar potency. SLNs are safe and effective permeation enhancers. ANN coupled with clustered bootstrap is a useful method for finding optimal solutions and estimating uncertainty associated with them. LDR models allow mechanistic understanding of comparative in vivo performances of different topical formulations, and help design efficient dermatological bioequivalence assessment methods.

Automatic recognition of 3D GGO CT imaging signs through the fusion of hybrid resampling and layer-wise fine-tuning CNNs.

PubMed

Han, Guanghui; Liu, Xiabi; Zheng, Guangyuan; Wang, Murong; Huang, Shan

2018-06-06

Ground-glass opacity (GGO) is a common CT imaging sign on high-resolution CT, which means the lesion is more likely to be malignant compared to common solid lung nodules. The automatic recognition of GGO CT imaging signs is of great importance for early diagnosis and possible cure of lung cancers. The present GGO recognition methods employ traditional low-level features and system performance improves slowly. Considering the high-performance of CNN model in computer vision field, we proposed an automatic recognition method of 3D GGO CT imaging signs through the fusion of hybrid resampling and layer-wise fine-tuning CNN models in this paper. Our hybrid resampling is performed on multi-views and multi-receptive fields, which reduces the risk of missing small or large GGOs by adopting representative sampling panels and processing GGOs with multiple scales simultaneously. The layer-wise fine-tuning strategy has the ability to obtain the optimal fine-tuning model. Multi-CNN models fusion strategy obtains better performance than any single trained model. We evaluated our method on the GGO nodule samples in publicly available LIDC-IDRI dataset of chest CT scans. The experimental results show that our method yields excellent results with 96.64% sensitivity, 71.43% specificity, and 0.83 F1 score. Our method is a promising approach to apply deep learning method to computer-aided analysis of specific CT imaging signs with insufficient labeled images. Graphical abstract We proposed an automatic recognition method of 3D GGO CT imaging signs through the fusion of hybrid resampling and layer-wise fine-tuning CNN models in this paper. Our hybrid resampling reduces the risk of missing small or large GGOs by adopting representative sampling panels and processing GGOs with multiple scales simultaneously. The layer-wise fine-tuning strategy has ability to obtain the optimal fine-tuning model. Our method is a promising approach to apply deep learning method to computer-aided analysis of specific CT imaging signs with insufficient labeled images.

WE-G-204-03: Photon-Counting Hexagonal Pixel Array CdTe Detector: Optimal Resampling to Square Pixels

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shrestha, S; Vedantham, S; Karellas, A

Purpose: Detectors with hexagonal pixels require resampling to square pixels for distortion-free display of acquired images. In this work, the presampling modulation transfer function (MTF) of a hexagonal pixel array photon-counting CdTe detector for region-of-interest fluoroscopy was measured and the optimal square pixel size for resampling was determined. Methods: A 0.65mm thick CdTe Schottky sensor capable of concurrently acquiring up to 3 energy-windowed images was operated in a single energy-window mode to include ≥10 KeV photons. The detector had hexagonal pixels with apothem of 30 microns resulting in pixel spacing of 60 and 51.96 microns along the two orthogonal directions.more » Images of a tungsten edge test device acquired under IEC RQA5 conditions were double Hough transformed to identify the edge and numerically differentiated. The presampling MTF was determined from the finely sampled line spread function that accounted for the hexagonal sampling. The optimal square pixel size was determined in two ways; the square pixel size for which the aperture function evaluated at the Nyquist frequencies along the two orthogonal directions matched that from the hexagonal pixel aperture functions, and the square pixel size for which the mean absolute difference between the square and hexagonal aperture functions was minimized over all frequencies up to the Nyquist limit. Results: Evaluation of the aperture functions over the entire frequency range resulted in square pixel size of 53 microns with less than 2% difference from the hexagonal pixel. Evaluation of the aperture functions at Nyquist frequencies alone resulted in 54 microns square pixels. For the photon-counting CdTe detector and after resampling to 53 microns square pixels using quadratic interpolation, the presampling MTF at Nyquist frequency of 9.434 cycles/mm along the two directions were 0.501 and 0.507. Conclusion: Hexagonal pixel array photon-counting CdTe detector after resampling to square pixels provides high-resolution imaging suitable for fluoroscopy.« less

Insight from uncertainty: bootstrap-derived diffusion metrics differentially predict memory function among older adults.

PubMed

Vorburger, Robert S; Habeck, Christian G; Narkhede, Atul; Guzman, Vanessa A; Manly, Jennifer J; Brickman, Adam M

2016-01-01

Diffusion tensor imaging suffers from an intrinsic low signal-to-noise ratio. Bootstrap algorithms have been introduced to provide a non-parametric method to estimate the uncertainty of the measured diffusion parameters. To quantify the variability of the principal diffusion direction, bootstrap-derived metrics such as the cone of uncertainty have been proposed. However, bootstrap-derived metrics are not independent of the underlying diffusion profile. A higher mean diffusivity causes a smaller signal-to-noise ratio and, thus, increases the measurement uncertainty. Moreover, the goodness of the tensor model, which relies strongly on the complexity of the underlying diffusion profile, influences bootstrap-derived metrics as well. The presented simulations clearly depict the cone of uncertainty as a function of the underlying diffusion profile. Since the relationship of the cone of uncertainty and common diffusion parameters, such as the mean diffusivity and the fractional anisotropy, is not linear, the cone of uncertainty has a different sensitivity. In vivo analysis of the fornix reveals the cone of uncertainty to be a predictor of memory function among older adults. No significant correlation occurs with the common diffusion parameters. The present work not only demonstrates the cone of uncertainty as a function of the actual diffusion profile, but also discloses the cone of uncertainty as a sensitive predictor of memory function. Future studies should incorporate bootstrap-derived metrics to provide more comprehensive analysis.

Systematic genomic identification of colorectal cancer genes delineating advanced from early clinical stage and metastasis

PubMed Central

2013-01-01

Background Colorectal cancer is the third leading cause of cancer deaths in the United States. The initial assessment of colorectal cancer involves clinical staging that takes into account the extent of primary tumor invasion, determining the number of lymph nodes with metastatic cancer and the identification of metastatic sites in other organs. Advanced clinical stage indicates metastatic cancer, either in regional lymph nodes or in distant organs. While the genomic and genetic basis of colorectal cancer has been elucidated to some degree, less is known about the identity of specific cancer genes that are associated with advanced clinical stage and metastasis. Methods We compiled multiple genomic data types (mutations, copy number alterations, gene expression and methylation status) as well as clinical meta-data from The Cancer Genome Atlas (TCGA). We used an elastic-net regularized regression method on the combined genomic data to identify genetic aberrations and their associated cancer genes that are indicators of clinical stage. We ranked candidate genes by their regression coefficient and level of support from multiple assay modalities. Results A fit of the elastic-net regularized regression to 197 samples and integrated analysis of four genomic platforms identified the set of top gene predictors of advanced clinical stage, including: WRN, SYK, DDX5 and ADRA2C. These genetic features were identified robustly in bootstrap resampling analysis. Conclusions We conducted an analysis integrating multiple genomic features including mutations, copy number alterations, gene expression and methylation. This integrated approach in which one considers all of these genomic features performs better than any individual genomic assay. We identified multiple genes that robustly delineate advanced clinical stage, suggesting their possible role in colorectal cancer metastatic progression. PMID:24308539

State Gun Law Environment and Youth Gun Carrying in the United States.

PubMed

Xuan, Ziming; Hemenway, David

2015-11-01

Gun violence and injuries pose a substantial threat to children and youth in the United States. Existing evidence points to the need for interventions and policies for keeping guns out of the hands of children and youth. (1) To examine the association between state gun law environment and youth gun carrying in the United States, and (2) to determine whether adult gun ownership mediates this association. This was a repeated cross-sectional observational study design with 3 years of data on youth gun carrying from US states. The Youth Risk Behavior Survey comprises data of representative samples of students in grades 9 to 12 from biennial years of 2007, 2009, and 2011. We hypothesized that states with more restrictive gun laws have lower rates of youth gun carrying, and this association is mediated by adult gun ownership. State gun law environment as measured by state gun law score. Youth gun carrying was defined as having carried a gun on at least 1 day during the 30 days before the survey. In the fully adjusted model, a 10-point increase in the state gun law score, which represented a more restrictive gun law environment, was associated with a 9% decrease in the odds of youth gun carrying (adjusted odds ratio [AOR], 0.91 [95% CI, 0.86-0.96]). Adult gun ownership mediated the association between state gun law score and youth gun carrying (AOR, 0.94 [ 95% CI, 0.86-1.01], with 29% attenuation of the regression coefficient from -0.09 to -0.07 based on bootstrap resampling). More restrictive overall gun control policies are associated with a reduced likelihood of youth gun carrying. These findings are relevant to gun policy debates about the critical importance of strengthening overall gun law environment to prevent youth gun carrying.

The empirical Gaia G-band extinction coefficient

NASA Astrophysics Data System (ADS)

Danielski, C.; Babusiaux, C.; Ruiz-Dern, L.; Sartoretti, P.; Arenou, F.

2018-06-01

Context. The first Gaia data release unlocked the access to photometric information for 1.1 billion sources in the G-band. Yet, given the high level of degeneracy between extinction and spectral energy distribution for large passbands such as the Gaia G-band, a correction for the interstellar reddening is needed in order to exploit Gaia data. Aims: The purpose of this manuscript is to provide the empirical estimation of the Gaia G-band extinction coefficient kG for both the red giants and main sequence stars in order to be able to exploit the first data release DR1. Methods: We selected two samples of single stars: one for the red giants and one for the main sequence. Both samples are the result of a cross-match between Gaia DR1 and 2MASS catalogues; they consist of high-quality photometry in the G-, J- and KS-bands. These samples were complemented by temperature and metallicity information retrieved from APOGEE DR13 and LAMOST DR2 surveys, respectively. We implemented a Markov chain Monte Carlo method where we used (G - KS)0 versus Teff and (J - KS)0 versus (G - KS)0, calibration relations to estimate the extinction coefficient kG and we quantify its corresponding confidence interval via bootstrap resampling. We tested our method on samples of red giants and main sequence stars, finding consistent solutions. Results: We present here the determination of the Gaia extinction coefficient through a completely empirical method. Furthermore we provide the scientific community with a formula for measuring the extinction coefficient as a function of stellar effective temperature, the intrinsic colour (G - KS)0, and absorption.

Identifying causal networks linking cancer processes and anti-tumor immunity using Bayesian network inference and metagene constructs.

