The Importance of Practice in the Development of Statistics.

DTIC Science & Technology

1983-01-01

RESOLUTION TEST CHART NATIONAL BUREAU OIF STANDARDS 1963 -A NRC Technical Summary Report #2471 C THE IMORTANCE OF PRACTICE IN to THE DEVELOPMENT OF STATISTICS...component analysis, bioassay, limits for a ratio, quality control, sampling inspection, non-parametric tests , transformation theory, ARIMA time series...models, sequential tests , cumulative sum charts, data analysis plotting techniques, and a resolution of the Bayes - frequentist controversy. It appears

When the Single Matters more than the Group (II): Addressing the Problem of High False Positive Rates in Single Case Voxel Based Morphometry Using Non-parametric Statistics.

PubMed

Scarpazza, Cristina; Nichols, Thomas E; Seramondi, Donato; Maumet, Camille; Sartori, Giuseppe; Mechelli, Andrea

2016-01-01

In recent years, an increasing number of studies have used Voxel Based Morphometry (VBM) to compare a single patient with a psychiatric or neurological condition of interest against a group of healthy controls. However, the validity of this approach critically relies on the assumption that the single patient is drawn from a hypothetical population with a normal distribution and variance equal to that of the control group. In a previous investigation, we demonstrated that family-wise false positive error rate (i.e., the proportion of statistical comparisons yielding at least one false positive) in single case VBM are much higher than expected (Scarpazza et al., 2013). Here, we examine whether the use of non-parametric statistics, which does not rely on the assumptions of normal distribution and equal variance, would enable the investigation of single subjects with good control of false positive risk. We empirically estimated false positive rates (FPRs) in single case non-parametric VBM, by performing 400 statistical comparisons between a single disease-free individual and a group of 100 disease-free controls. The impact of smoothing (4, 8, and 12 mm) and type of pre-processing (Modulated, Unmodulated) was also examined, as these factors have been found to influence FPRs in previous investigations using parametric statistics. The 400 statistical comparisons were repeated using two independent, freely available data sets in order to maximize the generalizability of the results. We found that the family-wise error rate was 5% for increases and 3.6% for decreases in one data set; and 5.6% for increases and 6.3% for decreases in the other data set (5% nominal). Further, these results were not dependent on the level of smoothing and modulation. Therefore, the present study provides empirical evidence that single case VBM studies with non-parametric statistics are not susceptible to high false positive rates. The critical implication of this finding is that VBM can be used to characterize neuroanatomical alterations in individual subjects as long as non-parametric statistics are employed.

Inhibition of Orthopaedic Implant Infections by Immunomodulatory Effects of Host Defense Peptides

DTIC Science & Technology

2014-12-01

significance was determined by t- tests or by one-way analysis of variance (ANOVA) followed by Bonferroni post hoc tests in experiments with multiple...groups. Non- parametric Mann-Whitney tests , Kruskal-Wallis ANOVA followed by Newman-Kuels post hoc tests , or van Elteren’s two-way tests were applied to...in D, and black symbols in A), statistical analysis was by one-way ANOVA followed by Bonferroni versus control, post hoc tests . Otherwise, statistical

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).

The chi-square test of independence.

PubMed

McHugh, Mary L

2013-01-01

The Chi-square statistic is a non-parametric (distribution free) tool designed to analyze group differences when the dependent variable is measured at a nominal level. Like all non-parametric statistics, the Chi-square is robust with respect to the distribution of the data. Specifically, it does not require equality of variances among the study groups or homoscedasticity in the data. It permits evaluation of both dichotomous independent variables, and of multiple group studies. Unlike many other non-parametric and some parametric statistics, the calculations needed to compute the Chi-square provide considerable information about how each of the groups performed in the study. This richness of detail allows the researcher to understand the results and thus to derive more detailed information from this statistic than from many others. The Chi-square is a significance statistic, and should be followed with a strength statistic. The Cramer's V is the most common strength test used to test the data when a significant Chi-square result has been obtained. Advantages of the Chi-square include its robustness with respect to distribution of the data, its ease of computation, the detailed information that can be derived from the test, its use in studies for which parametric assumptions cannot be met, and its flexibility in handling data from both two group and multiple group studies. Limitations include its sample size requirements, difficulty of interpretation when there are large numbers of categories (20 or more) in the independent or dependent variables, and tendency of the Cramer's V to produce relative low correlation measures, even for highly significant results.

Detecting trend on ecological river status - how to deal with short incomplete bioindicator time series? Methodological and operational issues

NASA Astrophysics Data System (ADS)

Cernesson, Flavie; Tournoud, Marie-George; Lalande, Nathalie

2018-06-01

Among the various parameters monitored in river monitoring networks, bioindicators provide very informative data. Analysing time variations in bioindicator data is tricky for water managers because the data sets are often short, irregular, and non-normally distributed. It is then a challenging methodological issue for scientists, as it is in Saône basin (30 000 km2, France) where, between 1998 and 2010, among 812 IBGN (French macroinvertebrate bioindicator) monitoring stations, only 71 time series have got more than 10 data values and were studied here. Combining various analytical tools (three parametric and non-parametric statistical tests plus a graphical analysis), 45 IBGN time series were classified as stationary and 26 as non-stationary (only one of which showing a degradation). Series from sampling stations located within the same hydroecoregion showed similar trends, while river size classes seemed to be non-significant to explain temporal trends. So, from a methodological point of view, combining statistical tests and graphical analysis is a relevant option when striving to improve trend detection. Moreover, it was possible to propose a way to summarise series in order to analyse links between ecological river quality indicators and land use stressors.

A Nonparametric Geostatistical Method For Estimating Species Importance

Treesearch

Andrew J. Lister; Rachel Riemann; Michael Hoppus

2001-01-01

Parametric statistical methods are not always appropriate for conducting spatial analyses of forest inventory data. Parametric geostatistical methods such as variography and kriging are essentially averaging procedures, and thus can be affected by extreme values. Furthermore, non normal distributions violate the assumptions of analyses in which test statistics are...

Assessing statistical differences between parameters estimates in Partial Least Squares path modeling.

PubMed

Rodríguez-Entrena, Macario; Schuberth, Florian; Gelhard, Carsten

2018-01-01

Structural equation modeling using partial least squares (PLS-SEM) has become a main-stream modeling approach in various disciplines. Nevertheless, prior literature still lacks a practical guidance on how to properly test for differences between parameter estimates. Whereas existing techniques such as parametric and non-parametric approaches in PLS multi-group analysis solely allow to assess differences between parameters that are estimated for different subpopulations, the study at hand introduces a technique that allows to also assess whether two parameter estimates that are derived from the same sample are statistically different. To illustrate this advancement to PLS-SEM, we particularly refer to a reduced version of the well-established technology acceptance model.

Practical statistics in pain research.

PubMed

Kim, Tae Kyun

2017-10-01

Pain is subjective, while statistics related to pain research are objective. This review was written to help researchers involved in pain research make statistical decisions. The main issues are related with the level of scales that are often used in pain research, the choice of statistical methods between parametric or nonparametric statistics, and problems which arise from repeated measurements. In the field of pain research, parametric statistics used to be applied in an erroneous way. This is closely related with the scales of data and repeated measurements. The level of scales includes nominal, ordinal, interval, and ratio scales. The level of scales affects the choice of statistics between parametric or non-parametric methods. In the field of pain research, the most frequently used pain assessment scale is the ordinal scale, which would include the visual analogue scale (VAS). There used to be another view, however, which considered the VAS to be an interval or ratio scale, so that the usage of parametric statistics would be accepted practically in some cases. Repeated measurements of the same subjects always complicates statistics. It means that measurements inevitably have correlations between each other, and would preclude the application of one-way ANOVA in which independence between the measurements is necessary. Repeated measures of ANOVA (RMANOVA), however, would permit the comparison between the correlated measurements as long as the condition of sphericity assumption is satisfied. Conclusively, parametric statistical methods should be used only when the assumptions of parametric statistics, such as normality and sphericity, are established.

Application of survival analysis methodology to the quantitative analysis of LC-MS proteomics data.

PubMed

Tekwe, Carmen D; Carroll, Raymond J; Dabney, Alan R

2012-08-01

Protein abundance in quantitative proteomics is often based on observed spectral features derived from liquid chromatography mass spectrometry (LC-MS) or LC-MS/MS experiments. Peak intensities are largely non-normal in distribution. Furthermore, LC-MS-based proteomics data frequently have large proportions of missing peak intensities due to censoring mechanisms on low-abundance spectral features. Recognizing that the observed peak intensities detected with the LC-MS method are all positive, skewed and often left-censored, we propose using survival methodology to carry out differential expression analysis of proteins. Various standard statistical techniques including non-parametric tests such as the Kolmogorov-Smirnov and Wilcoxon-Mann-Whitney rank sum tests, and the parametric survival model and accelerated failure time-model with log-normal, log-logistic and Weibull distributions were used to detect any differentially expressed proteins. The statistical operating characteristics of each method are explored using both real and simulated datasets. Survival methods generally have greater statistical power than standard differential expression methods when the proportion of missing protein level data is 5% or more. In particular, the AFT models we consider consistently achieve greater statistical power than standard testing procedures, with the discrepancy widening with increasing missingness in the proportions. The testing procedures discussed in this article can all be performed using readily available software such as R. The R codes are provided as supplemental materials. ctekwe@stat.tamu.edu.

Statistical analysis of the electric energy production from photovoltaic conversion using mobile and fixed constructions

NASA Astrophysics Data System (ADS)

Bugała, Artur; Bednarek, Karol; Kasprzyk, Leszek; Tomczewski, Andrzej

2017-10-01

The paper presents the most representative - from the three-year measurement time period - characteristics of daily and monthly electricity production from a photovoltaic conversion using modules installed in a fixed and 2-axis tracking construction. Results are presented for selected summer, autumn, spring and winter days. Analyzed measuring stand is located on the roof of the Faculty of Electrical Engineering Poznan University of Technology building. The basic parameters of the statistical analysis like mean value, standard deviation, skewness, kurtosis, median, range, or coefficient of variation were used. It was found that the asymmetry factor can be useful in the analysis of the daily electricity production from a photovoltaic conversion. In order to determine the repeatability of monthly electricity production, occurring between the summer, and summer and winter months, a non-parametric Mann-Whitney U test was used as a statistical solution. In order to analyze the repeatability of daily peak hours, describing the largest value of the hourly electricity production, a non-parametric Kruskal-Wallis test was applied as an extension of the Mann-Whitney U test. Based on the analysis of the electric energy distribution from a prepared monitoring system it was found that traditional forecasting methods of the electricity production from a photovoltaic conversion, like multiple regression models, should not be the preferred methods of the analysis.

Analysis of Parasite and Other Skewed Counts

PubMed Central

Alexander, Neal

2012-01-01

Objective To review methods for the statistical analysis of parasite and other skewed count data. Methods Statistical methods for skewed count data are described and compared, with reference to those used over a ten year period of Tropical Medicine and International Health. Two parasitological datasets are used for illustration. Results Ninety papers were identified, 89 with descriptive and 60 with inferential analysis. A lack of clarity is noted in identifying measures of location, in particular the Williams and geometric mean. The different measures are compared, emphasizing the legitimacy of the arithmetic mean for skewed data. In the published papers, the t test and related methods were often used on untransformed data, which is likely to be invalid. Several approaches to inferential analysis are described, emphasizing 1) non-parametric methods, while noting that they are not simply comparisons of medians, and 2) generalized linear modelling, in particular with the negative binomial distribution. Additional methods, such as the bootstrap, with potential for greater use are described. Conclusions Clarity is recommended when describing transformations and measures of location. It is suggested that non-parametric methods and generalized linear models are likely to be sufficient for most analyses. PMID:22943299

A Methodology for the Parametric Reconstruction of Non-Steady and Noisy Meteorological Time Series

NASA Astrophysics Data System (ADS)

Rovira, F.; Palau, J. L.; Millán, M.

2009-09-01

Climatic and meteorological time series often show some persistence (in time) in the variability of certain features. One could regard annual, seasonal and diurnal time variability as trivial persistence in the variability of some meteorological magnitudes (as, e.g., global radiation, air temperature above surface, etc.). In these cases, the traditional Fourier transform into frequency space will show the principal harmonics as the components with the largest amplitude. Nevertheless, meteorological measurements often show other non-steady (in time) variability. Some fluctuations in measurements (at different time scales) are driven by processes that prevail on some days (or months) of the year but disappear on others. By decomposing a time series into time-frequency space through the continuous wavelet transformation, one is able to determine both the dominant modes of variability and how those modes vary in time. This study is based on a numerical methodology to analyse non-steady principal harmonics in noisy meteorological time series. This methodology combines both the continuous wavelet transform and the development of a parametric model that includes the time evolution of the principal and the most statistically significant harmonics of the original time series. The parameterisation scheme proposed in this study consists of reproducing the original time series by means of a statistically significant finite sum of sinusoidal signals (waves), each defined by using the three usual parameters: amplitude, frequency and phase. To ensure the statistical significance of the parametric reconstruction of the original signal, we propose a standard statistical t-student analysis of the confidence level of the amplitude in the parametric spectrum for the different wave components. Once we have assured the level of significance of the different waves composing the parametric model, we can obtain the statistically significant principal harmonics (in time) of the original time series by using the Fourier transform of the modelled signal. Acknowledgements The CEAM Foundation is supported by the Generalitat Valenciana and BANCAIXA (València, Spain). This study has been partially funded by the European Commission (FP VI, Integrated Project CIRCE - No. 036961) and by the Ministerio de Ciencia e Innovación, research projects "TRANSREG” (CGL2007-65359/CLI) and "GRACCIE” (CSD2007-00067, Program CONSOLIDER-INGENIO 2010).

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

BLIND EXTRACTION OF AN EXOPLANETARY SPECTRUM THROUGH INDEPENDENT COMPONENT ANALYSIS

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

Waldmann, I. P.; Tinetti, G.; Hollis, M. D. J.