PubMed

Kaiser, Jacob L; Bland, Cassidy L; Klinke, David J

2016-03-01

Cancer arises from a deregulation of both intracellular and intercellular networks that maintain system homeostasis. Identifying the architecture of these networks and how they are changed in cancer is a pre-requisite for designing drugs to restore homeostasis. Since intercellular networks only appear in intact systems, it is difficult to identify how these networks become altered in human cancer using many of the common experimental models. To overcome this, we used the diversity in normal and malignant human tissue samples from the Cancer Genome Atlas (TCGA) database of human breast cancer to identify the topology associated with intercellular networks in vivo. To improve the underlying biological signals, we constructed Bayesian networks using metagene constructs, which represented groups of genes that are concomitantly associated with different immune and cancer states. We also used bootstrap resampling to establish the significance associated with the inferred networks. In short, we found opposing relationships between cell proliferation and epithelial-to-mesenchymal transformation (EMT) with regards to macrophage polarization. These results were consistent across multiple carcinomas in that proliferation was associated with a type 1 cell-mediated anti-tumor immune response and EMT was associated with a pro-tumor anti-inflammatory response. To address the identifiability of these networks from other datasets, we could identify the relationship between EMT and macrophage polarization with fewer samples when the Bayesian network was generated from malignant samples alone. However, the relationship between proliferation and macrophage polarization was identified with fewer samples when the samples were taken from a combination of the normal and malignant samples. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:470-479, 2016. © 2016 American Institute of Chemical Engineers.

Designing image segmentation studies: Statistical power, sample size and reference standard quality.

PubMed

Gibson, Eli; Hu, Yipeng; Huisman, Henkjan J; Barratt, Dean C

2017-12-01

Segmentation algorithms are typically evaluated by comparison to an accepted reference standard. The cost of generating accurate reference standards for medical image segmentation can be substantial. Since the study cost and the likelihood of detecting a clinically meaningful difference in accuracy both depend on the size and on the quality of the study reference standard, balancing these trade-offs supports the efficient use of research resources. In this work, we derive a statistical power calculation that enables researchers to estimate the appropriate sample size to detect clinically meaningful differences in segmentation accuracy (i.e. the proportion of voxels matching the reference standard) between two algorithms. Furthermore, we derive a formula to relate reference standard errors to their effect on the sample sizes of studies using lower-quality (but potentially more affordable and practically available) reference standards. The accuracy of the derived sample size formula was estimated through Monte Carlo simulation, demonstrating, with 95% confidence, a predicted statistical power within 4% of simulated values across a range of model parameters. This corresponds to sample size errors of less than 4 subjects and errors in the detectable accuracy difference less than 0.6%. The applicability of the formula to real-world data was assessed using bootstrap resampling simulations for pairs of algorithms from the PROMISE12 prostate MR segmentation challenge data set. The model predicted the simulated power for the majority of algorithm pairs within 4% for simulated experiments using a high-quality reference standard and within 6% for simulated experiments using a low-quality reference standard. A case study, also based on the PROMISE12 data, illustrates using the formulae to evaluate whether to use a lower-quality reference standard in a prostate segmentation study. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Reassessing the NTCTCS Staging Systems for Differentiated Thyroid Cancer, Including Age at Diagnosis

PubMed Central

McLeod, Donald S.A.; Jonklaas, Jacqueline; Brierley, James D.; Ain, Kenneth B.; Cooper, David S.; Fein, Henry G.; Haugen, Bryan R.; Ladenson, Paul W.; Magner, James; Ross, Douglas S.; Skarulis, Monica C.; Steward, David L.; Xing, Mingzhao; Litofsky, Danielle R.; Maxon, Harry R.

2015-01-01

Background: Thyroid cancer is unique for having age as a staging variable. Recently, the commonly used age cut-point of 45 years has been questioned. Objective: This study assessed alternate staging systems on the outcome of overall survival, and compared these with current National Thyroid Cancer Treatment Cooperative Study (NTCTCS) staging systems for papillary and follicular thyroid cancer. Methods: A total of 4721 patients with differentiated thyroid cancer were assessed. Five potential alternate staging systems were generated at age cut-points in five-year increments from 35 to 70 years, and tested for model discrimination (Harrell's C-statistic) and calibration (R2). The best five models for papillary and follicular cancer were further tested with bootstrap resampling and significance testing for discrimination. Results: The best five alternate papillary cancer systems had age cut-points of 45–50 years, with the highest scoring model using 50 years. No significant difference in C-statistic was found between the best alternate and current NTCTCS systems (p = 0.200). The best five alternate follicular cancer systems had age cut-points of 50–55 years, with the highest scoring model using 50 years. All five best alternate staging systems performed better compared with the current system (p = 0.003–0.035). There was no significant difference in discrimination between the best alternate system (cut-point age 50 years) and the best system of cut-point age 45 years (p = 0.197). Conclusions: No alternate papillary cancer systems assessed were significantly better than the current system. New alternate staging systems for follicular cancer appear to be better than the current NTCTCS system, although they require external validation. PMID:26203804

Heterogeneity of (18)F-FDG PET combined with expression of EGFR may improve the prognostic stratification of advanced oropharyngeal carcinoma.

PubMed

Wang, Hung-Ming; Cheng, Nai-Ming; Lee, Li-Yu; Fang, Yu-Hua Dean; Chang, Joseph Tung-Chieh; Tsan, Din-Li; Ng, Shu-Hang; Liao, Chun-Ta; Yang, Lan-Yan; Yen, Tzu-Chen

2016-02-01

The Ang's risk profile (based on p16, smoking and cancer stage) is a well-known prognostic factor in oropharyngeal squamous cell carcinoma (OPSCC). Whether heterogeneity in (18)F-fluorodeoxyglucose (FDG) positron emission tomographic (PET) images and epidermal growth factor receptor (EGFR) expression could provide additional information on clinical outcomes in advanced-stage OPSCC was investigated. Patients with stage III-IV OPSCC who completed primary therapy were eligible. Zone-size nonuniformity (ZSNU) extracted from pretreatment FDG PET scans was used as an index of image heterogeneity. EGFR and p16 expression were examined by immunohistochemistry. Disease-specific survival (DSS) and overall survival (OS) served as outcome measures. Kaplan-Meier estimates and Cox proportional hazards regression models were used for survival analysis. A bootstrap resampling technique was applied to investigate the stability of outcomes. Finally, a recursive partitioning analysis (RPA)-based model was constructed. A total of 113 patients were included, of which 28 were p16-positive. Multivariate analysis identified the Ang's profile, EGFR and ZSNU as independent predictors of both DSS and OS. Using RPA, the three risk factors were used to devise a prognostic scoring system that successfully predicted DSS in both p16-positive and -negative cases. The c-statistic of the prognostic index for DSS was 0.81, a value which was significantly superior to both AJCC stage (0.60) and the Ang's risk profile (0.68). In patients showing an Ang's high-risk profile (N = 77), the use of our scoring system clearly identified three distinct prognostic subgroups. It was concluded that a novel index may improve the prognostic stratification of patients with advanced-stage OPSCC. © 2015 UICC.

Novel prediction of anticancer drug chemosensitivity in cancer cell lines: evidence of moderation by microRNA expressions.

PubMed

Yang, Daniel S

2014-01-01

The objectives of this study are (1) to develop a novel "moderation" model of drug chemosensitivity and (2) to investigate if miRNA expression moderates the relationship between gene expression and drug chemosensitivity, specifically for HSP90 inhibitors applied to human cancer cell lines. A moderation model integrating the interaction between miRNA and gene expressions was developed to examine if miRNA expression affects the strength of the relationship between gene expression and chemosensitivity. Comprehensive datasets on miRNA expressions, gene expressions, and drug chemosensitivities were obtained from National Cancer Institute's NCI-60 cell lines including nine different cancer types. A workflow including steps of selecting genes, miRNAs, and compounds, correlating gene expression with chemosensitivity, and performing multivariate analysis was utilized to test the proposed model. The proposed moderation model identified 12 significantly-moderating miRNAs: miR-15b*, miR-16-2*, miR-9, miR-126*, miR-129*, miR-138, miR-519e*, miR-624*, miR-26b, miR-30e*, miR-32, and miR-196a, as well as two genes ERCC2 and SF3B1 which affect chemosensitivities of Tanespimycin and Alvespimycin - both HSP90 inhibitors. A bootstrap resampling of 2,500 times validates the significance of all 12 identified miRNAs. The results confirm that certain miRNA and gene expressions interact to produce an effect on drug response. The lack of correlation between miRNA and gene expression themselves suggests that miRNA transmits its effect through translation inhibition/control rather than mRNA degradation. The results suggest that miRNAs could serve not only as prognostic biomarkers for cancer treatment outcome but also as interventional agents to modulate desired chemosensitivity.

Contingencies of Self-Worth and Psychological Distress in Iranian Patients Seeking Cosmetic Surgery: Integrative Self-Knowledge as Mediator.

PubMed

Valikhani, Ahmad; Goodarzi, Mohammad Ali

2017-08-01

Although previous studies have shown that people applying for cosmetic surgery experience high-intensity psychological distress, important variables that function as protective factors have rarely been the subject of study in this population. Therefore, this study aims to investigate the role of low and high self-knowledge in experiencing psychological distress and contingencies of self-worth to appearance and approval from others and to identify the mediatory role of the integrative self-knowledge in patients seeking cosmetic surgery. Eighty-eight patients seeking cosmetic surgery were selected and completed the contingencies of self-worth and integrative self-knowledge scales, as well as the depression, anxiety and stress scale. Data were analyzed using multivariate analysis of variance (MANOVA) and path analysis using 5000 bootstrap resampling. The results of MANOVA showed that patients seeking cosmetic surgery with high self-knowledge had lower levels of depression, anxiety and stress compared to patients with low self-knowledge. They also gained lower scores in contingencies of self-worth to appearance and approval from others. The results of path analysis indicated that self-knowledge is a complete mediator in the relationship between contingencies of self-worth to appearance and approval from others and psychological distress. Based on the results of this study, it can be concluded that self-knowledge as a protective factor plays a major role in relation to the psychological distress experienced by the patients seeking cosmetic surgery. In fact, by increasing self-knowledge among this group of patients, their psychological distress can be decreased. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

A Bayesian Meta-Analysis of the Effect of Alcohol Use on HCV-Treatment Outcomes with a Comparison of Resampling Methods to Assess Uncertainty in Parameter Estimates.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cauthen, Katherine Regina; Lambert, Gregory Joseph; Finley, Patrick D.

There is mounting evidence that alcohol use is significantly linked to lower HCV treatment response rates in interferon-based therapies, though some of the evidence is conflicting. Furthermore, although health care providers recommend reducing or abstaining from alcohol use prior to treatment, many patients do not succeed in doing so. The goal of this meta-analysis was to systematically review and summarize the Englishlanguage literature up through January 30, 2015 regarding the relationship between alcohol use and HCV treatment outcomes, among patients who were not required to abstain from alcohol use in order to receive treatment. Seven pertinent articles studying 1,751 HCV-infectedmore » patients were identified. Log-ORs of HCV treatment response for heavy alcohol use and light alcohol use were calculated and compared. We employed a hierarchical Bayesian meta-analytic model to accommodate the small sample size. The summary estimate for the log-OR of HCV treatment response was -0.775 with a 95% credible interval of (-1.397, -0.236). The results of the Bayesian meta-analysis are slightly more conservative compared to those obtained from a boot-strapped, random effects model. We found evidence of heterogeneity (Q = 14.489, p = 0.025), accounting for 60.28% of the variation among log-ORs. Meta-regression to capture the sources of this heterogeneity did not identify any of the covariates investigated as significant. This meta-analysis confirms that heavy alcohol use is associated with decreased HCV treatment response compared to lighter levels of alcohol use. Further research is required to characterize the mechanism by which alcohol use affects HCV treatment response.« less

Multi-temporal clustering of continental floods and associated atmospheric circulations

NASA Astrophysics Data System (ADS)

Liu, Jianyu; Zhang, Yongqiang

2017-12-01

Investigating clustering of floods has important social, economic and ecological implications. This study examines the clustering of Australian floods at different temporal scales and its possible physical mechanisms. Flood series with different severities are obtained by peaks-over-threshold (POT) sampling in four flood thresholds. At intra-annual scale, Cox regression and monthly frequency methods are used to examine whether and when the flood clustering exists, respectively. At inter-annual scale, dispersion indices with four-time variation windows are applied to investigate the inter-annual flood clustering and its variation. Furthermore, the Kernel occurrence rate estimate and bootstrap resampling methods are used to identify flood-rich/flood-poor periods. Finally, seasonal variation of horizontal wind at 850 hPa and vertical wind velocity at 500 hPa are used to investigate the possible mechanisms causing the temporal flood clustering. Our results show that: (1) flood occurrences exhibit clustering at intra-annual scale, which are regulated by climate indices representing the impacts of the Pacific and Indian Oceans; (2) the flood-rich months occur from January to March over northern Australia, and from July to September over southwestern and southeastern Australia; (3) stronger inter-annual clustering takes place across southern Australia than northern Australia; and (4) Australian floods are characterised by regional flood-rich and flood-poor periods, with 1987-1992 identified as the flood-rich period across southern Australia, but the flood-poor period across northern Australia, and 2001-2006 being the flood-poor period across most regions of Australia. The intra-annual and inter-annual clustering and temporal variation of flood occurrences are in accordance with the variation of atmospheric circulation. These results provide relevant information for flood management under the influence of climate variability, and, therefore, are helpful for developing flood hazard mitigation schemes.

Multivariate Normal Tissue Complication Probability Modeling of Heart Valve Dysfunction in Hodgkin Lymphoma Survivors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cella, Laura, E-mail: laura.cella@cnr.it; Department of Advanced Biomedical Sciences, Federico II University School of Medicine, Naples; Liuzzi, Raffaele

Purpose: To establish a multivariate normal tissue complication probability (NTCP) model for radiation-induced asymptomatic heart valvular defects (RVD). Methods and Materials: Fifty-six patients treated with sequential chemoradiation therapy for Hodgkin lymphoma (HL) were retrospectively reviewed for RVD events. Clinical information along with whole heart, cardiac chambers, and lung dose distribution parameters was collected, and the correlations to RVD were analyzed by means of Spearman's rank correlation coefficient (Rs). For the selection of the model order and parameters for NTCP modeling, a multivariate logistic regression method using resampling techniques (bootstrapping) was applied. Model performance was evaluated using the area under themore » receiver operating characteristic curve (AUC). Results: When we analyzed the whole heart, a 3-variable NTCP model including the maximum dose, whole heart volume, and lung volume was shown to be the optimal predictive model for RVD (Rs = 0.573, P<.001, AUC = 0.83). When we analyzed the cardiac chambers individually, for the left atrium and for the left ventricle, an NTCP model based on 3 variables including the percentage volume exceeding 30 Gy (V30), cardiac chamber volume, and lung volume was selected as the most predictive model (Rs = 0.539, P<.001, AUC = 0.83; and Rs = 0.557, P<.001, AUC = 0.82, respectively). The NTCP values increase as heart maximum dose or cardiac chambers V30 increase. They also increase with larger volumes of the heart or cardiac chambers and decrease when lung volume is larger. Conclusions: We propose logistic NTCP models for RVD considering not only heart irradiation dose but also the combined effects of lung and heart volumes. Our study establishes the statistical evidence of the indirect effect of lung size on radio-induced heart toxicity.« less

Is questionnaire-based sitting time inaccurate and can it be improved? A cross-sectional investigation using accelerometer-based sitting time

PubMed Central

Gupta, Nidhi; Christiansen, Caroline Stordal; Hanisch, Christiana; Bay, Hans; Burr, Hermann; Holtermann, Andreas

2017-01-01

Objectives To investigate the differences between a questionnaire-based and accelerometer-based sitting time, and develop a model for improving the accuracy of questionnaire-based sitting time for predicting accelerometer-based sitting time. Methods 183 workers in a cross-sectional study reported sitting time per day using a single question during the measurement period, and wore 2 Actigraph GT3X+ accelerometers on the thigh and trunk for 1–4 working days to determine their actual sitting time per day using the validated Acti4 software. Least squares regression models were fitted with questionnaire-based siting time and other self-reported predictors to predict accelerometer-based sitting time. Results Questionnaire-based and accelerometer-based average sitting times were ≈272 and ≈476 min/day, respectively. A low Pearson correlation (r=0.32), high mean bias (204.1 min) and wide limits of agreement (549.8 to −139.7 min) between questionnaire-based and accelerometer-based sitting time were found. The prediction model based on questionnaire-based sitting explained 10% of the variance in accelerometer-based sitting time. Inclusion of 9 self-reported predictors in the model increased the explained variance to 41%, with 10% optimism using a resampling bootstrap validation. Based on a split validation analysis, the developed prediction model on ≈75% of the workers (n=132) reduced the mean and the SD of the difference between questionnaire-based and accelerometer-based sitting time by 64% and 42%, respectively, in the remaining 25% of the workers. Conclusions This study indicates that questionnaire-based sitting time has low validity and that a prediction model can be one solution to materially improve the precision of questionnaire-based sitting time. PMID:28093433

Anxiety Sensitivity and Smoking Behavior Among Trauma-Exposed Daily Smokers: The Explanatory Role of Smoking-Related Avoidance and Inflexibility.

PubMed

Bakhshaie, Jafar; Zvolensky, Michael J; Salazar, Adriana; Vujanovic, Anka A; Schmidt, Norman B

2016-01-01

Anxiety sensitivity (AS), defined as the extent to which individuals believe that anxiety-related sensations have harmful consequences, is associated with smoking processes and poorer clinical outcomes among trauma-exposed smokers. Yet the specific mechanisms underlying this association are unclear. Smoking-specific avoidance and inflexibility is a construct implicated in multiple manifestations of mood regulation that may underlie smoking behavior. The current study examined the explanatory role of smoking-specific avoidance and inflexibility in terms of the relation between AS and indices of smoking behavior among trauma-exposed smokers. The sample consisted of 217 treatment-seeking adult smokers (44% female; M age = 37.8; SD = 13.2; age range: 18-65 years), who were exposed to at least one lifetime Criterion A trauma event (Diagnostic and Statistical Manual of Mental Disorders [4th ed., text rev.; DSM-IV-TR] Criterion A for trauma exposure). Bootstrap analysis (5,000 re-samples) revealed that AS was indirectly related to the (a) number of cigarettes smoked per day, (b) number of years being a daily smoker, (c) number of failed quit attempts, and (d) heaviness of smoking index among trauma-exposed smokers through its relation with smoking-specific avoidance and inflexibility. These findings provide initial evidence suggesting that smoking-specific avoidance and inflexibility may be an important construct in better understanding AS-smoking relations among trauma-exposed smokers. Future work is needed to explore the extent to which smoking-specific avoidance and inflexibility account for relations between AS and other smoking processes (e.g., withdrawal, cessation outcome) in the context of trauma and smoking comorbidity. © The Author(s) 2015.

Academic Outcomes in Individuals With Childhood-Onset Epilepsy: Mediating Effects of Working Memory.

PubMed

Danguecan, Ashley N; Smith, Mary Lou

2017-08-01

Academic difficulties are common in children with epilepsy, although little is known about the effect of various seizure-related and cognitive variables. Given that persistent seizures may negatively impact academics, and that working memory is predictive of academic abilities, we examined the effects of recent seizures and working memory on word reading, spelling, and arithmetic in pediatric epilepsy. We hypothesized that persistent seizures would be associated with lower working memory ability, which would in turn result in poorer academic performance. Our sample consisted of 91 children with epilepsy being treated at the Hospital for Sick Children in Toronto, Canada, who underwent neuropsychological testing between 2002 and 2009 to help determine surgical candidacy. Four to 11 years later, follow-up testing was conducted on both surgical (n=61) and non-surgical (n=30) patients. Seizure status was defined by the presence or absence of seizures within the preceding 12 months. 5000 bias-corrected bootstrap resamples with replacement were used to calculate the 95% confidence intervals (CIs) for the indirect effect of seizure status on academics through working memory, controlling for baseline academic functioning. Persistent seizures were associated with reduced working memory, which was in turn associated with lower reading (B=-4.64, 95% CI [-10.21, -1.30]), spelling (B=-7.09, 95% CI [-13.97, -2.56], and arithmetic scores (B=-8.04, 95% CI [-13.66, -3.58] at follow-up. For children with intractable epilepsy, working memory deficits present a significant barrier to the development of academic skills. Working memory interventions may be a helpful adjunct to academic remediation in this population to facilitate academic progress. (JINS, 2017, 23, 594-604).