2013-03-20

Blind-source separation techniques are used to extract the transmission spectrum of the hot-Jupiter HD189733b recorded by the Hubble/NICMOS instrument. Such a 'blind' analysis of the data is based on the concept of independent component analysis. The detrending of Hubble/NICMOS data using the sole assumption that nongaussian systematic noise is statistically independent from the desired light-curve signals is presented. By not assuming any prior or auxiliary information but the data themselves, it is shown that spectroscopic errors only about 10%-30% larger than parametric methods can be obtained for 11 spectral bins with bin sizes of {approx}0.09 {mu}m. This represents a reasonablemore » trade-off between a higher degree of objectivity for the non-parametric methods and smaller standard errors for the parametric de-trending. Results are discussed in light of previous analyses published in the literature. The fact that three very different analysis techniques yield comparable spectra is a strong indication of the stability of these results.« less

Non-parametric wall model and methods of identifying boundary conditions for moments in gas flow equations

NASA Astrophysics Data System (ADS)

Liao, Meng; To, Quy-Dong; Léonard, Céline; Monchiet, Vincent

2018-03-01

In this paper, we use the molecular dynamics simulation method to study gas-wall boundary conditions. Discrete scattering information of gas molecules at the wall surface is obtained from collision simulations. The collision data can be used to identify the accommodation coefficients for parametric wall models such as Maxwell and Cercignani-Lampis scattering kernels. Since these scattering kernels are based on a limited number of accommodation coefficients, we adopt non-parametric statistical methods to construct the kernel to overcome these issues. Different from parametric kernels, the non-parametric kernels require no parameter (i.e. accommodation coefficients) and no predefined distribution. We also propose approaches to derive directly the Navier friction and Kapitza thermal resistance coefficients as well as other interface coefficients associated with moment equations from the non-parametric kernels. The methods are applied successfully to systems composed of CH4 or CO2 and graphite, which are of interest to the petroleum industry.

Radioactivity Registered With a Small Number of Events

NASA Astrophysics Data System (ADS)

Zlokazov, Victor; Utyonkov, Vladimir

2018-02-01

The synthesis of superheavy elements asks for the analysis of low statistics experimental data presumably obeying an unknown exponential distribution and to take the decision whether they originate from one source or have admixtures. Here we analyze predictions following from non-parametrical methods, employing only such fundamental sample properties as the sample mean, the median and the mode.

Development of computer-assisted instruction application for statistical data analysis android platform as learning resource

NASA Astrophysics Data System (ADS)

Hendikawati, P.; Arifudin, R.; Zahid, M. Z.

2018-03-01

This study aims to design an android Statistics Data Analysis application that can be accessed through mobile devices to making it easier for users to access. The Statistics Data Analysis application includes various topics of basic statistical along with a parametric statistics data analysis application. The output of this application system is parametric statistics data analysis that can be used for students, lecturers, and users who need the results of statistical calculations quickly and easily understood. Android application development is created using Java programming language. The server programming language uses PHP with the Code Igniter framework, and the database used MySQL. The system development methodology used is the Waterfall methodology with the stages of analysis, design, coding, testing, and implementation and system maintenance. This statistical data analysis application is expected to support statistical lecturing activities and make students easier to understand the statistical analysis of mobile devices.

Assessing noninferiority in a three-arm trial using the Bayesian approach.

PubMed

Ghosh, Pulak; Nathoo, Farouk; Gönen, Mithat; Tiwari, Ram C

2011-07-10

Non-inferiority trials, which aim to demonstrate that a test product is not worse than a competitor by more than a pre-specified small amount, are of great importance to the pharmaceutical community. As a result, methodology for designing and analyzing such trials is required, and developing new methods for such analysis is an important area of statistical research. The three-arm trial consists of a placebo, a reference and an experimental treatment, and simultaneously tests the superiority of the reference over the placebo along with comparing this reference to an experimental treatment. In this paper, we consider the analysis of non-inferiority trials using Bayesian methods which incorporate both parametric as well as semi-parametric models. The resulting testing approach is both flexible and robust. The benefit of the proposed Bayesian methods is assessed via simulation, based on a study examining home-based blood pressure interventions. Copyright © 2011 John Wiley & Sons, Ltd.

Estimating technical efficiency in the hospital sector with panel data: a comparison of parametric and non-parametric techniques.

PubMed

Siciliani, Luigi

2006-01-01

Policy makers are increasingly interested in developing performance indicators that measure hospital efficiency. These indicators may give the purchasers of health services an additional regulatory tool to contain health expenditure. Using panel data, this study compares different parametric (econometric) and non-parametric (linear programming) techniques for the measurement of a hospital's technical efficiency. This comparison was made using a sample of 17 Italian hospitals in the years 1996-9. Highest correlations are found in the efficiency scores between the non-parametric data envelopment analysis under the constant returns to scale assumption (DEA-CRS) and several parametric models. Correlation reduces markedly when using more flexible non-parametric specifications such as data envelopment analysis under the variable returns to scale assumption (DEA-VRS) and the free disposal hull (FDH) model. Correlation also generally reduces when moving from one output to two-output specifications. This analysis suggests that there is scope for developing performance indicators at hospital level using panel data, but it is important that extensive sensitivity analysis is carried out if purchasers wish to make use of these indicators in practice.

Robust biological parametric mapping: an improved technique for multimodal brain image analysis

NASA Astrophysics Data System (ADS)

Yang, Xue; Beason-Held, Lori; Resnick, Susan M.; Landman, Bennett A.

2011-03-01

Mapping the quantitative relationship between structure and function in the human brain is an important and challenging problem. Numerous volumetric, surface, region of interest and voxelwise image processing techniques have been developed to statistically assess potential correlations between imaging and non-imaging metrics. Recently, biological parametric mapping has extended the widely popular statistical parametric approach to enable application of the general linear model to multiple image modalities (both for regressors and regressands) along with scalar valued observations. This approach offers great promise for direct, voxelwise assessment of structural and functional relationships with multiple imaging modalities. However, as presented, the biological parametric mapping approach is not robust to outliers and may lead to invalid inferences (e.g., artifactual low p-values) due to slight mis-registration or variation in anatomy between subjects. To enable widespread application of this approach, we introduce robust regression and robust inference in the neuroimaging context of application of the general linear model. Through simulation and empirical studies, we demonstrate that our robust approach reduces sensitivity to outliers without substantial degradation in power. The robust approach and associated software package provides a reliable way to quantitatively assess voxelwise correlations between structural and functional neuroimaging modalities.

Short-term monitoring of benzene air concentration in an urban area: a preliminary study of application of Kruskal-Wallis non-parametric test to assess pollutant impact on global environment and indoor.

PubMed

Mura, Maria Chiara; De Felice, Marco; Morlino, Roberta; Fuselli, Sergio

2010-01-01

In step with the need to develop statistical procedures to manage small-size environmental samples, in this work we have used concentration values of benzene (C6H6), concurrently detected by seven outdoor and indoor monitoring stations over 12 000 minutes, in order to assess the representativeness of collected data and the impact of the pollutant on indoor environment. Clearly, the former issue is strictly connected to sampling-site geometry, which proves critical to correctly retrieving information from analysis of pollutants of sanitary interest. Therefore, according to current criteria for network-planning, single stations have been interpreted as nodes of a set of adjoining triangles; then, a) node pairs have been taken into account in order to estimate pollutant stationarity on triangle sides, as well as b) node triplets, to statistically associate data from air-monitoring with the corresponding territory area, and c) node sextuplets, to assess the impact probability of the outdoor pollutant on indoor environment for each area. Distributions from the various node combinations are all non-Gaussian, in the consequently, Kruskal-Wallis (KW) non-parametric statistics has been exploited to test variability on continuous density function from each pair, triplet and sextuplet. Results from the above-mentioned statistical analysis have shown randomness of site selection, which has not allowed a reliable generalization of monitoring data to the entire selected territory, except for a single "forced" case (70%); most important, they suggest a possible procedure to optimize network design.

The urban heat island in Rio de Janeiro, Brazil, in the last 30 years using remote sensing data

NASA Astrophysics Data System (ADS)

Peres, Leonardo de Faria; Lucena, Andrews José de; Rotunno Filho, Otto Corrêa; França, José Ricardo de Almeida

2018-02-01

The aim of this work is to study urban heat island (UHI) in Metropolitan Area of Rio de Janeiro (MARJ) based on the analysis of land-surface temperature (LST) and land-use patterns retrieved from Landsat-5/Thematic Mapper (TM), Landsat-7/Enhanced Thematic Mapper Plus (ETM+) and Landsat-8/Operational Land Imager (OLI) and Thermal Infrared Sensors (TIRS) data covering a 32-year period between 1984 and 2015. LST temporal evolution is assessed by comparing the average LST composites for 1984-1999 and 2000-2015 where the parametric Student t-test was conducted at 5% significance level to map the pixels where LST for the more recent period is statistically significantly greater than the previous one. The non-parametric Mann-Whitney-Wilcoxon rank sum test has also confirmed at the same 5% significance level that the more recent period (2000-2015) has higher LST values. UHI intensity between ;urban; and ;rural/urban low density; (;vegetation;) areas for 1984-1999 and 2000-2015 was established and confirmed by both parametric and non-parametric tests at 1% significance level as 3.3 °C (5.1 °C) and 4.4 °C (7.1 °C), respectively. LST has statistically significantly (p-value < 0.01) increased over time in two of three land cover classes (;urban; and ;urban low density;), respectively by 1.9 °C and 0.9 °C, except in ;vegetation; class. A spatial analysis was also performed to identify the urban pixels within MARJ where UHI is more intense by subtracting the LST of these pixels from the LST mean value of ;vegetation; land-use class.

It's all relative: ranking the diversity of aquatic bacterial communities.

PubMed

Shaw, Allison K; Halpern, Aaron L; Beeson, Karen; Tran, Bao; Venter, J Craig; Martiny, Jennifer B H

2008-09-01

The study of microbial diversity patterns is hampered by the enormous diversity of microbial communities and the lack of resources to sample them exhaustively. For many questions about richness and evenness, however, one only needs to know the relative order of diversity among samples rather than total diversity. We used 16S libraries from the Global Ocean Survey to investigate the ability of 10 diversity statistics (including rarefaction, non-parametric, parametric, curve extrapolation and diversity indices) to assess the relative diversity of six aquatic bacterial communities. Overall, we found that the statistics yielded remarkably similar rankings of the samples for a given sequence similarity cut-off. This correspondence, despite the different underlying assumptions of the statistics, suggests that diversity statistics are a useful tool for ranking samples of microbial diversity. In addition, sequence similarity cut-off influenced the diversity ranking of the samples, demonstrating that diversity statistics can also be used to detect differences in phylogenetic structure among microbial communities. Finally, a subsampling analysis suggests that further sequencing from these particular clone libraries would not have substantially changed the richness rankings of the samples.

Tips and Tricks for Successful Application of Statistical Methods to Biological Data.

PubMed

Schlenker, Evelyn

2016-01-01

This chapter discusses experimental design and use of statistics to describe characteristics of data (descriptive statistics) and inferential statistics that test the hypothesis posed by the investigator. Inferential statistics, based on probability distributions, depend upon the type and distribution of the data. For data that are continuous, randomly and independently selected, as well as normally distributed more powerful parametric tests such as Student's t test and analysis of variance (ANOVA) can be used. For non-normally distributed or skewed data, transformation of the data (using logarithms) may normalize the data allowing use of parametric tests. Alternatively, with skewed data nonparametric tests can be utilized, some of which rely on data that are ranked prior to statistical analysis. Experimental designs and analyses need to balance between committing type 1 errors (false positives) and type 2 errors (false negatives). For a variety of clinical studies that determine risk or benefit, relative risk ratios (random clinical trials and cohort studies) or odds ratios (case-control studies) are utilized. Although both use 2 × 2 tables, their premise and calculations differ. Finally, special statistical methods are applied to microarray and proteomics data, since the large number of genes or proteins evaluated increase the likelihood of false discoveries. Additional studies in separate samples are used to verify microarray and proteomic data. Examples in this chapter and references are available to help continued investigation of experimental designs and appropriate data analysis.

Likert scales, levels of measurement and the "laws" of statistics.

PubMed

Norman, Geoff

2010-12-01

Reviewers of research reports frequently criticize the choice of statistical methods. While some of these criticisms are well-founded, frequently the use of various parametric methods such as analysis of variance, regression, correlation are faulted because: (a) the sample size is too small, (b) the data may not be normally distributed, or (c) The data are from Likert scales, which are ordinal, so parametric statistics cannot be used. In this paper, I dissect these arguments, and show that many studies, dating back to the 1930s consistently show that parametric statistics are robust with respect to violations of these assumptions. Hence, challenges like those above are unfounded, and parametric methods can be utilized without concern for "getting the wrong answer".

Parametric Cost Deployment

NASA Technical Reports Server (NTRS)

Dean, Edwin B.

1995-01-01

Parametric cost analysis is a mathematical approach to estimating cost. Parametric cost analysis uses non-cost parameters, such as quality characteristics, to estimate the cost to bring forth, sustain, and retire a product. This paper reviews parametric cost analysis and shows how it can be used within the cost deployment process.

Comparison Between Linear and Non-parametric Regression Models for Genome-Enabled Prediction in Wheat

PubMed Central

Pérez-Rodríguez, Paulino; Gianola, Daniel; González-Camacho, Juan Manuel; Crossa, José; Manès, Yann; Dreisigacker, Susanne

2012-01-01

In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The linear models were linear on marker effects and included the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B. The non-linear models (this refers to non-linearity on markers) were reproducing kernel Hilbert space (RKHS) regression, Bayesian regularized neural networks (BRNN), and radial basis function neural networks (RBFNN). These statistical models were compared using 306 elite wheat lines from CIMMYT genotyped with 1717 diversity array technology (DArT) markers and two traits, days to heading (DTH) and grain yield (GY), measured in each of 12 environments. It was found that the three non-linear models had better overall prediction accuracy than the linear regression specification. Results showed a consistent superiority of RKHS and RBFNN over the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B models. PMID:23275882

Comparison between linear and non-parametric regression models for genome-enabled prediction in wheat.

PubMed

Pérez-Rodríguez, Paulino; Gianola, Daniel; González-Camacho, Juan Manuel; Crossa, José; Manès, Yann; Dreisigacker, Susanne

2012-12-01

In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The linear models were linear on marker effects and included the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B. The non-linear models (this refers to non-linearity on markers) were reproducing kernel Hilbert space (RKHS) regression, Bayesian regularized neural networks (BRNN), and radial basis function neural networks (RBFNN). These statistical models were compared using 306 elite wheat lines from CIMMYT genotyped with 1717 diversity array technology (DArT) markers and two traits, days to heading (DTH) and grain yield (GY), measured in each of 12 environments. It was found that the three non-linear models had better overall prediction accuracy than the linear regression specification. Results showed a consistent superiority of RKHS and RBFNN over the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B models.

Establishment of Biological Reference Intervals and Reference Curve for Urea by Exploratory Parametric and Non-Parametric Quantile Regression Models.

PubMed

Sarkar, Rajarshi

2013-07-01

The validity of the entire renal function tests as a diagnostic tool depends substantially on the Biological Reference Interval (BRI) of urea. Establishment of BRI of urea is difficult partly because exclusion criteria for selection of reference data are quite rigid and partly due to the compartmentalization considerations regarding age and sex of the reference individuals. Moreover, construction of Biological Reference Curve (BRC) of urea is imperative to highlight the partitioning requirements. This a priori study examines the data collected by measuring serum urea of 3202 age and sex matched individuals, aged between 1 and 80 years, by a kinetic UV Urease/GLDH method on a Roche Cobas 6000 auto-analyzer. Mann-Whitney U test of the reference data confirmed the partitioning requirement by both age and sex. Further statistical analysis revealed the incompatibility of the data for a proposed parametric model. Hence the data was non-parametrically analysed. BRI was found to be identical for both sexes till the 2(nd) decade, and the BRI for males increased progressively 6(th) decade onwards. Four non-parametric models were postulated for construction of BRC: Gaussian kernel, double kernel, local mean and local constant, of which the last one generated the best-fitting curves. Clinical decision making should become easier and diagnostic implications of renal function tests should become more meaningful if this BRI is followed and the BRC is used as a desktop tool in conjunction with similar data for serum creatinine.

kruX: matrix-based non-parametric eQTL discovery.

PubMed

Qi, Jianlong; Asl, Hassan Foroughi; Björkegren, Johan; Michoel, Tom

2014-01-14

The Kruskal-Wallis test is a popular non-parametric statistical test for identifying expression quantitative trait loci (eQTLs) from genome-wide data due to its robustness against variations in the underlying genetic model and expression trait distribution, but testing billions of marker-trait combinations one-by-one can become computationally prohibitive. We developed kruX, an algorithm implemented in Matlab, Python and R that uses matrix multiplications to simultaneously calculate the Kruskal-Wallis test statistic for several millions of marker-trait combinations at once. KruX is more than ten thousand times faster than computing associations one-by-one on a typical human dataset. We used kruX and a dataset of more than 500k SNPs and 20k expression traits measured in 102 human blood samples to compare eQTLs detected by the Kruskal-Wallis test to eQTLs detected by the parametric ANOVA and linear model methods. We found that the Kruskal-Wallis test is more robust against data outliers and heterogeneous genotype group sizes and detects a higher proportion of non-linear associations, but is more conservative for calling additive linear associations. kruX enables the use of robust non-parametric methods for massive eQTL mapping without the need for a high-performance computing infrastructure and is freely available from http://krux.googlecode.com.

Characterizing rainfall of hot arid region by using time-series modeling and sustainability approaches: a case study from Gujarat, India

NASA Astrophysics Data System (ADS)

Machiwal, Deepesh; Kumar, Sanjay; Dayal, Devi

2016-05-01

This study aimed at characterization of rainfall dynamics in a hot arid region of Gujarat, India by employing time-series modeling techniques and sustainability approach. Five characteristics, i.e., normality, stationarity, homogeneity, presence/absence of trend, and persistence of 34-year (1980-2013) period annual rainfall time series of ten stations were identified/detected by applying multiple parametric and non-parametric statistical tests. Furthermore, the study involves novelty of proposing sustainability concept for evaluating rainfall time series and demonstrated the concept, for the first time, by identifying the most sustainable rainfall series following reliability ( R y), resilience ( R e), and vulnerability ( V y) approach. Box-whisker plots, normal probability plots, and histograms indicated that the annual rainfall of Mandvi and Dayapar stations is relatively more positively skewed and non-normal compared with that of other stations, which is due to the presence of severe outlier and extreme. Results of Shapiro-Wilk test and Lilliefors test revealed that annual rainfall series of all stations significantly deviated from normal distribution. Two parametric t tests and the non-parametric Mann-Whitney test indicated significant non-stationarity in annual rainfall of Rapar station, where the rainfall was also found to be non-homogeneous based on the results of four parametric homogeneity tests. Four trend tests indicated significantly increasing rainfall trends at Rapar and Gandhidham stations. The autocorrelation analysis suggested the presence of persistence of statistically significant nature in rainfall series of Bhachau (3-year time lag), Mundra (1- and 9-year time lag), Nakhatrana (9-year time lag), and Rapar (3- and 4-year time lag). Results of sustainability approach indicated that annual rainfall of Mundra and Naliya stations ( R y = 0.50 and 0.44; R e = 0.47 and 0.47; V y = 0.49 and 0.46, respectively) are the most sustainable and dependable compared with that of other stations. The highest values of sustainability index at Mundra (0.120) and Naliya (0.112) stations confirmed the earlier findings of R y- R e- V y approach. In general, annual rainfall of the study area is less reliable, less resilient, and moderately vulnerable, which emphasizes the need of developing suitable strategies for managing water resources of the area on sustainable basis. Finally, it is recommended that multiple statistical tests (at least two) should be used in time-series modeling for making reliable decisions. Moreover, methodology and findings of the sustainability concept in rainfall time series can easily be adopted in other arid regions of the world.

Parametric Method Performance for Dynamic 3'-Deoxy-3'-18F-Fluorothymidine PET/CT in Epidermal Growth Factor Receptor-Mutated Non-Small Cell Lung Carcinoma Patients Before and During Therapy.

PubMed

Kramer, Gerbrand Maria; Frings, Virginie; Heijtel, Dennis; Smit, E F; Hoekstra, Otto S; Boellaard, Ronald

2017-06-01

The objective of this study was to validate several parametric methods for quantification of 3'-deoxy-3'- 18 F-fluorothymidine ( 18 F-FLT) PET in advanced-stage non-small cell lung carcinoma (NSCLC) patients with an activating epidermal growth factor receptor mutation who were treated with gefitinib or erlotinib. Furthermore, we evaluated the impact of noise on accuracy and precision of the parametric analyses of dynamic 18 F-FLT PET/CT to assess the robustness of these methods. Methods : Ten NSCLC patients underwent dynamic 18 F-FLT PET/CT at baseline and 7 and 28 d after the start of treatment. Parametric images were generated using plasma input Logan graphic analysis and 2 basis functions-based methods: a 2-tissue-compartment basis function model (BFM) and spectral analysis (SA). Whole-tumor-averaged parametric pharmacokinetic parameters were compared with those obtained by nonlinear regression of the tumor time-activity curve using a reversible 2-tissue-compartment model with blood volume fraction. In addition, 2 statistically equivalent datasets were generated by countwise splitting the original list-mode data, each containing 50% of the total counts. Both new datasets were reconstructed, and parametric pharmacokinetic parameters were compared between the 2 replicates and the original data. Results: After the settings of each parametric method were optimized, distribution volumes (V T ) obtained with Logan graphic analysis, BFM, and SA all correlated well with those derived using nonlinear regression at baseline and during therapy ( R 2 ≥ 0.94; intraclass correlation coefficient > 0.97). SA-based V T images were most robust to increased noise on a voxel-level (repeatability coefficient, 16% vs. >26%). Yet BFM generated the most accurate K 1 values ( R 2 = 0.94; intraclass correlation coefficient, 0.96). Parametric K 1 data showed a larger variability in general; however, no differences were found in robustness between methods (repeatability coefficient, 80%-84%). Conclusion: Both BFM and SA can generate quantitatively accurate parametric 18 F-FLT V T images in NSCLC patients before and during therapy. SA was more robust to noise, yet BFM provided more accurate parametric K 1 data. We therefore recommend BFM as the preferred parametric method for analysis of dynamic 18 F-FLT PET/CT studies; however, SA can also be used. © 2017 by the Society of Nuclear Medicine and Molecular Imaging.

Sensitivity to imputation models and assumptions in receiver operating characteristic analysis with incomplete data

PubMed Central

Karakaya, Jale; Karabulut, Erdem; Yucel, Recai M.

2015-01-01

Modern statistical methods using incomplete data have been increasingly applied in a wide variety of substantive problems. Similarly, receiver operating characteristic (ROC) analysis, a method used in evaluating diagnostic tests or biomarkers in medical research, has also been increasingly popular problem in both its development and application. While missing-data methods have been applied in ROC analysis, the impact of model mis-specification and/or assumptions (e.g. missing at random) underlying the missing data has not been thoroughly studied. In this work, we study the performance of multiple imputation (MI) inference in ROC analysis. Particularly, we investigate parametric and non-parametric techniques for MI inference under common missingness mechanisms. Depending on the coherency of the imputation model with the underlying data generation mechanism, our results show that MI generally leads to well-calibrated inferences under ignorable missingness mechanisms. PMID:26379316

Building and using a statistical 3D motion atlas for analyzing myocardial contraction in MRI

NASA Astrophysics Data System (ADS)

Rougon, Nicolas F.; Petitjean, Caroline; Preteux, Francoise J.

2004-05-01

We address the issue of modeling and quantifying myocardial contraction from 4D MR sequences, and present an unsupervised approach for building and using a statistical 3D motion atlas for the normal heart. This approach relies on a state-of-the-art variational non rigid registration (NRR) technique using generalized information measures, which allows for robust intra-subject motion estimation and inter-subject anatomical alignment. The atlas is built from a collection of jointly acquired tagged and cine MR exams in short- and long-axis views. Subject-specific non parametric motion estimates are first obtained by incremental NRR of tagged images onto the end-diastolic (ED) frame. Individual motion data are then transformed into the coordinate system of a reference subject using subject-to-reference mappings derived by NRR of cine ED images. Finally, principal component analysis of aligned motion data is performed for each cardiac phase, yielding a mean model and a set of eigenfields encoding kinematic ariability. The latter define an organ-dedicated hierarchical motion basis which enables parametric motion measurement from arbitrary tagged MR exams. To this end, the atlas is transformed into subject coordinates by reference-to-subject NRR of ED cine frames. Atlas-based motion estimation is then achieved by parametric NRR of tagged images onto the ED frame, yielding a compact description of myocardial contraction during diastole.

Breast-Lesion Characterization using Textural Features of Quantitative Ultrasound Parametric Maps.

PubMed

Sadeghi-Naini, Ali; Suraweera, Harini; Tran, William Tyler; Hadizad, Farnoosh; Bruni, Giancarlo; Rastegar, Rashin Fallah; Curpen, Belinda; Czarnota, Gregory J

2017-10-20

This study evaluated, for the first time, the efficacy of quantitative ultrasound (QUS) spectral parametric maps in conjunction with texture-analysis techniques to differentiate non-invasively benign versus malignant breast lesions. Ultrasound B-mode images and radiofrequency data were acquired from 78 patients with suspicious breast lesions. QUS spectral-analysis techniques were performed on radiofrequency data to generate parametric maps of mid-band fit, spectral slope, spectral intercept, spacing among scatterers, average scatterer diameter, and average acoustic concentration. Texture-analysis techniques were applied to determine imaging biomarkers consisting of mean, contrast, correlation, energy and homogeneity features of parametric maps. These biomarkers were utilized to classify benign versus malignant lesions with leave-one-patient-out cross-validation. Results were compared to histopathology findings from biopsy specimens and radiology reports on MR images to evaluate the accuracy of technique. Among the biomarkers investigated, one mean-value parameter and 14 textural features demonstrated statistically significant differences (p < 0.05) between the two lesion types. A hybrid biomarker developed using a stepwise feature selection method could classify the legions with a sensitivity of 96%, a specificity of 84%, and an AUC of 0.97. Findings from this study pave the way towards adapting novel QUS-based frameworks for breast cancer screening and rapid diagnosis in clinic.

A nonparametric spatial scan statistic for continuous data.

PubMed

Jung, Inkyung; Cho, Ho Jin

2015-10-20

Spatial scan statistics are widely used for spatial cluster detection, and several parametric models exist. For continuous data, a normal-based scan statistic can be used. However, the performance of the model has not been fully evaluated for non-normal data. We propose a nonparametric spatial scan statistic based on the Wilcoxon rank-sum test statistic and compared the performance of the method with parametric models via a simulation study under various scenarios. The nonparametric method outperforms the normal-based scan statistic in terms of power and accuracy in almost all cases under consideration in the simulation study. The proposed nonparametric spatial scan statistic is therefore an excellent alternative to the normal model for continuous data and is especially useful for data following skewed or heavy-tailed distributions.

kruX: matrix-based non-parametric eQTL discovery

PubMed Central

2014-01-01

Background The Kruskal-Wallis test is a popular non-parametric statistical test for identifying expression quantitative trait loci (eQTLs) from genome-wide data due to its robustness against variations in the underlying genetic model and expression trait distribution, but testing billions of marker-trait combinations one-by-one can become computationally prohibitive. Results We developed kruX, an algorithm implemented in Matlab, Python and R that uses matrix multiplications to simultaneously calculate the Kruskal-Wallis test statistic for several millions of marker-trait combinations at once. KruX is more than ten thousand times faster than computing associations one-by-one on a typical human dataset. We used kruX and a dataset of more than 500k SNPs and 20k expression traits measured in 102 human blood samples to compare eQTLs detected by the Kruskal-Wallis test to eQTLs detected by the parametric ANOVA and linear model methods. We found that the Kruskal-Wallis test is more robust against data outliers and heterogeneous genotype group sizes and detects a higher proportion of non-linear associations, but is more conservative for calling additive linear associations. Conclusion kruX enables the use of robust non-parametric methods for massive eQTL mapping without the need for a high-performance computing infrastructure and is freely available from http://krux.googlecode.com. PMID:24423115

STAPP: Spatiotemporal analysis of plantar pressure measurements using statistical parametric mapping.