Neotrombicula inopinata (Acari: Trombiculidae) – a possible causative agent of trombiculiasis in Europe

PubMed Central

2014-01-01

Background For over a decade, the presence of trombiculid mites in some mountain areas of La Rioja (Northern Spain) and their association with seasonal human dermatitis have been recognized. This work aimed to establish the species identity of the agent causing trombiculiasis in the study area. Methods Trombiculid larvae (chigger mites) were collected from vegetation in the Sierra Cebollera Natural Park and in Sierra La Hez during an outbreak of human trombiculiasis in 2010. Three specimens collected from a bird were also examined. Identification was made using morphological and morphometric traits based on the most recent taxonomic sources. A comparison of those mites with specimens of the same species collected throughout Europe was performed by means of cluster analysis with multiscale bootstrap resampling and calculation of approximately unbiased p-values. Results All collected mites were identified as Neotrombicula inopinata (Oudemans, 1909). Therefore, this species is the most likely causative agent of trombiculiasis in Spain, not Neotrombicula autumnalis (Shaw, 1790), as it was generally assumed. No chigger was identified as N. autumnalis in the study area. Neotrombicula inopinata clearly differs from N. autumnalis in the presence of eight or more setae in the 1st and 2nd rows of dorsal idiosomal setae vs. six setae in N. autumnalis. Comparison of N. inopinata samples from different locations shows significant geographic variability in morphometric traits. Samples from Western and Eastern Europe and the Caucasus formed three separate clusters. Conclusion Since the taxonomical basis of many studies concerning N. autumnalis as a causative agent of trombiculiasis is insufficient, it is highly possible that N. inopinata may be hiding behind the common name of “harvest bug” in Europe, together with N. autumnalis. PMID:24589214

Neotrombicula inopinata (Acari: Trombiculidae) - a possible causative agent of trombiculiasis in Europe.

PubMed

Stekolnikov, Alexandr A; Santibáñez, Paula; Palomar, Ana M; Oteo, José A

2014-03-03

For over a decade, the presence of trombiculid mites in some mountain areas of La Rioja (Northern Spain) and their association with seasonal human dermatitis have been recognized. This work aimed to establish the species identity of the agent causing trombiculiasis in the study area. Trombiculid larvae (chigger mites) were collected from vegetation in the Sierra Cebollera Natural Park and in Sierra La Hez during an outbreak of human trombiculiasis in 2010. Three specimens collected from a bird were also examined. Identification was made using morphological and morphometric traits based on the most recent taxonomic sources. A comparison of those mites with specimens of the same species collected throughout Europe was performed by means of cluster analysis with multiscale bootstrap resampling and calculation of approximately unbiased p-values. All collected mites were identified as Neotrombicula inopinata (Oudemans, 1909). Therefore, this species is the most likely causative agent of trombiculiasis in Spain, not Neotrombicula autumnalis (Shaw, 1790), as it was generally assumed. No chigger was identified as N. autumnalis in the study area. Neotrombicula inopinata clearly differs from N. autumnalis in the presence of eight or more setae in the 1st and 2nd rows of dorsal idiosomal setae vs. six setae in N. autumnalis. Comparison of N. inopinata samples from different locations shows significant geographic variability in morphometric traits. Samples from Western and Eastern Europe and the Caucasus formed three separate clusters. Since the taxonomical basis of many studies concerning N. autumnalis as a causative agent of trombiculiasis is insufficient, it is highly possible that N. inopinata may be hiding behind the common name of "harvest bug" in Europe, together with N. autumnalis.

Benchmarking of a T-wave alternans detection method based on empirical mode decomposition.

PubMed

Blanco-Velasco, Manuel; Goya-Esteban, Rebeca; Cruz-Roldán, Fernando; García-Alberola, Arcadi; Rojo-Álvarez, José Luis

2017-07-01

T-wave alternans (TWA) is a fluctuation of the ST-T complex occurring on an every-other-beat basis of the surface electrocardiogram (ECG). It has been shown to be an informative risk stratifier for sudden cardiac death, though the lack of gold standard to benchmark detection methods has promoted the use of synthetic signals. This work proposes a novel signal model to study the performance of a TWA detection. Additionally, the methodological validation of a denoising technique based on empirical mode decomposition (EMD), which is used here along with the spectral method, is also tackled. The proposed test bed system is based on the following guidelines: (1) use of open source databases to enable experimental replication; (2) use of real ECG signals and physiological noise; (3) inclusion of randomized TWA episodes. Both sensitivity (Se) and specificity (Sp) are separately analyzed. Also a nonparametric hypothesis test, based on Bootstrap resampling, is used to determine whether the presence of the EMD block actually improves the performance. The results show an outstanding specificity when the EMD block is used, even in very noisy conditions (0.96 compared to 0.72 for SNR = 8 dB), being always superior than that of the conventional SM alone. Regarding the sensitivity, using the EMD method also outperforms in noisy conditions (0.57 compared to 0.46 for SNR=8 dB), while it decreases in noiseless conditions. The proposed test setting designed to analyze the performance guarantees that the actual physiological variability of the cardiac system is reproduced. The use of the EMD-based block in noisy environment enables the identification of most patients with fatal arrhythmias. Copyright © 2017 Elsevier B.V. All rights reserved.

An economic evaluation of planned immediate versus delayed birth for preterm prelabour rupture of membranes: findings from the PPROMT randomised controlled trial.

PubMed

Lain, S J; Roberts, C L; Bond, D M; Smith, J; Morris, J M

2017-03-01

This study is an economic evaluation of immediate birth compared with expectant management in women with preterm prelabour rupture of the membranes near term (PPROMT). A cost-effectiveness analysis alongside the PPROMT randomised controlled trial. Obstetric departments in 65 hospitals across 11 countries. Women with a singleton pregnancy with ruptured membranes between 34 +0 and 36 +6 weeks gestation. Women were randomly allocated to immediate birth or expectant management. Costs to the health system were identified and valued. National hospital costing data from both the UK and Australia were used. Average cost per recruit in each arm was calculated and 95% confidence intervals were estimated using bootstrap re-sampling. Averages costs during antenatal care, delivery and postnatal care, and by country were estimated. Total mean cost difference between immediate birth and expectant management arms of the trial. From 11 countries 923 women were randomised to immediate birth and 912 were randomised to expectant management. Total mean costs per recruit were £8852 for immediate birth and £8740 for expectant delivery resulting in a mean difference in costs of £112 (95% CI: -431 to 662). The expectant management arm had significantly higher antenatal costs, whereas the immediate birth arm had significantly higher delivery and neonatal costs. There was large variation between total mean costs by country. This economic evaluation found no evidence that expectant management was more or less costly than immediate birth. Outpatient management may offer opportunities for cost savings for those women with delayed delivery. For women with preterm prelabour rupture of the membranes, the relative benefits and harms of immediate and expectant management should inform counselling as costs are similar. © 2016 Royal College of Obstetricians and Gynaecologists.

The 3D-based scaling index algorithm to optimize structure analysis of trabecular bone in postmenopausal women with and without osteoporotic spine fractures

NASA Astrophysics Data System (ADS)

Muller, Dirk; Monetti, Roberto A.; Bohm, Holger F.; Bauer, Jan; Rummeny, Ernst J.; Link, Thomas M.; Rath, Christoph W.

2004-05-01

The scaling index method (SIM) is a recently proposed non-linear technique to extract texture measures for the quantitative characterisation of the trabecular bone structure in high resolution magnetic resonance imaging (HR-MRI). The three-dimensional tomographic images are interpreted as a point distribution in a state space where each point (voxel) is defined by its x, y, z coordinates and the grey value. The SIM estimates local scaling properties to describe the nonlinear morphological features in this four-dimensional point distribution. Thus, it can be used for differentiating between cluster-, rod-, sheet-like and unstructured (background) image components, which makes it suitable for quantifying the microstructure of human cancellous bone. The SIM was applied to high resolution magnetic resonance images of the distal radius in patients with and without osteoporotic spine fractures in order to quantify the deterioration of bone structure. Using the receiver operator characteristic (ROC) analysis the diagnostic performance of this texture measure in differentiating patients with and without fractures was compared with bone mineral density (BMD). The SIM demonstrated the best area under the curve (AUC) value for discriminating the two groups. The reliability of our new texture measure and the validity of our results were assessed by applying bootstrapping resampling methods. The results of this study show that trabecular structure measures derived from HR-MRI of the radius in a clinical setting using a recently proposed algorithm based on a local 3D scaling index method can significantly improve the diagnostic performance in differentiating postmenopausal women with and without osteoporotic spine fractures.

Cost-effectiveness analysis of low-molecular-weight heparin versus aspirin thromboprophylaxis in patients newly diagnosed with multiple myeloma.

PubMed

Chalayer, Emilie; Bourmaud, Aurélie; Tinquaut, Fabien; Chauvin, Franck; Tardy, Bernard

2016-09-01

The aim of this study was to assess the cost-effectiveness of low molecular weight heparin versus aspirin as primary thromboprophylaxis throughout chemotherapy for newly diagnosed multiple myeloma patients treated with protocols including thalidomide from the perspective of French health care providers. We used a modeling approach combining data from the only randomized trial evaluating the efficacy of the two treatments and secondary sources for costs, and utility values. We performed a decision-tree analysis and our base case was a hypothetical cohort of 10,000 patients. A bootstrap resampling technique was used. The incremental cost-effectiveness ratio was calculated using estimated quality-adjusted life years as the efficacy outcome. Incremental costs and effectiveness were estimated for each strategy and the incremental cost-effectiveness ratio was calculated. One-way sensitivity analyses were performed. The number of quality-adjusted life years was estimated to be 0.300 with aspirin and 0.299 with heparin. The estimated gain with aspirin was therefore approximately one day. Over 6months, the mean total cost was € 1518 (SD=601) per patient in the heparin arm and € 273 (SD=1019) in the aspirin arm. This resulted in an incremental cost of € 1245 per patient treated with heparin. The incremental cost-effectiveness ratio for the aspirin versus heparin strategy was calculated to be - 687,398 € (95% CI, -13,457,369 to -225,385). Aspirin rather than heparin thromboprophylaxis, during the first six months of chemotherapy for myeloma, is associated with significant cost savings per patient and also with an unexpected slight increase in quality of life. Copyright © 2016 Elsevier Ltd. All rights reserved.

The effects of velocities and lensing on moments of the Hubble diagram

NASA Astrophysics Data System (ADS)

Macaulay, E.; Davis, T. M.; Scovacricchi, D.; Bacon, D.; Collett, T.; Nichol, R. C.