PubMed

Booth, Brian G; Keijsers, Noël L W; Sijbers, Jan; Huysmans, Toon

2018-05-03

Pedobarography produces large sets of plantar pressure samples that are routinely subsampled (e.g. using regions of interest) or aggregated (e.g. center of pressure trajectories, peak pressure images) in order to simplify statistical analysis and provide intuitive clinical measures. We hypothesize that these data reductions discard gait information that can be used to differentiate between groups or conditions. To test the hypothesis of null information loss, we created an implementation of statistical parametric mapping (SPM) for dynamic plantar pressure datasets (i.e. plantar pressure videos). Our SPM software framework brings all plantar pressure videos into anatomical and temporal correspondence, then performs statistical tests at each sampling location in space and time. Novelly, we introduce non-linear temporal registration into the framework in order to normalize for timing differences within the stance phase. We refer to our software framework as STAPP: spatiotemporal analysis of plantar pressure measurements. Using STAPP, we tested our hypothesis on plantar pressure videos from 33 healthy subjects walking at different speeds. As walking speed increased, STAPP was able to identify significant decreases in plantar pressure at mid-stance from the heel through the lateral forefoot. The extent of these plantar pressure decreases has not previously been observed using existing plantar pressure analysis techniques. We therefore conclude that the subsampling of plantar pressure videos - a task which led to the discarding of gait information in our study - can be avoided using STAPP. Copyright © 2018 Elsevier B.V. All rights reserved.

Study design and statistical analysis of data in human population studies with the micronucleus assay.

PubMed

Ceppi, Marcello; Gallo, Fabio; Bonassi, Stefano

2011-01-01

The most common study design performed in population studies based on the micronucleus (MN) assay, is the cross-sectional study, which is largely performed to evaluate the DNA damaging effects of exposure to genotoxic agents in the workplace, in the environment, as well as from diet or lifestyle factors. Sample size is still a critical issue in the design of MN studies since most recent studies considering gene-environment interaction, often require a sample size of several hundred subjects, which is in many cases difficult to achieve. The control of confounding is another major threat to the validity of causal inference. The most popular confounders considered in population studies using MN are age, gender and smoking habit. Extensive attention is given to the assessment of effect modification, given the increasing inclusion of biomarkers of genetic susceptibility in the study design. Selected issues concerning the statistical treatment of data have been addressed in this mini-review, starting from data description, which is a critical step of statistical analysis, since it allows to detect possible errors in the dataset to be analysed and to check the validity of assumptions required for more complex analyses. Basic issues dealing with statistical analysis of biomarkers are extensively evaluated, including methods to explore the dose-response relationship among two continuous variables and inferential analysis. A critical approach to the use of parametric and non-parametric methods is presented, before addressing the issue of most suitable multivariate models to fit MN data. In the last decade, the quality of statistical analysis of MN data has certainly evolved, although even nowadays only a small number of studies apply the Poisson model, which is the most suitable method for the analysis of MN data.

Appraisal of within- and between-laboratory reproducibility of non-radioisotopic local lymph node assay using flow cytometry, LLNA:BrdU-FCM: comparison of OECD TG429 performance standard and statistical evaluation.

PubMed

Yang, Hyeri; Na, Jihye; Jang, Won-Hee; Jung, Mi-Sook; Jeon, Jun-Young; Heo, Yong; Yeo, Kyung-Wook; Jo, Ji-Hoon; Lim, Kyung-Min; Bae, SeungJin

2015-05-05

Mouse local lymph node assay (LLNA, OECD TG429) is an alternative test replacing conventional guinea pig tests (OECD TG406) for the skin sensitization test but the use of a radioisotopic agent, (3)H-thymidine, deters its active dissemination. New non-radioisotopic LLNA, LLNA:BrdU-FCM employs a non-radioisotopic analog, 5-bromo-2'-deoxyuridine (BrdU) and flow cytometry. For an analogous method, OECD TG429 performance standard (PS) advises that two reference compounds be tested repeatedly and ECt(threshold) values obtained must fall within acceptable ranges to prove within- and between-laboratory reproducibility. However, this criteria is somewhat arbitrary and sample size of ECt is less than 5, raising concerns about insufficient reliability. Here, we explored various statistical methods to evaluate the reproducibility of LLNA:BrdU-FCM with stimulation index (SI), the raw data for ECt calculation, produced from 3 laboratories. Descriptive statistics along with graphical representation of SI was presented. For inferential statistics, parametric and non-parametric methods were applied to test the reproducibility of SI of a concurrent positive control and the robustness of results were investigated. Descriptive statistics and graphical representation of SI alone could illustrate the within- and between-laboratory reproducibility. Inferential statistics employing parametric and nonparametric methods drew similar conclusion. While all labs passed within- and between-laboratory reproducibility criteria given by OECD TG429 PS based on ECt values, statistical evaluation based on SI values showed that only two labs succeeded in achieving within-laboratory reproducibility. For those two labs that satisfied the within-lab reproducibility, between-laboratory reproducibility could be also attained based on inferential as well as descriptive statistics. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

A Comparison of Distribution Free and Non-Distribution Free Factor Analysis Methods

ERIC Educational Resources Information Center

Ritter, Nicola L.

2012-01-01

Many researchers recognize that factor analysis can be conducted on both correlation matrices and variance-covariance matrices. Although most researchers extract factors from non-distribution free or parametric methods, researchers can also extract factors from distribution free or non-parametric methods. The nature of the data dictates the method…

[The research protocol VI: How to choose the appropriate statistical test. Inferential statistics].

PubMed

Flores-Ruiz, Eric; Miranda-Novales, María Guadalupe; Villasís-Keever, Miguel Ángel

2017-01-01

The statistical analysis can be divided in two main components: descriptive analysis and inferential analysis. An inference is to elaborate conclusions from the tests performed with the data obtained from a sample of a population. Statistical tests are used in order to establish the probability that a conclusion obtained from a sample is applicable to the population from which it was obtained. However, choosing the appropriate statistical test in general poses a challenge for novice researchers. To choose the statistical test it is necessary to take into account three aspects: the research design, the number of measurements and the scale of measurement of the variables. Statistical tests are divided into two sets, parametric and nonparametric. Parametric tests can only be used if the data show a normal distribution. Choosing the right statistical test will make it easier for readers to understand and apply the results.

Biological Parametric Mapping: A Statistical Toolbox for Multi-Modality Brain Image Analysis

PubMed Central

Casanova, Ramon; Ryali, Srikanth; Baer, Aaron; Laurienti, Paul J.; Burdette, Jonathan H.; Hayasaka, Satoru; Flowers, Lynn; Wood, Frank; Maldjian, Joseph A.

2006-01-01

In recent years multiple brain MR imaging modalities have emerged; however, analysis methodologies have mainly remained modality specific. In addition, when comparing across imaging modalities, most researchers have been forced to rely on simple region-of-interest type analyses, which do not allow the voxel-by-voxel comparisons necessary to answer more sophisticated neuroscience questions. To overcome these limitations, we developed a toolbox for multimodal image analysis called biological parametric mapping (BPM), based on a voxel-wise use of the general linear model. The BPM toolbox incorporates information obtained from other modalities as regressors in a voxel-wise analysis, thereby permitting investigation of more sophisticated hypotheses. The BPM toolbox has been developed in MATLAB with a user friendly interface for performing analyses, including voxel-wise multimodal correlation, ANCOVA, and multiple regression. It has a high degree of integration with the SPM (statistical parametric mapping) software relying on it for visualization and statistical inference. Furthermore, statistical inference for a correlation field, rather than a widely-used T-field, has been implemented in the correlation analysis for more accurate results. An example with in-vivo data is presented demonstrating the potential of the BPM methodology as a tool for multimodal image analysis. PMID:17070709

Small-window parametric imaging based on information entropy for ultrasound tissue characterization

PubMed Central

Tsui, Po-Hsiang; Chen, Chin-Kuo; Kuo, Wen-Hung; Chang, King-Jen; Fang, Jui; Ma, Hsiang-Yang; Chou, Dean

2017-01-01

Constructing ultrasound statistical parametric images by using a sliding window is a widely adopted strategy for characterizing tissues. Deficiency in spatial resolution, the appearance of boundary artifacts, and the prerequisite data distribution limit the practicability of statistical parametric imaging. In this study, small-window entropy parametric imaging was proposed to overcome the above problems. Simulations and measurements of phantoms were executed to acquire backscattered radiofrequency (RF) signals, which were processed to explore the feasibility of small-window entropy imaging in detecting scatterer properties. To validate the ability of entropy imaging in tissue characterization, measurements of benign and malignant breast tumors were conducted (n = 63) to compare performances of conventional statistical parametric (based on Nakagami distribution) and entropy imaging by the receiver operating characteristic (ROC) curve analysis. The simulation and phantom results revealed that entropy images constructed using a small sliding window (side length = 1 pulse length) adequately describe changes in scatterer properties. The area under the ROC for using small-window entropy imaging to classify tumors was 0.89, which was higher than 0.79 obtained using statistical parametric imaging. In particular, boundary artifacts were largely suppressed in the proposed imaging technique. Entropy enables using a small window for implementing ultrasound parametric imaging. PMID:28106118

Small-window parametric imaging based on information entropy for ultrasound tissue characterization

NASA Astrophysics Data System (ADS)

Tsui, Po-Hsiang; Chen, Chin-Kuo; Kuo, Wen-Hung; Chang, King-Jen; Fang, Jui; Ma, Hsiang-Yang; Chou, Dean

2017-01-01

Constructing ultrasound statistical parametric images by using a sliding window is a widely adopted strategy for characterizing tissues. Deficiency in spatial resolution, the appearance of boundary artifacts, and the prerequisite data distribution limit the practicability of statistical parametric imaging. In this study, small-window entropy parametric imaging was proposed to overcome the above problems. Simulations and measurements of phantoms were executed to acquire backscattered radiofrequency (RF) signals, which were processed to explore the feasibility of small-window entropy imaging in detecting scatterer properties. To validate the ability of entropy imaging in tissue characterization, measurements of benign and malignant breast tumors were conducted (n = 63) to compare performances of conventional statistical parametric (based on Nakagami distribution) and entropy imaging by the receiver operating characteristic (ROC) curve analysis. The simulation and phantom results revealed that entropy images constructed using a small sliding window (side length = 1 pulse length) adequately describe changes in scatterer properties. The area under the ROC for using small-window entropy imaging to classify tumors was 0.89, which was higher than 0.79 obtained using statistical parametric imaging. In particular, boundary artifacts were largely suppressed in the proposed imaging technique. Entropy enables using a small window for implementing ultrasound parametric imaging.

Outcome of temporal lobe epilepsy surgery predicted by statistical parametric PET imaging.

PubMed

Wong, C Y; Geller, E B; Chen, E Q; MacIntyre, W J; Morris, H H; Raja, S; Saha, G B; Lüders, H O; Cook, S A; Go, R T

1996-07-01

PET is useful in the presurgical evaluation of temporal lobe epilepsy. The purpose of this retrospective study is to assess the clinical use of statistical parametric imaging in predicting surgical outcome. Interictal 18FDG-PET scans in 17 patients with surgically-treated temporal lobe epilepsy (Group A-13 seizure-free, group B = 4 not seizure-free at 6 mo) were transformed into statistical parametric imaging, with each pixel representing a z-score value by using the mean and s.d. of count distribution in each individual patient, for both visual and quantitative analysis. Mean z-scores were significantly more negative in anterolateral (AL) and mesial (M) regions on the operated side than the nonoperated side in group A (AL: p < 0.00005, M: p = 0.0097), but not in group B (AL: p = 0.46, M: p = 0.08). Statistical parametric imaging correctly lateralized 16 out of 17 patients. Only the AL region, however, was significant in predicting surgical outcome (F = 29.03, p < 0.00005). Using a cut-off z-score value of -1.5, statistical parametric imaging correctly classified 92% of temporal lobes from group A and 88% of those from Group B. The preliminary results indicate that statistical parametric imaging provides both clinically useful information for lateralization in temporal lobe epilepsy and a reliable predictive indicator of clinical outcome following surgical treatment.

A global goodness-of-fit test for receiver operating characteristic curve analysis via the bootstrap method.

PubMed

Zou, Kelly H; Resnic, Frederic S; Talos, Ion-Florin; Goldberg-Zimring, Daniel; Bhagwat, Jui G; Haker, Steven J; Kikinis, Ron; Jolesz, Ferenc A; Ohno-Machado, Lucila

2005-10-01

Medical classification accuracy studies often yield continuous data based on predictive models for treatment outcomes. A popular method for evaluating the performance of diagnostic tests is the receiver operating characteristic (ROC) curve analysis. The main objective was to develop a global statistical hypothesis test for assessing the goodness-of-fit (GOF) for parametric ROC curves via the bootstrap. A simple log (or logit) and a more flexible Box-Cox normality transformations were applied to untransformed or transformed data from two clinical studies to predict complications following percutaneous coronary interventions (PCIs) and for image-guided neurosurgical resection results predicted by tumor volume, respectively. We compared a non-parametric with a parametric binormal estimate of the underlying ROC curve. To construct such a GOF test, we used the non-parametric and parametric areas under the curve (AUCs) as the metrics, with a resulting p value reported. In the interventional cardiology example, logit and Box-Cox transformations of the predictive probabilities led to satisfactory AUCs (AUC=0.888; p=0.78, and AUC=0.888; p=0.73, respectively), while in the brain tumor resection example, log and Box-Cox transformations of the tumor size also led to satisfactory AUCs (AUC=0.898; p=0.61, and AUC=0.899; p=0.42, respectively). In contrast, significant departures from GOF were observed without applying any transformation prior to assuming a binormal model (AUC=0.766; p=0.004, and AUC=0.831; p=0.03), respectively. In both studies the p values suggested that transformations were important to consider before applying any binormal model to estimate the AUC. Our analyses also demonstrated and confirmed the predictive values of different classifiers for determining the interventional complications following PCIs and resection outcomes in image-guided neurosurgery.

Analysis of censored data.

PubMed

Lucijanic, Marko; Petrovecki, Mladen

2012-01-01

Analyzing events over time is often complicated by incomplete, or censored, observations. Special non-parametric statistical methods were developed to overcome difficulties in summarizing and comparing censored data. Life-table (actuarial) method and Kaplan-Meier method are described with an explanation of survival curves. For the didactic purpose authors prepared a workbook based on most widely used Kaplan-Meier method. It should help the reader understand how Kaplan-Meier method is conceptualized and how it can be used to obtain statistics and survival curves needed to completely describe a sample of patients. Log-rank test and hazard ratio are also discussed.

SPM analysis of parametric (R)-[11C]PK11195 binding images: plasma input versus reference tissue parametric methods.

PubMed

Schuitemaker, Alie; van Berckel, Bart N M; Kropholler, Marc A; Veltman, Dick J; Scheltens, Philip; Jonker, Cees; Lammertsma, Adriaan A; Boellaard, Ronald

2007-05-01

(R)-[11C]PK11195 has been used for quantifying cerebral microglial activation in vivo. In previous studies, both plasma input and reference tissue methods have been used, usually in combination with a region of interest (ROI) approach. Definition of ROIs, however, can be labourious and prone to interobserver variation. In addition, results are only obtained for predefined areas and (unexpected) signals in undefined areas may be missed. On the other hand, standard pharmacokinetic models are too sensitive to noise to calculate (R)-[11C]PK11195 binding on a voxel-by-voxel basis. Linearised versions of both plasma input and reference tissue models have been described, and these are more suitable for parametric imaging. The purpose of this study was to compare the performance of these plasma input and reference tissue parametric methods on the outcome of statistical parametric mapping (SPM) analysis of (R)-[11C]PK11195 binding. Dynamic (R)-[11C]PK11195 PET scans with arterial blood sampling were performed in 7 younger and 11 elderly healthy subjects. Parametric images of volume of distribution (Vd) and binding potential (BP) were generated using linearised versions of plasma input (Logan) and reference tissue (Reference Parametric Mapping) models. Images were compared at the group level using SPM with a two-sample t-test per voxel, both with and without proportional scaling. Parametric BP images without scaling provided the most sensitive framework for determining differences in (R)-[11C]PK11195 binding between younger and elderly subjects. Vd images could only demonstrate differences in (R)-[11C]PK11195 binding when analysed with proportional scaling due to intersubject variation in K1/k2 (blood-brain barrier transport and non-specific binding).

Non-classical Signature of Parametric Fluorescence and its Application in Metrology

NASA Astrophysics Data System (ADS)

Hamar, M.; Michálek, V.; Pathak, A.

2014-08-01

The article provides a short theoretical background of what the non-classical light means. We applied the criterion for the existence of non-classical effects derived by C.T. Lee on parametric fluorescence. The criterion was originally derived for the study of two light beams with one mode per beam. We checked if the criterion is still working for two multimode beams of parametric down-conversion through numerical simulations. The theoretical results were tested by measurement of photon number statistics of twin beams emitted by nonlinear BBO crystal pumped by intense femtoseconds UV pulse. We used ICCD camera as the detector of photons in both beams. It appears that the criterion can be used for the measurement of the quantum efficiencies of the ICCD cameras.

[Diversity and frequency of scientific research design and statistical methods in the "Arquivos Brasileiros de Oftalmologia": a systematic review of the "Arquivos Brasileiros de Oftalmologia"--1993-2002].

PubMed

Crosta, Fernando; Nishiwaki-Dantas, Maria Cristina; Silvino, Wilmar; Dantas, Paulo Elias Correa

2005-01-01

To verify the frequency of study design, applied statistical analysis and approval by institutional review offices (Ethics Committee) of articles published in the "Arquivos Brasileiros de Oftalmologia" during a 10-year interval, with later comparative and critical analysis by some of the main international journals in the field of Ophthalmology. Systematic review without metanalysis was performed. Scientific papers published in the "Arquivos Brasileiros de Oftalmologia" between January 1993 and December 2002 were reviewed by two independent reviewers and classified according to the applied study design, statistical analysis and approval by the institutional review offices. To categorize those variables, a descriptive statistical analysis was used. After applying inclusion and exclusion criteria, 584 articles for evaluation of statistical analysis and, 725 articles for evaluation of study design were reviewed. Contingency table (23.10%) was the most frequently applied statistical method, followed by non-parametric tests (18.19%), Student's t test (12.65%), central tendency measures (10.60%) and analysis of variance (9.81%). Of 584 reviewed articles, 291 (49.82%) presented no statistical analysis. Observational case series (26.48%) was the most frequently used type of study design, followed by interventional case series (18.48%), observational case description (13.37%), non-random clinical study (8.96%) and experimental study (8.55%). We found a higher frequency of observational clinical studies, lack of statistical analysis in almost half of the published papers. Increase in studies with approval by institutional review Ethics Committee was noted since it became mandatory in 1996.

Non-parametric methods for cost-effectiveness analysis: the central limit theorem and the bootstrap compared.

PubMed

Nixon, Richard M; Wonderling, David; Grieve, Richard D

2010-03-01

Cost-effectiveness analyses (CEA) alongside randomised controlled trials commonly estimate incremental net benefits (INB), with 95% confidence intervals, and compute cost-effectiveness acceptability curves and confidence ellipses. Two alternative non-parametric methods for estimating INB are to apply the central limit theorem (CLT) or to use the non-parametric bootstrap method, although it is unclear which method is preferable. This paper describes the statistical rationale underlying each of these methods and illustrates their application with a trial-based CEA. It compares the sampling uncertainty from using either technique in a Monte Carlo simulation. The experiments are repeated varying the sample size and the skewness of costs in the population. The results showed that, even when data were highly skewed, both methods accurately estimated the true standard errors (SEs) when sample sizes were moderate to large (n>50), and also gave good estimates for small data sets with low skewness. However, when sample sizes were relatively small and the data highly skewed, using the CLT rather than the bootstrap led to slightly more accurate SEs. We conclude that while in general using either method is appropriate, the CLT is easier to implement, and provides SEs that are at least as accurate as the bootstrap. (c) 2009 John Wiley & Sons, Ltd.

Variable selection in a flexible parametric mixture cure model with interval-censored data.

PubMed

Scolas, Sylvie; El Ghouch, Anouar; Legrand, Catherine; Oulhaj, Abderrahim

2016-03-30

In standard survival analysis, it is generally assumed that every individual will experience someday the event of interest. However, this is not always the case, as some individuals may not be susceptible to this event. Also, in medical studies, it is frequent that patients come to scheduled interviews and that the time to the event is only known to occur between two visits. That is, the data are interval-censored with a cure fraction. Variable selection in such a setting is of outstanding interest. Covariates impacting the survival are not necessarily the same as those impacting the probability to experience the event. The objective of this paper is to develop a parametric but flexible statistical model to analyze data that are interval-censored and include a fraction of cured individuals when the number of potential covariates may be large. We use the parametric mixture cure model with an accelerated failure time regression model for the survival, along with the extended generalized gamma for the error term. To overcome the issue of non-stable and non-continuous variable selection procedures, we extend the adaptive LASSO to our model. By means of simulation studies, we show good performance of our method and discuss the behavior of estimates with varying cure and censoring proportion. Lastly, our proposed method is illustrated with a real dataset studying the time until conversion to mild cognitive impairment, a possible precursor of Alzheimer's disease. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Linkage mapping of beta 2 EEG waves via non-parametric regression.

PubMed

Ghosh, Saurabh; Begleiter, Henri; Porjesz, Bernice; Chorlian, David B; Edenberg, Howard J; Foroud, Tatiana; Goate, Alison; Reich, Theodore

2003-04-01

Parametric linkage methods for analyzing quantitative trait loci are sensitive to violations in trait distributional assumptions. Non-parametric methods are relatively more robust. In this article, we modify the non-parametric regression procedure proposed by Ghosh and Majumder [2000: Am J Hum Genet 66:1046-1061] to map Beta 2 EEG waves using genome-wide data generated in the COGA project. Significant linkage findings are obtained on chromosomes 1, 4, 5, and 15 with findings at multiple regions on chromosomes 4 and 15. We analyze the data both with and without incorporating alcoholism as a covariate. We also test for epistatic interactions between regions of the genome exhibiting significant linkage with the EEG phenotypes and find evidence of epistatic interactions between a region each on chromosome 1 and chromosome 4 with one region on chromosome 15. While regressing out the effect of alcoholism does not affect the linkage findings, the epistatic interactions become statistically insignificant. Copyright 2003 Wiley-Liss, Inc.

[Linkage analysis of susceptibility loci in 2 target chromosomes in pedigrees with paranoid schizophrenia and undifferentiated schizophrenia].

PubMed

Zeng, Li-ping; Hu, Zheng-mao; Mu, Li-li; Mei, Gui-sen; Lu, Xiu-ling; Zheng, Yong-jun; Li, Pei-jian; Zhang, Ying-xue; Pan, Qian; Long, Zhi-gao; Dai, He-ping; Zhang, Zhuo-hua; Xia, Jia-hui; Zhao, Jing-ping; Xia, Kun

2011-06-01

To investigate the relationship of susceptibility loci in chromosomes 1q21-25 and 6p21-25 and schizophrenia subtypes in Chinese population. A genomic scan and parametric and non-parametric analyses were performed on 242 individuals from 36 schizophrenia pedigrees, including 19 paranoid schizophrenia and 17 undifferentiated schizophrenia pedigrees, from Henan province of China using 5 microsatellite markers in the chromosome region 1q21-25 and 8 microsatellite markers in the chromosome region 6p21-25, which were the candidates of previous studies. All affected subjects were diagnosed and typed according to the criteria of the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revised (DSM-IV-TR; American Psychiatric Association, 2000). All subjects signed informed consent. In chromosome 1, parametric analysis under the dominant inheritance mode of all 36 pedigrees showed that the maximum multi-point heterogeneity Log of odds score method (HLOD) score was 1.33 (α = 0.38). The non-parametric analysis and the single point and multi-point nonparametric linkage (NPL) scores suggested linkage at D1S484, D1S2878, and D1S196. In the 19 paranoid schizophrenias pedigrees, linkage was not observed for any of the 5 markers. In the 17 undifferentiated schizophrenia pedigrees, the multi-point NPL score was 1.60 (P= 0.0367) at D1S484. The single point NPL score was 1.95(P= 0.0145) and the multi-point NPL score was 2.39 (P= 0.0041) at D1S2878. Additionally, the multi-point NPL score was 1.74 (P= 0.0255) at D1S196. These same three loci showed suggestive linkage during the integrative analysis of all 36 pedigrees. In chromosome 6, parametric linkage analysis under the dominant and recessive inheritance and the non-parametric linkage analysis of all 36 pedigrees and the 17 undifferentiated schizophrenia pedigrees, linkage was not observed for any of the 8 markers. In the 19 paranoid schizophrenias pedigrees, parametric analysis showed that under recessive inheritance mode the maximum single-point HLOD score was 1.26 (α = 0.40) and the multi-point HLOD was 1.12 (α = 0.38) at D6S289 in the chromosome 6p23. In nonparametric analysis, the single-point NPL score was 1.52 (P= 0.0402) and the multi-point NPL score was 1.92 (P= 0.0206) at D6S289. Susceptibility genes correlated with undifferentiated schizophrenia pedigrees from D1S484, D1S2878, D1S196 loci, and those correlated with paranoid schizophrenia pedigrees from D6S289 locus are likely present in chromosome regions 1q23.3 and 1q24.2, and chromosome region 6p23, respectively.

A question of separation: disentangling tracer bias and gravitational non-linearity with counts-in-cells statistics

NASA Astrophysics Data System (ADS)

Uhlemann, C.; Feix, M.; Codis, S.; Pichon, C.; Bernardeau, F.; L'Huillier, B.; Kim, J.; Hong, S. E.; Laigle, C.; Park, C.; Shin, J.; Pogosyan, D.

2018-02-01

Starting from a very accurate model for density-in-cells statistics of dark matter based on large deviation theory, a bias model for the tracer density in spheres is formulated. It adopts a mean bias relation based on a quadratic bias model to relate the log-densities of dark matter to those of mass-weighted dark haloes in real and redshift space. The validity of the parametrized bias model is established using a parametrization-independent extraction of the bias function. This average bias model is then combined with the dark matter PDF, neglecting any scatter around it: it nevertheless yields an excellent model for densities-in-cells statistics of mass tracers that is parametrized in terms of the underlying dark matter variance and three bias parameters. The procedure is validated on measurements of both the one- and two-point statistics of subhalo densities in the state-of-the-art Horizon Run 4 simulation showing excellent agreement for measured dark matter variance and bias parameters. Finally, it is demonstrated that this formalism allows for a joint estimation of the non-linear dark matter variance and the bias parameters using solely the statistics of subhaloes. Having verified that galaxy counts in hydrodynamical simulations sampled on a scale of 10 Mpc h-1 closely resemble those of subhaloes, this work provides important steps towards making theoretical predictions for density-in-cells statistics applicable to upcoming galaxy surveys like Euclid or WFIRST.

Evaluating Cellular Polyfunctionality with a Novel Polyfunctionality Index

PubMed Central

Larsen, Martin; Sauce, Delphine; Arnaud, Laurent; Fastenackels, Solène; Appay, Victor; Gorochov, Guy

2012-01-01

Functional evaluation of naturally occurring or vaccination-induced T cell responses in mice, men and monkeys has in recent years advanced from single-parameter (e.g. IFN-γ-secretion) to much more complex multidimensional measurements. Co-secretion of multiple functional molecules (such as cytokines and chemokines) at the single-cell level is now measurable due primarily to major advances in multiparametric flow cytometry. The very extensive and complex datasets generated by this technology raise the demand for proper analytical tools that enable the analysis of combinatorial functional properties of T cells, hence polyfunctionality. Presently, multidimensional functional measures are analysed either by evaluating all combinations of parameters individually or by summing frequencies of combinations that include the same number of simultaneous functions. Often these evaluations are visualized as pie charts. Whereas pie charts effectively represent and compare average polyfunctionality profiles of particular T cell subsets or patient groups, they do not document the degree or variation of polyfunctionality within a group nor does it allow more sophisticated statistical analysis. Here we propose a novel polyfunctionality index that numerically evaluates the degree and variation of polyfuntionality, and enable comparative and correlative parametric and non-parametric statistical tests. Moreover, it allows the usage of more advanced statistical approaches, such as cluster analysis. We believe that the polyfunctionality index will render polyfunctionality an appropriate end-point measure in future studies of T cell responsiveness. PMID:22860124

Key statistical and analytical issues for evaluating treatment effects in periodontal research.