2017-05-01

We consider the dispersion on the supernova distance-redshift relation due to peculiar velocities and gravitational lensing, and the sensitivity of these effects to the amplitude of the matter power spectrum. We use the Method-of-the-Moments (MeMo) lensing likelihood developed by Quartin et al., which accounts for the characteristic non-Gaussian distribution caused by lensing magnification with measurements of the first four central moments of the distribution of magnitudes. We build on the MeMo likelihood by including the effects of peculiar velocities directly into the model for the moments. In order to measure the moments from sparse numbers of supernovae, we take a new approach using Kernel density estimation to estimate the underlying probability density function of the magnitude residuals. We also describe a bootstrap re-sampling approach to estimate the data covariance matrix. We then apply the method to the joint light-curve analysis (JLA) supernova catalogue. When we impose only that the intrinsic dispersion in magnitudes is independent of redshift, we find σ _8=0.44^{+0.63}_{-0.44} at the one standard deviation level, although we note that in tests on simulations, this model tends to overestimate the magnitude of the intrinsic dispersion, and underestimate σ8. We note that the degeneracy between intrinsic dispersion and the effects of σ8 is more pronounced when lensing and velocity effects are considered simultaneously, due to a cancellation of redshift dependence when both effects are included. Keeping the model of the intrinsic dispersion fixed as a Gaussian distribution of width 0.14 mag, we find σ _8 = 1.07^{+0.50}_{-0.76}.

Assessing operating characteristics of CAD algorithms in the absence of a gold standard

DOE Office of Scientific and Technical Information (OSTI.GOV)

Roy Choudhury, Kingshuk; Paik, David S.; Yi, Chin A.

2010-04-15

Purpose: The authors examine potential bias when using a reference reader panel as ''gold standard'' for estimating operating characteristics of CAD algorithms for detecting lesions. As an alternative, the authors propose latent class analysis (LCA), which does not require an external gold standard to evaluate diagnostic accuracy. Methods: A binomial model for multiple reader detections using different diagnostic protocols was constructed, assuming conditional independence of readings given true lesion status. Operating characteristics of all protocols were estimated by maximum likelihood LCA. Reader panel and LCA based estimates were compared using data simulated from the binomial model for a range ofmore » operating characteristics. LCA was applied to 36 thin section thoracic computed tomography data sets from the Lung Image Database Consortium (LIDC): Free search markings of four radiologists were compared to markings from four different CAD assisted radiologists. For real data, bootstrap-based resampling methods, which accommodate dependence in reader detections, are proposed to test of hypotheses of differences between detection protocols. Results: In simulation studies, reader panel based sensitivity estimates had an average relative bias (ARB) of -23% to -27%, significantly higher (p-value <0.0001) than LCA (ARB -2% to -6%). Specificity was well estimated by both reader panel (ARB -0.6% to -0.5%) and LCA (ARB 1.4%-0.5%). Among 1145 lesion candidates LIDC considered, LCA estimated sensitivity of reference readers (55%) was significantly lower (p-value 0.006) than CAD assisted readers' (68%). Average false positives per patient for reference readers (0.95) was not significantly lower (p-value 0.28) than CAD assisted readers' (1.27). Conclusions: Whereas a gold standard based on a consensus of readers may substantially bias sensitivity estimates, LCA may be a significantly more accurate and consistent means for evaluating diagnostic accuracy.« less

The interplay between sleep and mood in predicting academic functioning, physical health and psychological health: a longitudinal study.

PubMed

Wong, Mark Lawrence; Lau, Esther Yuet Ying; Wan, Jacky Ho Yin; Cheung, Shu Fai; Hui, C Harry; Mok, Doris Shui Ying

2013-04-01

Existing studies on sleep and behavioral outcomes are mostly correlational. Longitudinal data is limited. The current longitudinal study assessed how sleep duration and sleep quality may be causally linked to daytime functions, including physical health (physical well-being and daytime sleepiness), psychological health (mood and self-esteem) and academic functioning (school grades and study effort). The mediation role of mood in the relationship between sleep quality, sleep duration and these daytime functions is also assessed. A sample of 930 Chinese students (aged 18-25) from Hong Kong/Macau completed self-reported questionnaires online across three academic semesters. Sleep behaviors are assessed by the sleep timing questionnaire (for sleep duration and weekday/weekend sleep discrepancy) and the Pittsburgh sleep quality index (sleep quality); physical health by the World Health Organization quality of life scale-brief version (physical well-being) and Epworth Sleepiness Scale (daytime sleepiness); psychological health by the depression anxiety stress scale (mood) and Rosenberg Self-esteem Scale (self-esteem) and academic functioning by grade-point-average and the college student expectation questionnaire (study effort). Structural equation modeling with a bootstrap resample of 5000 showed that after controlling for demographics and participants' daytime functions at baseline, academic functions, physical and psychological health were predicted by the duration and quality of sleep. While some sleep behaviors directly predicted daytime functions, others had an indirect effect on daytime functions through negative mood, such as anxiety. Sleep duration and quality have direct and indirect (via mood) effects on college students' academic function, physical and psychological health. Our findings underscore the importance of healthy sleep patterns for better adjustment in college years. Copyright © 2012 Elsevier Inc. All rights reserved.

Hospitalization costs associated with diarrhea among children during the period of rotavirus circulation in the Northwest region of Argentina.

PubMed

Giglio, Norberto D; Caruso, Martín; Castellano, Vanesa E; Choque, Liliana; Sandoval, Silvia; Micone, Paula; Gentile, Ángela

2017-12-01

To assess direct medical costs, outof-pocket expenses, and indirect costs in cases of hospitalizations for acute diarrhea among children <5 years of age at Hospital de Niños "Héctor Quintana" in the province of Jujuy during the period of rotavirus circulation in the Northwest region of Argentina. Cross-sectional study on diseaserelated costs. All children <5 years of age, hospitalized with the diagnosis of acute diarrhea and dehydration during the period of rotavirus circulation between May 1st and October 31st of 2013, were included. The assessment of direct medical costs was done by reviewing medical records whereas out-of-pocket expenses and indirect costs were determined using a survey. For the 95% confidence interval of the average cost per patient, a probabilistic bootstrapping analysis of 10 000 simulations by resampling was done. One hundred and five patients were enrolled. Their average age was 18 months (standard deviation: 12); 62 (59%) were boys. The average direct medical cost, out-of-pocket expense, and lost income per case was ARS 3413.6 (2856.35-3970.93) (USD 577.59), ARS 134.92 (85.95-213.57) (USD 22.82), and ARS 301 (223.28-380.02) (USD 50.93), respectively. The total cost per hospitalization event was ARS 3849.52 (3298-4402.25) (USD 651.35). The total cost per hospitalization event was within what is expected for Latin America. Costs are broken down into direct medical costs (significant share), compared to out-of-pocket expenses (3.5%) and indirect costs (7.8%). Sociedad Argentina de Pediatría

Microseismicity at the North Anatolian Fault in the Sea of Marmara offshore Istanbul, NW Turkey

USGS Publications Warehouse

Bulut, Fatih; Bohnhoff, Marco; Ellsworth, William L.; Aktar, Mustafa; Dresen, Georg

2009-01-01

The North Anatolian Fault Zone (NAFZ) below the Sea of Marmara forms a “seismic gap” where a major earthquake is expected to occur in the near future. This segment of the fault lies between the 1912 Ganos and 1999 İzmit ruptures and is the only NAFZ segment that has not ruptured since 1766. To monitor the microseismic activity at the main fault branch offshore of Istanbul below the Çınarcık Basin, a permanent seismic array (PIRES) was installed on the two outermost Prince Islands, Yassiada and Sivriada, at a few kilometers distance to the fault. In addition, a temporary network of ocean bottom seismometers was deployed throughout the Çınarcık Basin. Slowness vectors are determined combining waveform cross correlation and P wave polarization. We jointly invert azimuth and traveltime observations for hypocenter determination and apply a bootstrap resampling technique to quantify the location precision. We observe seismicity rates of 20 events per month for M < 2.5 along the basin. The spatial distribution of hypocenters suggests that the two major fault branches bounding the depocenter below the Çınarcık Basin merge to one single master fault below ∼17 km depth. On the basis of a cross-correlation technique we group closely spaced earthquakes and determine composite focal mechanisms implementing recordings of surrounding permanent land stations. Fault plane solutions have a predominant right-lateral strike-slip mechanism, indicating that normal faulting along this part of the NAFZ plays a minor role. Toward the west we observe increasing components of thrust faulting. This supports the model of NW trending, dextral strike-slip motion along the northern and main branch of the NAFZ below the eastern Sea of Marmara.

Metric variation and sexual dimorphism in the dentition of Ouranopithecus macedoniensis.

PubMed

Schrein, Caitlin M

2006-04-01

The fossil sample attributed to the late Miocene hominoid taxon Ouranopithecus macedoniensis is characterized by a high degree of dental metric variation. As a result, some researchers support a multiple-species taxonomy for this sample. Other researchers do not think that the sample variation is too great to be accommodated within one species. This study examines variation and sexual dimorphism in mandibular canine and postcanine dental metrics of an Ouranopithecus sample. Bootstrapping (resampling with replacement) of extant hominoid dental metric data is performed to test the hypothesis that the coefficients of variation (CV) and the indices of sexual dimorphism (ISD) of the fossil sample are not significantly different from those of modern great apes. Variation and sexual dimorphism in Ouranopithecus M(1) dimensions were statistically different from those of all extant ape samples; however, most of the dental metrics of Ouranopithecus were neither more variable nor more sexually dimorphic than those of Gorilla and Pongo. Similarly high levels of mandibular molar variation are known to characterize other fossil hominoid species. The Ouranopithecus specimens are morphologically homogeneous and it is probable that all but one specimen included in this study are from a single population. It is unlikely that the sample includes specimens of two sympatric large-bodied hominoid species. For these reasons, a single-species hypothesis is not rejected for the Ouranopithecus macedoniensis material. Correlations between mandibular first molar tooth size dimorphism and body size dimorphism indicate that O. macedoniensis and other extinct hominoids were more sexually size dimorphic than any living great apes, which suggests that social behaviors and life history profiles of these species may have been different from those of living species.

Impact of Antithrombotic Regimen on Mortality, Ischemic, and Bleeding Outcomes after Transcatheter Aortic Valve Replacement.