PubMed

Tu, Yu-Kang; Gilthorpe, Mark S

2012-06-01

Statistics is an indispensible tool for evaluating treatment effects in clinical research. Due to the complexities of periodontal disease progression and data collection, statistical analyses for periodontal research have been a great challenge for both clinicians and statisticians. The aim of this article is to provide an overview of several basic, but important, statistical issues related to the evaluation of treatment effects and to clarify some common statistical misconceptions. Some of these issues are general, concerning many disciplines, and some are unique to periodontal research. We first discuss several statistical concepts that have sometimes been overlooked or misunderstood by periodontal researchers. For instance, decisions about whether to use the t-test or analysis of covariance, or whether to use parametric tests such as the t-test or its non-parametric counterpart, the Mann-Whitney U-test, have perplexed many periodontal researchers. We also describe more advanced methodological issues that have sometimes been overlooked by researchers. For instance, the phenomenon of regression to the mean is a fundamental issue to be considered when evaluating treatment effects, and collinearity amongst covariates is a conundrum that must be resolved when explaining and predicting treatment effects. Quick and easy solutions to these methodological and analytical issues are not always available in the literature, and careful statistical thinking is paramount when conducting useful and meaningful research. © 2012 John Wiley & Sons A/S.

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.

Non-parametric model selection for subject-specific topological organization of resting-state functional connectivity.

PubMed

Ferrarini, Luca; Veer, Ilya M; van Lew, Baldur; Oei, Nicole Y L; van Buchem, Mark A; Reiber, Johan H C; Rombouts, Serge A R B; Milles, J

2011-06-01

In recent years, graph theory has been successfully applied to study functional and anatomical connectivity networks in the human brain. Most of these networks have shown small-world topological characteristics: high efficiency in long distance communication between nodes, combined with highly interconnected local clusters of nodes. Moreover, functional studies performed at high resolutions have presented convincing evidence that resting-state functional connectivity networks exhibits (exponentially truncated) scale-free behavior. Such evidence, however, was mostly presented qualitatively, in terms of linear regressions of the degree distributions on log-log plots. Even when quantitative measures were given, these were usually limited to the r(2) correlation coefficient. However, the r(2) statistic is not an optimal estimator of explained variance, when dealing with (truncated) power-law models. Recent developments in statistics have introduced new non-parametric approaches, based on the Kolmogorov-Smirnov test, for the problem of model selection. In this work, we have built on this idea to statistically tackle the issue of model selection for the degree distribution of functional connectivity at rest. The analysis, performed at voxel level and in a subject-specific fashion, confirmed the superiority of a truncated power-law model, showing high consistency across subjects. Moreover, the most highly connected voxels were found to be consistently part of the default mode network. Our results provide statistically sound support to the evidence previously presented in literature for a truncated power-law model of resting-state functional connectivity. Copyright © 2010 Elsevier Inc. All rights reserved.

Genetic Algorithm Based Framework for Automation of Stochastic Modeling of Multi-Season Streamflows

NASA Astrophysics Data System (ADS)

Srivastav, R. K.; Srinivasan, K.; Sudheer, K.

2009-05-01

Synthetic streamflow data generation involves the synthesis of likely streamflow patterns that are statistically indistinguishable from the observed streamflow data. The various kinds of stochastic models adopted for multi-season streamflow generation in hydrology are: i) parametric models which hypothesize the form of the periodic dependence structure and the distributional form a priori (examples are PAR, PARMA); disaggregation models that aim to preserve the correlation structure at the periodic level and the aggregated annual level; ii) Nonparametric models (examples are bootstrap/kernel based methods), which characterize the laws of chance, describing the stream flow process, without recourse to prior assumptions as to the form or structure of these laws; (k-nearest neighbor (k-NN), matched block bootstrap (MABB)); non-parametric disaggregation model. iii) Hybrid models which blend both parametric and non-parametric models advantageously to model the streamflows effectively. Despite many of these developments that have taken place in the field of stochastic modeling of streamflows over the last four decades, accurate prediction of the storage and the critical drought characteristics has been posing a persistent challenge to the stochastic modeler. This is partly because, usually, the stochastic streamflow model parameters are estimated by minimizing a statistically based objective function (such as maximum likelihood (MLE) or least squares (LS) estimation) and subsequently the efficacy of the models is being validated based on the accuracy of prediction of the estimates of the water-use characteristics, which requires large number of trial simulations and inspection of many plots and tables. Still accurate prediction of the storage and the critical drought characteristics may not be ensured. In this study a multi-objective optimization framework is proposed to find the optimal hybrid model (blend of a simple parametric model, PAR(1) model and matched block bootstrap (MABB) ) based on the explicit objective functions of minimizing the relative bias and relative root mean square error in estimating the storage capacity of the reservoir. The optimal parameter set of the hybrid model is obtained based on the search over a multi- dimensional parameter space (involving simultaneous exploration of the parametric (PAR(1)) as well as the non-parametric (MABB) components). This is achieved using the efficient evolutionary search based optimization tool namely, non-dominated sorting genetic algorithm - II (NSGA-II). This approach helps in reducing the drudgery involved in the process of manual selection of the hybrid model, in addition to predicting the basic summary statistics dependence structure, marginal distribution and water-use characteristics accurately. The proposed optimization framework is used to model the multi-season streamflows of River Beaver and River Weber of USA. In case of both the rivers, the proposed GA-based hybrid model yields a much better prediction of the storage capacity (where simultaneous exploration of both parametric and non-parametric components is done) when compared with the MLE-based hybrid models (where the hybrid model selection is done in two stages, thus probably resulting in a sub-optimal model). This framework can be further extended to include different linear/non-linear hybrid stochastic models at other temporal and spatial scales as well.

Astrophysical data analysis with information field theory

NASA Astrophysics Data System (ADS)

Enßlin, Torsten

2014-12-01

Non-parametric imaging and data analysis in astrophysics and cosmology can be addressed by information field theory (IFT), a means of Bayesian, data based inference on spatially distributed signal fields. IFT is a statistical field theory, which permits the construction of optimal signal recovery algorithms. It exploits spatial correlations of the signal fields even for nonlinear and non-Gaussian signal inference problems. The alleviation of a perception threshold for recovering signals of unknown correlation structure by using IFT will be discussed in particular as well as a novel improvement on instrumental self-calibration schemes. IFT can be applied to many areas. Here, applications in in cosmology (cosmic microwave background, large-scale structure) and astrophysics (galactic magnetism, radio interferometry) are presented.

Neural network representation and learning of mappings and their derivatives

NASA Technical Reports Server (NTRS)

White, Halbert; Hornik, Kurt; Stinchcombe, Maxwell; Gallant, A. Ronald

1991-01-01

Discussed here are recent theorems proving that artificial neural networks are capable of approximating an arbitrary mapping and its derivatives as accurately as desired. This fact forms the basis for further results establishing the learnability of the desired approximations, using results from non-parametric statistics. These results have potential applications in robotics, chaotic dynamics, control, and sensitivity analysis. An example involving learning the transfer function and its derivatives for a chaotic map is discussed.

[Detection of quadratic phase coupling between EEG signal components by nonparamatric and parametric methods of bispectral analysis].

PubMed

Schmidt, K; Witte, H

1999-11-01

Recently the assumption of the independence of individual frequency components in a signal has been rejected, for example, for the EEG during defined physiological states such as sleep or sedation [9, 10]. Thus, the use of higher-order spectral analysis capable of detecting interrelations between individual signal components has proved useful. The aim of the present study was to investigate the quality of various non-parametric and parametric estimation algorithms using simulated as well as true physiological data. We employed standard algorithms available for the MATLAB. The results clearly show that parametric bispectral estimation is superior to non-parametric estimation in terms of the quality of peak localisation and the discrimination from other peaks.

The use of analysis of variance procedures in biological studies

USGS Publications Warehouse

Williams, B.K.

1987-01-01

The analysis of variance (ANOVA) is widely used in biological studies, yet there remains considerable confusion among researchers about the interpretation of hypotheses being tested. Ambiguities arise when statistical designs are unbalanced, and in particular when not all combinations of design factors are represented in the data. This paper clarifies the relationship among hypothesis testing, statistical modelling and computing procedures in ANOVA for unbalanced data. A simple two-factor fixed effects design is used to illustrate three common parametrizations for ANOVA models, and some associations among these parametrizations are developed. Biologically meaningful hypotheses for main effects and interactions are given in terms of each parametrization, and procedures for testing the hypotheses are described. The standard statistical computing procedures in ANOVA are given along with their corresponding hypotheses. Throughout the development unbalanced designs are assumed and attention is given to problems that arise with missing cells.

A Semi-parametric Transformation Frailty Model for Semi-competing Risks Survival Data

PubMed Central

Jiang, Fei; Haneuse, Sebastien

2016-01-01

In the analysis of semi-competing risks data interest lies in estimation and inference with respect to a so-called non-terminal event, the observation of which is subject to a terminal event. Multi-state models are commonly used to analyse such data, with covariate effects on the transition/intensity functions typically specified via the Cox model and dependence between the non-terminal and terminal events specified, in part, by a unit-specific shared frailty term. To ensure identifiability, the frailties are typically assumed to arise from a parametric distribution, specifically a Gamma distribution with mean 1.0 and variance, say, σ2. When the frailty distribution is misspecified, however, the resulting estimator is not guaranteed to be consistent, with the extent of asymptotic bias depending on the discrepancy between the assumed and true frailty distributions. In this paper, we propose a novel class of transformation models for semi-competing risks analysis that permit the non-parametric specification of the frailty distribution. To ensure identifiability, the class restricts to parametric specifications of the transformation and the error distribution; the latter are flexible, however, and cover a broad range of possible specifications. We also derive the semi-parametric efficient score under the complete data setting and propose a non-parametric score imputation method to handle right censoring; consistency and asymptotic normality of the resulting estimators is derived and small-sample operating characteristics evaluated via simulation. Although the proposed semi-parametric transformation model and non-parametric score imputation method are motivated by the analysis of semi-competing risks data, they are broadly applicable to any analysis of multivariate time-to-event outcomes in which a unit-specific shared frailty is used to account for correlation. Finally, the proposed model and estimation procedures are applied to a study of hospital readmission among patients diagnosed with pancreatic cancer. PMID:28439147

Immunity to Salmonella typhi: considerations relevant to measurement of cellular immunity in typhoid-endemic regions.

PubMed Central

Murphy, J R; Wasserman, S S; Baqar, S; Schlesinger, L; Ferreccio, C; Lindberg, A A; Levine, M M

1989-01-01

Experiments were performed in Baltimore, Maryland and in Santiago, Chile, to determine the level of Salmonella typhi antigen-driven in vitro lymphocyte replication response which signifies specific acquired immunity to this bacterium and to determine the best method of data analysis and form of data presentation. Lymphocyte replication was measured as incorporation of 3H-thymidine into desoxyribonucleic acid. Data (ct/min/culture) were analyzed in raw form and following log transformation, by non-parametric and parametric statistical procedures. A preference was developed for log-transformed data and discriminant analysis. Discriminant analysis of log-transformed data revealed 3H-thymidine incorporation rates greater than 3,433 for particulate S. typhi, Ty2 antigen stimulated cultures signified acquired immunity at a sensitivity and specificity of 82.7; for soluble S. typhi O polysaccharide antigen-stimulated cultures, ct/min/culture values of greater than 1,237 signified immunity (sensitivity and specificity 70.5%). PMID:2702777

The Dundee Ready Education Environment Measure (DREEM): a review of its adoption and use.

PubMed

Miles, Susan; Swift, Louise; Leinster, Sam J

2012-01-01

The Dundee Ready Education Environment Measure (DREEM) was published in 1997 as a tool to evaluate educational environments of medical schools and other health training settings and a recent review concluded that it was the most suitable such instrument. This study aimed to review the settings and purposes to which the DREEM has been applied and the approaches used to analyse and report it, with a view to guiding future users towards appropriate methodology. A systematic literature review was conducted using the Web of Knowledge databases of all articles reporting DREEM data between 1997 and 4 January 2011. The review found 40 publications, using data from 20 countries. DREEM is used in evaluation for diagnostic purposes, comparison between different groups and comparison with ideal/expected scores. A variety of non-parametric and parametric statistical methods have been applied, but their use is inconsistent. DREEM has been used internationally for different purposes and is regarded as a useful tool by users. However, reporting and analysis differs between publications. This lack of uniformity makes comparison between institutions difficult. Most users of DREEM are not statisticians and there is a need for informed guidelines on its reporting and statistical analysis.

Evaluation of standardized and applied variables in predicting treatment outcomes of polytrauma patients.

PubMed

Aksamija, Goran; Mulabdic, Adi; Rasic, Ismar; Muhovic, Samir; Gavric, Igor

2011-01-01

Polytrauma is defined as an injury where they are affected by at least two different organ systems or body, with at least one life-threatening injuries. Given the multilevel model care of polytrauma patients within KCUS are inevitable weaknesses in the management of this category of patients. To determine the dynamics of existing procedures in treatment of polytrauma patients on admission to KCUS, and based on statistical analysis of variables applied to determine and define the factors that influence the final outcome of treatment, and determine their mutual relationship, which may result in eliminating the flaws in the approach to the problem. The study was based on 263 polytrauma patients. Parametric and non-parametric statistical methods were used. Basic statistics were calculated, based on the calculated parameters for the final achievement of research objectives, multicoleration analysis, image analysis, discriminant analysis and multifactorial analysis were used. From the universe of variables for this study we selected sample of n = 25 variables, of which the first two modular, others belong to the common measurement space (n = 23) and in this paper defined as a system variable methods, procedures and assessments of polytrauma patients. After the multicoleration analysis, since the image analysis gave a reliable measurement results, we started the analysis of eigenvalues, that is defining the factors upon which they obtain information about the system solve the problem of the existing model and its correlation with treatment outcome. The study singled out the essential factors that determine the current organizational model of care, which may affect the treatment and better outcome of polytrauma patients. This analysis has shown the maximum correlative relationships between these practices and contributed to development guidelines that are defined by isolated factors.

Gray matter abnormalities of the dorsal posterior cingulate in sleep walking.

PubMed

Heidbreder, Anna; Stefani, Ambra; Brandauer, Elisabeth; Steiger, Ruth; Kremser, Christian; Gizewski, Elke R; Young, Peter; Poewe, Werner; Högl, Birgit; Scherfler, Christoph

2017-08-01

This study aimed to determine whether voxel-based analysis of T1 weighted magnetic resonance imaging (MRI) and diffusion tensor imaging is able to detect alterations of gray and white matter morphometry as well as measures of mean diffusivity and fractional anisotropy in patients with non-rapid eye movement parasomnia. 3 Tesla MRI was performed in 14 drug-free, polysomnography-confirmed adult patients with non-rapid eye movement parasomnia (age: 29 ± 4.2 years; disease duration 19.2 ± 7.7 years) and 14 healthy subjects, matched for age and gender. Statistical parametric mapping was applied to objectively identify focal changes of MRI parameters throughout the entire brain volume. Statistical parametric mapping localized significant decreases of gray matter volume in the left dorsal posterior cingulate cortex (BA23) and posterior midcingulate cortex (BA24) in patients with non-rapid eye movement parasomnias compared to the control group (p < 0.001, corrected for multiple comparisons). No significant differences of mean diffusivity and fractional anisotropy measures were found between the non-rapid eye movement parasomnia group and the healthy control group. Recently, the simultaneous co-existence of arousal or wakefulness originating from the motor and cingulate cortices and persistent sleep in associative cortical regions was suggested as a functional framework of somnambulism. Gray matter volume decline in the dorsal posterior and posterior midcingulate cortex reported in this study might represent the neuroanatomical substrate for this condition. Copyright © 2017 Elsevier B.V. All rights reserved.

Supratentorial lesions contribute to trigeminal neuralgia in multiple sclerosis.

PubMed

Fröhlich, Kilian; Winder, Klemens; Linker, Ralf A; Engelhorn, Tobias; Dörfler, Arnd; Lee, De-Hyung; Hilz, Max J; Schwab, Stefan; Seifert, Frank

2018-06-01

Background It has been proposed that multiple sclerosis lesions afflicting the pontine trigeminal afferents contribute to trigeminal neuralgia in multiple sclerosis. So far, there are no imaging studies that have evaluated interactions between supratentorial lesions and trigeminal neuralgia in multiple sclerosis patients. Methods We conducted a retrospective study and sought multiple sclerosis patients with trigeminal neuralgia and controls in a local database. Multiple sclerosis lesions were manually outlined and transformed into stereotaxic space. We determined the lesion overlap and performed a voxel-wise subtraction analysis. Secondly, we conducted a voxel-wise non-parametric analysis using the Liebermeister test. Results From 12,210 multiple sclerosis patient records screened, we identified 41 patients with trigeminal neuralgia. The voxel-wise subtraction analysis yielded associations between trigeminal neuralgia and multiple sclerosis lesions in the pontine trigeminal afferents, as well as larger supratentorial lesion clusters in the contralateral insula and hippocampus. The non-parametric statistical analysis using the Liebermeister test yielded similar areas to be associated with multiple sclerosis-related trigeminal neuralgia. Conclusions Our study confirms previous data on associations between multiple sclerosis-related trigeminal neuralgia and pontine lesions, and showed for the first time an association with lesions in the insular region, a region involved in pain processing and endogenous pain modulation.

Guidelines for the design and statistical analysis of experiments in papers submitted to ATLA.

PubMed

Festing, M F

2001-01-01

In vitro experiments need to be well designed and correctly analysed if they are to achieve their full potential to replace the use of animals in research. An "experiment" is a procedure for collecting scientific data in order to answer a hypothesis, or to provide material for generating new hypotheses, and differs from a survey because the scientist has control over the treatments that can be applied. Most experiments can be classified into one of a few formal designs, the most common being completely randomised, and randomised block designs. These are quite common with in vitro experiments, which are often replicated in time. Some experiments involve a single independent (treatment) variable, while other "factorial" designs simultaneously vary two or more independent variables, such as drug treatment and cell line. Factorial designs often provide additional information at little extra cost. Experiments need to be carefully planned to avoid bias, be powerful yet simple, provide for a valid statistical analysis and, in some cases, have a wide range of applicability. Virtually all experiments need some sort of statistical analysis in order to take account of biological variation among the experimental subjects. Parametric methods using the t test or analysis of variance are usually more powerful than non-parametric methods, provided the underlying assumptions of normality of the residuals and equal variances are approximately valid. The statistical analyses of data from a completely randomised design, and from a randomised-block design are demonstrated in Appendices 1 and 2, and methods of determining sample size are discussed in Appendix 3. Appendix 4 gives a checklist for authors submitting papers to ATLA.

Research design and statistical methods in Pakistan Journal of Medical Sciences (PJMS).

PubMed

Akhtar, Sohail; Shah, Syed Wadood Ali; Rafiq, M; Khan, Ajmal

2016-01-01

This article compares the study design and statistical methods used in 2005, 2010 and 2015 of Pakistan Journal of Medical Sciences (PJMS). Only original articles of PJMS were considered for the analysis. The articles were carefully reviewed for statistical methods and designs, and then recorded accordingly. The frequency of each statistical method and research design was estimated and compared with previous years. A total of 429 articles were evaluated (n=74 in 2005, n=179 in 2010, n=176 in 2015) in which 171 (40%) were cross-sectional and 116 (27%) were prospective study designs. A verity of statistical methods were found in the analysis. The most frequent methods include: descriptive statistics (n=315, 73.4%), chi-square/Fisher's exact tests (n=205, 47.8%) and student t-test (n=186, 43.4%). There was a significant increase in the use of statistical methods over time period: t-test, chi-square/Fisher's exact test, logistic regression, epidemiological statistics, and non-parametric tests. This study shows that a diverse variety of statistical methods have been used in the research articles of PJMS and frequency improved from 2005 to 2015. However, descriptive statistics was the most frequent method of statistical analysis in the published articles while cross-sectional study design was common study design.

Simulation of parametric model towards the fixed covariate of right censored lung cancer data

NASA Astrophysics Data System (ADS)

Afiqah Muhamad Jamil, Siti; Asrul Affendi Abdullah, M.; Kek, Sie Long; Ridwan Olaniran, Oyebayo; Enera Amran, Syahila

2017-09-01

In this study, simulation procedure was applied to measure the fixed covariate of right censored data by using parametric survival model. The scale and shape parameter were modified to differentiate the analysis of parametric regression survival model. Statistically, the biases, mean biases and the coverage probability were used in this analysis. Consequently, different sample sizes were employed to distinguish the impact of parametric regression model towards right censored data with 50, 100, 150 and 200 number of sample. R-statistical software was utilised to develop the coding simulation with right censored data. Besides, the final model of right censored simulation was compared with the right censored lung cancer data in Malaysia. It was found that different values of shape and scale parameter with different sample size, help to improve the simulation strategy for right censored data and Weibull regression survival model is suitable fit towards the simulation of survival of lung cancer patients data in Malaysia.

Generalized Correlation Coefficient for Non-Parametric Analysis of Microarray Time-Course Data.

PubMed

Tan, Qihua; Thomassen, Mads; Burton, Mark; Mose, Kristian Fredløv; Andersen, Klaus Ejner; Hjelmborg, Jacob; Kruse, Torben

2017-06-06

Modeling complex time-course patterns is a challenging issue in microarray study due to complex gene expression patterns in response to the time-course experiment. We introduce the generalized correlation coefficient and propose a combinatory approach for detecting, testing and clustering the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray time-course data and for exploring the complex relationships in the omics data for studying their association with disease and health.

Parametric resonance in the early Universe—a fitting analysis

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

Figueroa, Daniel G.; Torrentí, Francisco, E-mail: daniel.figueroa@cern.ch, E-mail: f.torrenti@csic.es

Particle production via parametric resonance in the early Universe, is a non-perturbative, non-linear and out-of-equilibrium phenomenon. Although it is a well studied topic, whenever a new scenario exhibits parametric resonance, a full re-analysis is normally required. To avoid this tedious task, many works present often only a simplified linear treatment of the problem. In order to surpass this circumstance in the future, we provide a fitting analysis of parametric resonance through all its relevant stages: initial linear growth, non-linear evolution, and relaxation towards equilibrium. Using lattice simulations in an expanding grid in 3+1 dimensions, we parametrize the dynamics' outcome scanningmore » over the relevant ingredients: role of the oscillatory field, particle coupling strength, initial conditions, and background expansion rate. We emphasize the inaccuracy of the linear calculation of the decay time of the oscillatory field, and propose a more appropriate definition of this scale based on the subsequent non-linear dynamics. We provide simple fits to the relevant time scales and particle energy fractions at each stage. Our fits can be applied to post-inflationary preheating scenarios, where the oscillatory field is the inflaton, or to spectator-field scenarios, where the oscillatory field can be e.g. a curvaton, or the Standard Model Higgs.« less

Detection of semi-volatile organic compounds in permeable ...

EPA Pesticide Factsheets

Abstract The Edison Environmental Center (EEC) has a research and demonstration permeable parking lot comprised of three different permeable systems: permeable asphalt, porous concrete and interlocking concrete permeable pavers. Water quality and quantity analysis has been ongoing since January, 2010. This paper describes a subset of the water quality analysis, analysis of semivolatile organic compounds (SVOCs) to determine if hydrocarbons were in water infiltrated through the permeable surfaces. SVOCs were analyzed in samples collected from 11 dates over a 3 year period, from 2/8/2010 to 4/1/2013.Results are broadly divided into three categories: 42 chemicals were never detected; 12 chemicals (11 chemical test) were detected at a rate of less than 10% or less; and 22 chemicals were detected at a frequency of 10% or greater (ranging from 10% to 66.5% detections). Fundamental and exploratory statistical analyses were performed on these latter analyses results by grouping results by surface type. The statistical analyses were limited due to low frequency of detections and dilutions of samples which impacted detection limits. The infiltrate data through three permeable surfaces were analyzed as non-parametric data by the Kaplan-Meier estimation method for fundamental statistics; there were some statistically observable difference in concentration between pavement types when using Tarone-Ware Comparison Hypothesis Test. Additionally Spearman Rank order non-parame

Rank-based permutation approaches for non-parametric factorial designs.

PubMed

Umlauft, Maria; Konietschke, Frank; Pauly, Markus

2017-11-01

Inference methods for null hypotheses formulated in terms of distribution functions in general non-parametric factorial designs are studied. The methods can be applied to continuous, ordinal or even ordered categorical data in a unified way, and are based only on ranks. In this set-up Wald-type statistics and ANOVA-type statistics are the current state of the art. The first method is asymptotically exact but a rather liberal statistical testing procedure for small to moderate sample size, while the latter is only an approximation which does not possess the correct asymptotic α level under the null. To bridge these gaps, a novel permutation approach is proposed which can be seen as a flexible generalization of the Kruskal-Wallis test to all kinds of factorial designs with independent observations. It is proven that the permutation principle is asymptotically correct while keeping its finite exactness property when data are exchangeable. The results of extensive simulation studies foster these theoretical findings. A real data set exemplifies its applicability. © 2017 The British Psychological Society.

Guided SAR image despeckling with probabilistic non local weights

NASA Astrophysics Data System (ADS)

Gokul, Jithin; Nair, Madhu S.; Rajan, Jeny

2017-12-01

SAR images are generally corrupted by granular disturbances called speckle, which makes visual analysis and detail extraction a difficult task. Non Local despeckling techniques with probabilistic similarity has been a recent trend in SAR despeckling. To achieve effective speckle suppression without compromising detail preservation, we propose an improvement for the existing Generalized Guided Filter with Bayesian Non-Local Means (GGF-BNLM) method. The proposed method (Guided SAR Image Despeckling with Probabilistic Non Local Weights) replaces parametric constants based on heuristics in GGF-BNLM method with dynamically derived values based on the image statistics for weight computation. Proposed changes make GGF-BNLM method adaptive and as a result, significant improvement is achieved in terms of performance. Experimental analysis on SAR images shows excellent speckle reduction without compromising feature preservation when compared to GGF-BNLM method. Results are also compared with other state-of-the-art and classic SAR depseckling techniques to demonstrate the effectiveness of the proposed method.

omicsNPC: Applying the Non-Parametric Combination Methodology to the Integrative Analysis of Heterogeneous Omics Data

PubMed Central

Karathanasis, Nestoras; Tsamardinos, Ioannis

2016-01-01

Background The advance of omics technologies has made possible to measure several data modalities on a system of interest. In this work, we illustrate how the Non-Parametric Combination methodology, namely NPC, can be used for simultaneously assessing the association of different molecular quantities with an outcome of interest. We argue that NPC methods have several potential applications in integrating heterogeneous omics technologies, as for example identifying genes whose methylation and transcriptional levels are jointly deregulated, or finding proteins whose abundance shows the same trends of the expression of their encoding genes. Results We implemented the NPC methodology within “omicsNPC”, an R function specifically tailored for the characteristics of omics data. We compare omicsNPC against a range of alternative methods on simulated as well as on real data. Comparisons on simulated data point out that omicsNPC produces unbiased / calibrated p-values and performs equally or significantly better than the other methods included in the study; furthermore, the analysis of real data show that omicsNPC (a) exhibits higher statistical power than other methods, (b) it is easily applicable in a number of different scenarios, and (c) its results have improved biological interpretability. Conclusions The omicsNPC function competitively behaves in all comparisons conducted in this study. Taking into account that the method (i) requires minimal assumptions, (ii) it can be used on different studies designs and (iii) it captures the dependences among heterogeneous data modalities, omicsNPC provides a flexible and statistically powerful solution for the integrative analysis of different omics data. PMID:27812137

Combined non-parametric and parametric approach for identification of time-variant systems

NASA Astrophysics Data System (ADS)

Dziedziech, Kajetan; Czop, Piotr; Staszewski, Wieslaw J.; Uhl, Tadeusz

2018-03-01

Identification of systems, structures and machines with variable physical parameters is a challenging task especially when time-varying vibration modes are involved. The paper proposes a new combined, two-step - i.e. non-parametric and parametric - modelling approach in order to determine time-varying vibration modes based on input-output measurements. Single-degree-of-freedom (SDOF) vibration modes from multi-degree-of-freedom (MDOF) non-parametric system representation are extracted in the first step with the use of time-frequency wavelet-based filters. The second step involves time-varying parametric representation of extracted modes with the use of recursive linear autoregressive-moving-average with exogenous inputs (ARMAX) models. The combined approach is demonstrated using system identification analysis based on the experimental mass-varying MDOF frame-like structure subjected to random excitation. The results show that the proposed combined method correctly captures the dynamics of the analysed structure, using minimum a priori information on the model.