PubMed

Varshney, Anubodh; Watson, Ryan A; Noll, Andrew; Im, KyungAh; Rossi, Jeffrey; Shah, Pinak; Giugliano, Robert P

2018-05-19

Optimal antithrombotic therapy after transcatheter aortic valve replacement (TAVR) remains unclear. We evaluated the association between antithrombotic regimens and outcomes in TAVR patients. We retrospectively analyzed consecutive patients who underwent TAVR at a single academic center from April 2009 to March 2014. Antithrombotic regimens were classified as single or dual antiplatelet therapy (AP), single antiplatelet plus anticoagulant (SAC), or triple therapy (TT). The primary endpoint was a composite of death, myocardial infarction (MI), stroke, and major bleeding. Adjusted hazard ratios (HRs) were obtained with best subset variable selection methods using bootstrap resampling. Of 246 patients who underwent TAVR, 241 were eligible for analysis with 133, 88, and 20 patients in the AP, SAC, and TT groups, respectively. During a median 2.1-year follow-up, 53.5% had at least one endpoint-the most common was death (68%), followed by major bleeding (23%), stroke (6%), and MI (3%). At 2 years, the composite outcome occurred in 70% of TT, 42% of SAC, and 31% of AP patients. Compared to AP, adjusted HRs for the composite outcome were 2.88 [95% Confidence intervals (CI) (1.61-5.16); p = 0.0004] and 1.66 (95% CI [1.13-2.42]; p = 0.009) in the TT and SAC groups, respectively. Mortality rates at 2 years were 61% in the TT, 32% in the SAC, and 26% in the AP groups (p = 0.005). The risk of the composite outcome of death, MI, stroke, or major bleeding at 2-year follow-up was significantly higher in TAVR patients treated with TT or SAC versus AP, even after multivariate adjustment.

ASYMPTOTIC DISTRIBUTION OF ΔAUC, NRIs, AND IDI BASED ON THEORY OF U-STATISTICS

PubMed Central

Demler, Olga V.; Pencina, Michael J.; Cook, Nancy R.; D’Agostino, Ralph B.

2017-01-01

The change in AUC (ΔAUC), the IDI, and NRI are commonly used measures of risk prediction model performance. Some authors have reported good validity of associated methods of estimating their standard errors (SE) and construction of confidence intervals, whereas others have questioned their performance. To address these issues we unite the ΔAUC, IDI, and three versions of the NRI under the umbrella of the U-statistics family. We rigorously show that the asymptotic behavior of ΔAUC, NRIs, and IDI fits the asymptotic distribution theory developed for U-statistics. We prove that the ΔAUC, NRIs, and IDI are asymptotically normal, unless they compare nested models under the null hypothesis. In the latter case, asymptotic normality and existing SE estimates cannot be applied to ΔAUC, NRIs, or IDI. In the former case SE formulas proposed in the literature are equivalent to SE formulas obtained from U-statistics theory if we ignore adjustment for estimated parameters. We use Sukhatme-Randles-deWet condition to determine when adjustment for estimated parameters is necessary. We show that adjustment is not necessary for SEs of the ΔAUC and two versions of the NRI when added predictor variables are significant and normally distributed. The SEs of the IDI and three-category NRI should always be adjusted for estimated parameters. These results allow us to define when existing formulas for SE estimates can be used and when resampling methods such as the bootstrap should be used instead when comparing nested models. We also use the U-statistic theory to develop a new SE estimate of ΔAUC. PMID:28627112

Asymptotic distribution of ∆AUC, NRIs, and IDI based on theory of U-statistics.

PubMed

Demler, Olga V; Pencina, Michael J; Cook, Nancy R; D'Agostino, Ralph B

2017-09-20

The change in area under the curve (∆AUC), the integrated discrimination improvement (IDI), and net reclassification index (NRI) are commonly used measures of risk prediction model performance. Some authors have reported good validity of associated methods of estimating their standard errors (SE) and construction of confidence intervals, whereas others have questioned their performance. To address these issues, we unite the ∆AUC, IDI, and three versions of the NRI under the umbrella of the U-statistics family. We rigorously show that the asymptotic behavior of ∆AUC, NRIs, and IDI fits the asymptotic distribution theory developed for U-statistics. We prove that the ∆AUC, NRIs, and IDI are asymptotically normal, unless they compare nested models under the null hypothesis. In the latter case, asymptotic normality and existing SE estimates cannot be applied to ∆AUC, NRIs, or IDI. In the former case, SE formulas proposed in the literature are equivalent to SE formulas obtained from U-statistics theory if we ignore adjustment for estimated parameters. We use Sukhatme-Randles-deWet condition to determine when adjustment for estimated parameters is necessary. We show that adjustment is not necessary for SEs of the ∆AUC and two versions of the NRI when added predictor variables are significant and normally distributed. The SEs of the IDI and three-category NRI should always be adjusted for estimated parameters. These results allow us to define when existing formulas for SE estimates can be used and when resampling methods such as the bootstrap should be used instead when comparing nested models. We also use the U-statistic theory to develop a new SE estimate of ∆AUC. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Geriatric Fever Score: A New Decision Rule for Geriatric Care

PubMed Central

Vong, Si-Chon; Yang, Tzu-Meng; Chen, Kuo-Tai; Lin, Hung-Jung; Chen, Jiann-Hwa; Su, Shih-Bin; Guo, How-Ran; Hsu, Chien-Chin

2014-01-01

Background Evaluating geriatric patients with fever is time-consuming and challenging. We investigated independent mortality predictors of geriatric patients with fever and developed a prediction rule for emergency care, critical care, and geriatric care physicians to classify patients into mortality risk and disposition groups. Materials and Methods Consecutive geriatric patients (≥65 years old) visiting the emergency department (ED) of a university-affiliated medical center between June 1 and July 21, 2010, were enrolled when they met the criteria of fever: a tympanic temperature ≥37.2°C or a baseline temperature elevated ≥1.3°C. Thirty-day mortality was the primary endpoint. Internal validation with bootstrap re-sampling was done. Results Three hundred thirty geriatric patients were enrolled. We found three independent mortality predictors: Leukocytosis (WBC >12,000 cells/mm3), Severe coma (GCS ≤ 8), and Thrombocytopenia (platelets <150 103/mm3) (LST). After assigning weights to each predictor, we developed a Geriatric Fever Score that stratifies patients into two mortality-risk and disposition groups: low (4.0%) (95% CI: 2.3–6.9%): a general ward or treatment in the ED then discharge and high (30.3%) (95% CI: 17.4–47.3%): consider the intensive care unit. The area under the curve for the rule was 0.73. Conclusions We found that the Geriatric Fever Score is a simple and rapid rule for predicting 30-day mortality and classifying mortality risk and disposition in geriatric patients with fever, although external validation should be performed to confirm its usefulness in other clinical settings. It might help preserve medical resources for patients in greater need. PMID:25340811

A neural network based reputation bootstrapping approach for service selection

NASA Astrophysics Data System (ADS)

Wu, Quanwang; Zhu, Qingsheng; Li, Peng

2015-10-01

With the concept of service-oriented computing becoming widely accepted in enterprise application integration, more and more computing resources are encapsulated as services and published online. Reputation mechanism has been studied to establish trust on prior unknown services. One of the limitations of current reputation mechanisms is that they cannot assess the reputation of newly deployed services as no record of their previous behaviours exists. Most of the current bootstrapping approaches merely assign default reputation values to newcomers. However, by this kind of methods, either newcomers or existing services will be favoured. In this paper, we present a novel reputation bootstrapping approach, where correlations between features and performance of existing services are learned through an artificial neural network (ANN) and they are then generalised to establish a tentative reputation when evaluating new and unknown services. Reputations of services published previously by the same provider are also incorporated for reputation bootstrapping if available. The proposed reputation bootstrapping approach is seamlessly embedded into an existing reputation model and implemented in the extended service-oriented architecture. Empirical studies of the proposed approach are shown at last.

Systematic evaluation of sequential geostatistical resampling within MCMC for posterior sampling of near-surface geophysical inverse problems

NASA Astrophysics Data System (ADS)

Ruggeri, Paolo; Irving, James; Holliger, Klaus

2015-08-01

We critically examine the performance of sequential geostatistical resampling (SGR) as a model proposal mechanism for Bayesian Markov-chain-Monte-Carlo (MCMC) solutions to near-surface geophysical inverse problems. Focusing on a series of simple yet realistic synthetic crosshole georadar tomographic examples characterized by different numbers of data, levels of data error and degrees of model parameter spatial correlation, we investigate the efficiency of three different resampling strategies with regard to their ability to generate statistically independent realizations from the Bayesian posterior distribution. Quite importantly, our results show that, no matter what resampling strategy is employed, many of the examined test cases require an unreasonably high number of forward model runs to produce independent posterior samples, meaning that the SGR approach as currently implemented will not be computationally feasible for a wide range of problems. Although use of a novel gradual-deformation-based proposal method can help to alleviate these issues, it does not offer a full solution. Further, we find that the nature of the SGR is found to strongly influence MCMC performance; however no clear rule exists as to what set of inversion parameters and/or overall proposal acceptance rate will allow for the most efficient implementation. We conclude that although the SGR methodology is highly attractive as it allows for the consideration of complex geostatistical priors as well as conditioning to hard and soft data, further developments are necessary in the context of novel or hybrid MCMC approaches for it to be considered generally suitable for near-surface geophysical inversions.

Improving particle filters in rainfall-runoff models: application of the resample-move step and development of the ensemble Gaussian particle filter

NASA Astrophysics Data System (ADS)

Plaza Guingla, D. A.; Pauwels, V. R.; De Lannoy, G. J.; Matgen, P.; Giustarini, L.; De Keyser, R.

2012-12-01

The objective of this work is to analyze the improvement in the performance of the particle filter by including a resample-move step or by using a modified Gaussian particle filter. Specifically, the standard particle filter structure is altered by the inclusion of the Markov chain Monte Carlo move step. The second choice adopted in this study uses the moments of an ensemble Kalman filter analysis to define the importance density function within the Gaussian particle filter structure. Both variants of the standard particle filter are used in the assimilation of densely sampled discharge records into a conceptual rainfall-runoff model. In order to quantify the obtained improvement, discharge root mean square errors are compared for different particle filters, as well as for the ensemble Kalman filter. First, a synthetic experiment is carried out. The results indicate that the performance of the standard particle filter can be improved by the inclusion of the resample-move step, but its effectiveness is limited to situations with limited particle impoverishment. The results also show that the modified Gaussian particle filter outperforms the rest of the filters. Second, a real experiment is carried out in order to validate the findings from the synthetic experiment. The addition of the resample-move step does not show a considerable improvement due to performance limitations in the standard particle filter with real data. On the other hand, when an optimal importance density function is used in the Gaussian particle filter, the results show a considerably improved performance of the particle filter.

Optimizing spectral resolutions for the classification of C3 and C4 grass species, using wavelengths of known absorption features

NASA Astrophysics Data System (ADS)

Adjorlolo, Clement; Cho, Moses A.; Mutanga, Onisimo; Ismail, Riyad

2012-01-01

Hyperspectral remote-sensing approaches are suitable for detection of the differences in 3-carbon (C3) and four carbon (C4) grass species phenology and composition. However, the application of hyperspectral sensors to vegetation has been hampered by high-dimensionality, spectral redundancy, and multicollinearity problems. In this experiment, resampling of hyperspectral data to wider wavelength intervals, around a few band-centers, sensitive to the biophysical and biochemical properties of C3 or C4 grass species is proposed. The approach accounts for an inherent property of vegetation spectral response: the asymmetrical nature of the inter-band correlations between a waveband and its shorter- and longer-wavelength neighbors. It involves constructing a curve of weighting threshold of correlation (Pearson's r) between a chosen band-center and its neighbors, as a function of wavelength. In addition, data were resampled to some multispectral sensors-ASTER, GeoEye-1, IKONOS, QuickBird, RapidEye, SPOT 5, and WorldView-2 satellites-for comparative purposes, with the proposed method. The resulting datasets were analyzed, using the random forest algorithm. The proposed resampling method achieved improved classification accuracy (κ=0.82), compared to the resampled multispectral datasets (κ=0.78, 0.65, 0.62, 0.59, 0.65, 0.62, 0.76, respectively). Overall, results from this study demonstrated that spectral resolutions for C3 and C4 grasses can be optimized and controlled for high dimensionality and multicollinearity problems, yet yielding high classification accuracies. The findings also provide a sound basis for programming wavebands for future sensors.

On-line crack prognosis in attachment lug using Lamb wave-deterministic resampling particle filter-based method

NASA Astrophysics Data System (ADS)

Yuan, Shenfang; Chen, Jian; Yang, Weibo; Qiu, Lei

2017-08-01

Fatigue crack growth prognosis is important for prolonging service time, improving safety, and reducing maintenance cost in many safety-critical systems, such as in aircraft, wind turbines, bridges, and nuclear plants. Combining fatigue crack growth models with the particle filter (PF) method has proved promising to deal with the uncertainties during fatigue crack growth and reach a more accurate prognosis. However, research on prognosis methods integrating on-line crack monitoring with the PF method is still lacking, as well as experimental verifications. Besides, the PF methods adopted so far are almost all sequential importance resampling-based PFs, which usually encounter sample impoverishment problems, and hence performs poorly. To solve these problems, in this paper, the piezoelectric transducers (PZTs)-based active Lamb wave method is adopted for on-line crack monitoring. The deterministic resampling PF (DRPF) is proposed to be used in fatigue crack growth prognosis, which can overcome the sample impoverishment problem. The proposed method is verified through fatigue tests of attachment lugs, which are a kind of important joint component in aerospace systems.

Sample size and the detection of a hump-shaped relationship between biomass and species richness in Mediterranean wetlands

USGS Publications Warehouse

Espinar, J.L.

2006-01-01

Questions: What is the observed relationship between biomass and species richness across both spatial and temporal scales in communities of submerged annual macrophytes? Does the number of plots sampled affect detection of hump-shaped pattern? Location: Don??ana National Park, southwestern Spain. Methods: A total of 102 plots were sampled during four hydrological cycles. In each hydrological cycle, the plots were distributed randomly along an environmental flooding gradient in three contrasted microhabitats located in the transition zone just below the upper marsh. In each plot (0.5 m x 0.5 m), plant density and above- and below-ground biomass of submerged vegetation were measured. The hump-shaped model was tested by using a generalized linear model (GLM). A bootstrap procedure was used to test the effect of the number of plots on the ability to detect hump-shaped patterns. Result: The area exhibited low species density with a range of 1 - 9 species and low values of biomass with a range of 0.2 - 87.6 g-DW / 0.25 m2. When data from all years and all microhabitats were combined, the relationships between biomass and species richness showed a hump-shaped pattern. The number of plots was large enough to allow detection of the hump-shaped pattern across microhabitats but it was too small to confirm the hump-shaped pattern within each individual microhabitat. Conclusion: This study provides evidence of hump-shaped patterns across microhabitats when GLM analysis is used. In communities of submerged annual macrophytes in Mediterranean wetlands, the highest species density occurs in intermediate values of biomass. The bootstrap procedure indicates that the number of plots affects the detection of hump-shaped patterns. ?? IAVS; Opulus Press.

A CNN based Hybrid approach towards automatic image registration

NASA Astrophysics Data System (ADS)

Arun, Pattathal V.; Katiyar, Sunil K.

2013-06-01

Image registration is a key component of various image processing operations which involve the analysis of different image data sets. Automatic image registration domains have witnessed the application of many intelligent methodologies over the past decade; however inability to properly model object shape as well as contextual information had limited the attainable accuracy. In this paper, we propose a framework for accurate feature shape modeling and adaptive resampling using advanced techniques such as Vector Machines, Cellular Neural Network (CNN), SIFT, coreset, and Cellular Automata. CNN has found to be effective in improving feature matching as well as resampling stages of registration and complexity of the approach has been considerably reduced using corset optimization The salient features of this work are cellular neural network approach based SIFT feature point optimisation, adaptive resampling and intelligent object modelling. Developed methodology has been compared with contemporary methods using different statistical measures. Investigations over various satellite images revealed that considerable success was achieved with the approach. System has dynamically used spectral and spatial information for representing contextual knowledge using CNN-prolog approach. Methodology also illustrated to be effective in providing intelligent interpretation and adaptive resampling. Rejestracja obrazu jest kluczowym składnikiem różnych operacji jego przetwarzania. W ostatnich latach do automatycznej rejestracji obrazu wykorzystuje się metody sztucznej inteligencji, których największą wadą, obniżającą dokładność uzyskanych wyników jest brak możliwości dobrego wymodelowania kształtu i informacji kontekstowych. W niniejszej pracy zaproponowano zasady dokładnego modelowania kształtu oraz adaptacyjnego resamplingu z wykorzystaniem zaawansowanych technik, takich jak Vector Machines (VM), komórkowa sieć neuronowa (CNN), przesiewanie (SIFT), Coreset i automaty komórkowe. Stwierdzono, że za pomocą CNN można skutecznie poprawiać dopasowanie obiektów obrazowych oraz resampling kolejnych kroków rejestracji, zaś zastosowanie optymalizacji metodą Coreset znacznie redukuje złożoność podejścia. Zasadniczym przedmiotem pracy są: optymalizacja punktów metodą SIFT oparta na podejściu CNN, adaptacyjny resampling oraz inteligentne modelowanie obiektów. Opracowana metoda została porównana ze współcześnie stosowanymi metodami wykorzystującymi różne miary statystyczne. Badania nad różnymi obrazami satelitarnymi wykazały, że stosując opracowane podejście osiągnięto bardzo dobre wyniki. System stosując podejście CNN-prolog dynamicznie wykorzystuje informacje spektralne i przestrzenne dla reprezentacji wiedzy kontekstowej. Metoda okazała się również skuteczna w dostarczaniu inteligentnych interpretacji i w adaptacyjnym resamplingu.

A sup-score test for the cure fraction in mixture models for long-term survivors.

PubMed

Hsu, Wei-Wen; Todem, David; Kim, KyungMann

2016-12-01

The evaluation of cure fractions in oncology research under the well known cure rate model has attracted considerable attention in the literature, but most of the existing testing procedures have relied on restrictive assumptions. A common assumption has been to restrict the cure fraction to a constant under alternatives to homogeneity, thereby neglecting any information from covariates. This article extends the literature by developing a score-based statistic that incorporates covariate information to detect cure fractions, with the existing testing procedure serving as a special case. A complication of this extension, however, is that the implied hypotheses are not typical and standard regularity conditions to conduct the test may not even hold. Using empirical processes arguments, we construct a sup-score test statistic for cure fractions and establish its limiting null distribution as a functional of mixtures of chi-square processes. In practice, we suggest a simple resampling procedure to approximate this limiting distribution. Our simulation results show that the proposed test can greatly improve efficiency over tests that neglect the heterogeneity of the cure fraction under the alternative. The practical utility of the methodology is illustrated using ovarian cancer survival data with long-term follow-up from the surveillance, epidemiology, and end results registry. © 2016, The International Biometric Society.

Bootstrap investigation of the stability of a Cox regression model.

PubMed

Altman, D G; Andersen, P K

1989-07-01

We describe a bootstrap investigation of the stability of a Cox proportional hazards regression model resulting from the analysis of a clinical trial of azathioprine versus placebo in patients with primary biliary cirrhosis. We have considered stability to refer both to the choice of variables included in the model and, more importantly, to the predictive ability of the model. In stepwise Cox regression analyses of 100 bootstrap samples using 17 candidate variables, the most frequently selected variables were those selected in the original analysis, and no other important variable was identified. Thus there was no reason to doubt the model obtained in the original analysis. For each patient in the trial, bootstrap confidence intervals were constructed for the estimated probability of surviving two years. It is shown graphically that these intervals are markedly wider than those obtained from the original model.

Bootstrap and Counter-Bootstrap approaches for formation of the cortege of Informative indicators by Results of Measurements

NASA Astrophysics Data System (ADS)

Artemenko, M. V.; Chernetskaia, I. E.; Kalugina, N. M.; Shchekina, E. N.

2018-04-01

This article describes the solution of the actual problem of the productive formation of a cortege of informative measured features of the object of observation and / or control using author's algorithms for the use of bootstraps and counter-bootstraps technologies for processing the results of measurements of various states of the object on the basis of different volumes of the training sample. The work that is presented in this paper considers aggregation by specific indicators of informative capacity by linear, majority, logical and “greedy” methods, applied both individually and integrally. The results of the computational experiment are discussed, and in conclusion is drawn that the application of the proposed methods contributes to an increase in the efficiency of classification of the states of the object from the results of measurements.

Robust estimation of the proportion of treatment effect explained by surrogate marker information.

PubMed

Parast, Layla; McDermott, Mary M; Tian, Lu

2016-05-10

In randomized treatment studies where the primary outcome requires long follow-up of patients and/or expensive or invasive obtainment procedures, the availability of a surrogate marker that could be used to estimate the treatment effect and could potentially be observed earlier than the primary outcome would allow researchers to make conclusions regarding the treatment effect with less required follow-up time and resources. The Prentice criterion for a valid surrogate marker requires that a test for treatment effect on the surrogate marker also be a valid test for treatment effect on the primary outcome of interest. Based on this criterion, methods have been developed to define and estimate the proportion of treatment effect on the primary outcome that is explained by the treatment effect on the surrogate marker. These methods aim to identify useful statistical surrogates that capture a large proportion of the treatment effect. However, current methods to estimate this proportion usually require restrictive model assumptions that may not hold in practice and thus may lead to biased estimates of this quantity. In this paper, we propose a nonparametric procedure to estimate the proportion of treatment effect on the primary outcome that is explained by the treatment effect on a potential surrogate marker and extend this procedure to a setting with multiple surrogate markers. We compare our approach with previously proposed model-based approaches and propose a variance estimation procedure based on a perturbation-resampling method. Simulation studies demonstrate that the procedure performs well in finite samples and outperforms model-based procedures when the specified models are not correct. We illustrate our proposed procedure using a data set from a randomized study investigating a group-mediated cognitive behavioral intervention for peripheral artery disease participants. Copyright © 2015 John Wiley & Sons, Ltd.

Robust multivariate nonparametric tests for detection of two-sample location shift in clinical trials

PubMed Central

Jiang, Xuejun; Guo, Xu; Zhang, Ning; Wang, Bo

2018-01-01

This article presents and investigates performance of a series of robust multivariate nonparametric tests for detection of location shift between two multivariate samples in randomized controlled trials. The tests are built upon robust estimators of distribution locations (medians, Hodges-Lehmann estimators, and an extended U statistic) with both unscaled and scaled versions. The nonparametric tests are robust to outliers and do not assume that the two samples are drawn from multivariate normal distributions. Bootstrap and permutation approaches are introduced for determining the p-values of the proposed test statistics. Simulation studies are conducted and numerical results are reported to examine performance of the proposed statistical tests. The numerical results demonstrate that the robust multivariate nonparametric tests constructed from the Hodges-Lehmann estimators are more efficient than those based on medians and the extended U statistic. The permutation approach can provide a more stringent control of Type I error and is generally more powerful than the bootstrap procedure. The proposed robust nonparametric tests are applied to detect multivariate distributional difference between the intervention and control groups in the Thai Healthy Choices study and examine the intervention effect of a four-session motivational interviewing-based intervention developed in the study to reduce risk behaviors among youth living with HIV. PMID:29672555

A frequentist approach to computer model calibration

DOE PAGES

Wong, Raymond K. W.; Storlie, Curtis Byron; Lee, Thomas C. M.

2016-05-05

The paper considers the computer model calibration problem and provides a general frequentist solution. Under the framework proposed, the data model is semiparametric with a non-parametric discrepancy function which accounts for any discrepancy between physical reality and the computer model. In an attempt to solve a fundamentally important (but often ignored) identifiability issue between the computer model parameters and the discrepancy function, the paper proposes a new and identifiable parameterization of the calibration problem. It also develops a two-step procedure for estimating all the relevant quantities under the new parameterization. This estimation procedure is shown to enjoy excellent rates ofmore » convergence and can be straightforwardly implemented with existing software. For uncertainty quantification, bootstrapping is adopted to construct confidence regions for the quantities of interest. As a result, the practical performance of the methodology is illustrated through simulation examples and an application to a computational fluid dynamics model.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wong, Raymond K. W.; Storlie, Curtis Byron; Lee, Thomas C. M.

The paper considers the computer model calibration problem and provides a general frequentist solution. Under the framework proposed, the data model is semiparametric with a non-parametric discrepancy function which accounts for any discrepancy between physical reality and the computer model. In an attempt to solve a fundamentally important (but often ignored) identifiability issue between the computer model parameters and the discrepancy function, the paper proposes a new and identifiable parameterization of the calibration problem. It also develops a two-step procedure for estimating all the relevant quantities under the new parameterization. This estimation procedure is shown to enjoy excellent rates ofmore » convergence and can be straightforwardly implemented with existing software. For uncertainty quantification, bootstrapping is adopted to construct confidence regions for the quantities of interest. As a result, the practical performance of the methodology is illustrated through simulation examples and an application to a computational fluid dynamics model.« less

Assessing sequential data assimilation techniques for integrating GRACE data into a hydrological model

NASA Astrophysics Data System (ADS)

Khaki, M.; Hoteit, I.; Kuhn, M.; Awange, J.; Forootan, E.; van Dijk, A. I. J. M.; Schumacher, M.; Pattiaratchi, C.

2017-09-01

The time-variable terrestrial water storage (TWS) products from the Gravity Recovery And Climate Experiment (GRACE) have been increasingly used in recent years to improve the simulation of hydrological models by applying data assimilation techniques. In this study, for the first time, we assess the performance of the most popular data assimilation sequential techniques for integrating GRACE TWS into the World-Wide Water Resources Assessment (W3RA) model. We implement and test stochastic and deterministic ensemble-based Kalman filters (EnKF), as well as Particle filters (PF) using two different resampling approaches of Multinomial Resampling and Systematic Resampling. These choices provide various opportunities for weighting observations and model simulations during the assimilation and also accounting for error distributions. Particularly, the deterministic EnKF is tested to avoid perturbing observations before assimilation (that is the case in an ordinary EnKF). Gaussian-based random updates in the EnKF approaches likely do not fully represent the statistical properties of the model simulations and TWS observations. Therefore, the fully non-Gaussian PF is also applied to estimate more realistic updates. Monthly GRACE TWS are assimilated into W3RA covering the entire Australia. To evaluate the filters performances and analyze their impact on model simulations, their estimates are validated by independent in-situ measurements. Our results indicate that all implemented filters improve the estimation of water storage simulations of W3RA. The best results are obtained using two versions of deterministic EnKF, i.e. the Square Root Analysis (SQRA) scheme and the Ensemble Square Root Filter (EnSRF), respectively, improving the model groundwater estimations errors by 34% and 31% compared to a model run without assimilation. Applying the PF along with Systematic Resampling successfully decreases the model estimation error by 23%.

Two-year pilot study of newborn screening for congenital adrenal hyperplasia in New South Wales compared with nationwide case surveillance in Australia.

PubMed

Gleeson, Helena K; Wiley, Veronica; Wilcken, Bridget; Elliott, Elizabeth; Cowell, Christopher; Thonsett, Michael; Byrne, Geoffrey; Ambler, Geoffrey

2008-10-01

To assess the benefits and practicalities of setting up a newborn screening (NBS) program in Australia for congenital adrenal hyperplasia (CAH) through a 2 year pilot screening in ACT/NSW and comparing with case surveillance in other states. The pilot newborn screening occurred between 1/10/95 and 30/9/97 in NSW/ACT. Concurrently, case reporting for all new CAH cases occurred through the Australian Paediatric Surveillance Unit (APSU) across Australia. Details of clinical presentation, re-sampling and laboratory performance were assessed. 185,854 newborn infants were screened for CAH in NSW/ACT. Concurrently, 30 cases of CAH were reported to APSU, twelve of which were from NSW/ACT. CAH incidence was 1 in 15 488 (screened population) vs 1 in 18,034 births (unscreened) (difference not significant). Median age of initial notification was day 8 with confirmed diagnosis at 13(5-23) days in the screened population vs 16(7-37) days in the unscreened population (not significant). Of the 5 clinically unsuspected males in the screened population, one had mild salt-wasting by the time of notification, compared with salt-wasting crisis in all 6 males from the unscreened population. 96% of results were reported by day 10. Resampling was requested in 637 (0.4%) and median re-sampling delay was 11(0-28) days with higher resample rates in males (p < 0.0001). The within-laboratory cost per case of clinically unsuspected cases was A$42 717. There seems good justification for NBS for CAH based on clear prevention of salt-wasting crises and their potential long-term consequences. Also, prospects exist for enhancing screening performance.

Resampling algorithm for the Spatial Infrared Imaging Telescope (SPIRIT III) Fourier transform spectrometer

NASA Astrophysics Data System (ADS)

Sargent, Steven D.; Greenman, Mark E.; Hansen, Scott M.

1998-11-01

The Spatial Infrared Imaging Telescope (SPIRIT III) is the primary sensor aboard the Midcourse Space Experiment (MSX), which was launched 24 April 1996. SPIRIT III included a Fourier transform spectrometer that collected terrestrial and celestial background phenomenology data for the Ballistic Missile Defense Organization (BMDO). This spectrometer used a helium-neon reference laser to measure the optical path difference (OPD) in the spectrometer and to command the analog-to-digital conversion of the infrared detector signals, thereby ensuring the data were sampled at precise increments of OPD. Spectrometer data must be sampled at accurate increments of OPD to optimize the spectral resolution and spectral position of the transformed spectra. Unfortunately, a failure in the power supply preregulator at the MSX spacecraft/SPIRIT III interface early in the mission forced the spectrometer to be operated without the reference laser until a failure investigation was completed. During this time data were collected in a backup mode that used an electronic clock to sample the data. These data were sampled evenly in time, and because the scan velocity varied, at nonuniform increments of OPD. The scan velocity profile depended on scan direction and scan length, and varied over time, greatly degrading the spectral resolution and spectral and radiometric accuracy of the measurements. The Convert software used to process the SPIRIT III data was modified to resample the clock-sampled data at even increments of OPD, using scan velocity profiles determined from ground and on-orbit data, greatly improving the quality of the clock-sampled data. This paper presents the resampling algorithm, the characterization of the scan velocity profiles, and the results of applying the resampling algorithm to on-orbit data.

The Novaya Zemlya Event of 31 December 1992 and Seismic Identification Issues: Annual Seismic Research Symposium (15th) Held in Vail, Colorado on 8-10 September 1993

DTIC Science & Technology

1993-09-10

1993). A bootstrap generalizedlikelihood ratio test in discriminant analysis, Proc. 15th Annual Seismic Research Symposium, in press. I Hedlin, M., J... ratio indicate that the event does not belong to the first class. The bootstrap technique is used here as well to set the critical value of the test ...Methodist University. Baek, J., H. L. Gray, W. A. Woodward and M.D. Fisk (1993). A Bootstrap Generalized Likelihood Ratio Test in Discriminant