Kernel-based whole-genome prediction of complex traits: a review.

PubMed

Morota, Gota; Gianola, Daniel

2014-01-01

Prediction of genetic values has been a focus of applied quantitative genetics since the beginning of the 20th century, with renewed interest following the advent of the era of whole genome-enabled prediction. Opportunities offered by the emergence of high-dimensional genomic data fueled by post-Sanger sequencing technologies, especially molecular markers, have driven researchers to extend Ronald Fisher and Sewall Wright's models to confront new challenges. In particular, kernel methods are gaining consideration as a regression method of choice for genome-enabled prediction. Complex traits are presumably influenced by many genomic regions working in concert with others (clearly so when considering pathways), thus generating interactions. Motivated by this view, a growing number of statistical approaches based on kernels attempt to capture non-additive effects, either parametrically or non-parametrically. This review centers on whole-genome regression using kernel methods applied to a wide range of quantitative traits of agricultural importance in animals and plants. We discuss various kernel-based approaches tailored to capturing total genetic variation, with the aim of arriving at an enhanced predictive performance in the light of available genome annotation information. Connections between prediction machines born in animal breeding, statistics, and machine learning are revisited, and their empirical prediction performance is discussed. Overall, while some encouraging results have been obtained with non-parametric kernels, recovering non-additive genetic variation in a validation dataset remains a challenge in quantitative genetics.

The NWRA Classification Infrastructure: description and extension to the Discriminant Analysis Flare Forecasting System (DAFFS)

NASA Astrophysics Data System (ADS)

Leka, K. D.; Barnes, Graham; Wagner, Eric

2018-04-01

A classification infrastructure built upon Discriminant Analysis (DA) has been developed at NorthWest Research Associates for examining the statistical differences between samples of two known populations. Originating to examine the physical differences between flare-quiet and flare-imminent solar active regions, we describe herein some details of the infrastructure including: parametrization of large datasets, schemes for handling "null" and "bad" data in multi-parameter analysis, application of non-parametric multi-dimensional DA, an extension through Bayes' theorem to probabilistic classification, and methods invoked for evaluating classifier success. The classifier infrastructure is applicable to a wide range of scientific questions in solar physics. We demonstrate its application to the question of distinguishing flare-imminent from flare-quiet solar active regions, updating results from the original publications that were based on different data and much smaller sample sizes. Finally, as a demonstration of "Research to Operations" efforts in the space-weather forecasting context, we present the Discriminant Analysis Flare Forecasting System (DAFFS), a near-real-time operationally-running solar flare forecasting tool that was developed from the research-directed infrastructure.

Analysis of a Rocket Based Combined Cycle Engine during Rocket Only Operation

NASA Technical Reports Server (NTRS)

Smith, T. D.; Steffen, C. J., Jr.; Yungster, S.; Keller, D. J.

1998-01-01

The all rocket mode of operation is a critical factor in the overall performance of a rocket based combined cycle (RBCC) vehicle. However, outside of performing experiments or a full three dimensional analysis, there are no first order parametric models to estimate performance. As a result, an axisymmetric RBCC engine was used to analytically determine specific impulse efficiency values based upon both full flow and gas generator configurations. Design of experiments methodology was used to construct a test matrix and statistical regression analysis was used to build parametric models. The main parameters investigated in this study were: rocket chamber pressure, rocket exit area ratio, percent of injected secondary flow, mixer-ejector inlet area, mixer-ejector area ratio, and mixer-ejector length-to-inject diameter ratio. A perfect gas computational fluid dynamics analysis was performed to obtain values of vacuum specific impulse. Statistical regression analysis was performed based on both full flow and gas generator engine cycles. Results were also found to be dependent upon the entire cycle assumptions. The statistical regression analysis determined that there were five significant linear effects, six interactions, and one second-order effect. Two parametric models were created to provide performance assessments of an RBCC engine in the all rocket mode of operation.

Two-sample statistics for testing the equality of survival functions against improper semi-parametric accelerated failure time alternatives: an application to the analysis of a breast cancer clinical trial.

PubMed

Broët, Philippe; Tsodikov, Alexander; De Rycke, Yann; Moreau, Thierry

2004-06-01

This paper presents two-sample statistics suited for testing equality of survival functions against improper semi-parametric accelerated failure time alternatives. These tests are designed for comparing either the short- or the long-term effect of a prognostic factor, or both. These statistics are obtained as partial likelihood score statistics from a time-dependent Cox model. As a consequence, the proposed tests can be very easily implemented using widely available software. A breast cancer clinical trial is presented as an example to demonstrate the utility of the proposed tests.

Identification of trends in rainfall, rainy days and 24 h maximum rainfall over subtropical Assam in Northeast India

NASA Astrophysics Data System (ADS)

Jhajharia, Deepak; Yadav, Brijesh K.; Maske, Sunil; Chattopadhyay, Surajit; Kar, Anil K.

2012-01-01

Trends in rainfall, rainy days and 24 h maximum rainfall are investigated using the Mann-Kendall non-parametric test at twenty-four sites of subtropical Assam located in the northeastern region of India. The trends are statistically confirmed by both the parametric and non-parametric methods and the magnitudes of significant trends are obtained through the linear regression test. In Assam, the average monsoon rainfall (rainy days) during the monsoon months of June to September is about 1606 mm (70), which accounts for about 70% (64%) of the annual rainfall (rainy days). On monthly time scales, sixteen and seventeen sites (twenty-one sites each) witnessed decreasing trends in the total rainfall (rainy days), out of which one and three trends (seven trends each) were found to be statistically significant in June and July, respectively. On the other hand, seventeen sites witnessed increasing trends in rainfall in the month of September, but none were statistically significant. In December (February), eighteen (twenty-two) sites witnessed decreasing (increasing) trends in total rainfall, out of which five (three) trends were statistically significant. For the rainy days during the months of November to January, twenty-two or more sites witnessed decreasing trends in Assam, but for nine (November), twelve (January) and eighteen (December) sites, these trends were statistically significant. These observed changes in rainfall, although most time series are not convincing as they show predominantly no significance, along with the well-reported climatic warming in monsoon and post-monsoon seasons may have implications for human health and water resources management over bio-diversity rich Northeast India.

Results of a SNP genome screen in a large Costa Rican pedigree segregating for severe bipolar disorder.

PubMed

Service, Susan; Molina, Julio; Deyoung, Joseph; Jawaheer, Damini; Aldana, Ileana; Vu, Thuy; Araya, Carmen; Araya, Xinia; Bejarano, Julio; Fournier, Eduardo; Ramirez, Magui; Mathews, Carol A; Davanzo, Pablo; Macaya, Gabriel; Sandkuijl, Lodewijk; Sabatti, Chiara; Reus, Victor; Freimer, Nelson

2006-06-05

We have ascertained in the Central Valley of Costa Rica a new kindred (CR201) segregating for severe bipolar disorder (BP-I). The family was identified by tracing genealogical connections among eight persons initially independently ascertained for a genome wide association study of BP-I. For the genome screen in CR201, we trimmed the family down to 168 persons (82 of whom are genotyped), containing 25 individuals with a best-estimate diagnosis of BP-I. A total of 4,690 SNP markers were genotyped. Analysis of the data was hampered by the size and complexity of the pedigree, which prohibited using exact multipoint methods on the entire kindred. Two-point parametric linkage analysis, using a conservative model of transmission, produced a maximum LOD score of 2.78 on chromosome 6, and a total of 39 loci with LOD scores >1.0. Multipoint parametric and non-parametric linkage analysis was performed separately on four sections of CR201, and interesting (nominal P-value from either analysis <0.01), although not statistically significant, regions were highlighted on chromosomes 1, 2, 3, 12, 16, 19, and 22, in at least one section of the pedigree, or when considering all sections together. The difficulties of analyzing genome wide SNP data for complex disorders in large, potentially informative, kindreds are discussed.

Robustness of S1 statistic with Hodges-Lehmann for skewed distributions

NASA Astrophysics Data System (ADS)

Ahad, Nor Aishah; Yahaya, Sharipah Soaad Syed; Yin, Lee Ping

2016-10-01

Analysis of variance (ANOVA) is a common use parametric method to test the differences in means for more than two groups when the populations are normally distributed. ANOVA is highly inefficient under the influence of non- normal and heteroscedastic settings. When the assumptions are violated, researchers are looking for alternative such as Kruskal-Wallis under nonparametric or robust method. This study focused on flexible method, S1 statistic for comparing groups using median as the location estimator. S1 statistic was modified by substituting the median with Hodges-Lehmann and the default scale estimator with the variance of Hodges-Lehmann and MADn to produce two different test statistics for comparing groups. Bootstrap method was used for testing the hypotheses since the sampling distributions of these modified S1 statistics are unknown. The performance of the proposed statistic in terms of Type I error was measured and compared against the original S1 statistic, ANOVA and Kruskal-Wallis. The propose procedures show improvement compared to the original statistic especially under extremely skewed distribution.

Parametrically excited non-linear multidegree-of-freedom systems with repeated natural frequencies

NASA Astrophysics Data System (ADS)

Tezak, E. G.; Nayfeh, A. H.; Mook, D. T.

1982-12-01

A method for analyzing multidegree-of-freedom systems having a repeated natural frequency subjected to a parametric excitation is presented. Attention is given to the ordering of the various terms (linear and non-linear) in the governing equations. The analysis is based on the method of multiple scales. As a numerical example involving a parametric resonance, panel flutter is discussed in detail in order to illustrate the type of results one can expect to obtain with this analysis. Some of the analytical results are verified by a numerical integration of the governing equations.

Statistical analysis of water-quality data containing multiple detection limits II: S-language software for nonparametric distribution modeling and hypothesis testing

USGS Publications Warehouse

Lee, L.; Helsel, D.

2007-01-01

Analysis of low concentrations of trace contaminants in environmental media often results in left-censored data that are below some limit of analytical precision. Interpretation of values becomes complicated when there are multiple detection limits in the data-perhaps as a result of changing analytical precision over time. Parametric and semi-parametric methods, such as maximum likelihood estimation and robust regression on order statistics, can be employed to model distributions of multiply censored data and provide estimates of summary statistics. However, these methods are based on assumptions about the underlying distribution of data. Nonparametric methods provide an alternative that does not require such assumptions. A standard nonparametric method for estimating summary statistics of multiply-censored data is the Kaplan-Meier (K-M) method. This method has seen widespread usage in the medical sciences within a general framework termed "survival analysis" where it is employed with right-censored time-to-failure data. However, K-M methods are equally valid for the left-censored data common in the geosciences. Our S-language software provides an analytical framework based on K-M methods that is tailored to the needs of the earth and environmental sciences community. This includes routines for the generation of empirical cumulative distribution functions, prediction or exceedance probabilities, and related confidence limits computation. Additionally, our software contains K-M-based routines for nonparametric hypothesis testing among an unlimited number of grouping variables. A primary characteristic of K-M methods is that they do not perform extrapolation and interpolation. Thus, these routines cannot be used to model statistics beyond the observed data range or when linear interpolation is desired. For such applications, the aforementioned parametric and semi-parametric methods must be used.

Age-dependent biochemical quantities: an approach for calculating reference intervals.

PubMed

Bjerner, J

2007-01-01

A parametric method is often preferred when calculating reference intervals for biochemical quantities, as non-parametric methods are less efficient and require more observations/study subjects. Parametric methods are complicated, however, because of three commonly encountered features. First, biochemical quantities seldom display a Gaussian distribution, and there must either be a transformation procedure to obtain such a distribution or a more complex distribution has to be used. Second, biochemical quantities are often dependent on a continuous covariate, exemplified by rising serum concentrations of MUC1 (episialin, CA15.3) with increasing age. Third, outliers often exert substantial influence on parametric estimations and therefore need to be excluded before calculations are made. The International Federation of Clinical Chemistry (IFCC) currently recommends that confidence intervals be calculated for the reference centiles obtained. However, common statistical packages allowing for the adjustment of a continuous covariate do not make this calculation. In the method described in the current study, Tukey's fence is used to eliminate outliers and two-stage transformations (modulus-exponential-normal) in order to render Gaussian distributions. Fractional polynomials are employed to model functions for mean and standard deviations dependent on a covariate, and the model is selected by maximum likelihood. Confidence intervals are calculated for the fitted centiles by combining parameter estimation and sampling uncertainties. Finally, the elimination of outliers was made dependent on covariates by reiteration. Though a good knowledge of statistical theory is needed when performing the analysis, the current method is rewarding because the results are of practical use in patient care.

Summarizing techniques that combine three non-parametric scores to detect disease-associated 2-way SNP-SNP interactions.

PubMed

Sengupta Chattopadhyay, Amrita; Hsiao, Ching-Lin; Chang, Chien Ching; Lian, Ie-Bin; Fann, Cathy S J

2014-01-01

Identifying susceptibility genes that influence complex diseases is extremely difficult because loci often influence the disease state through genetic interactions. Numerous approaches to detect disease-associated SNP-SNP interactions have been developed, but none consistently generates high-quality results under different disease scenarios. Using summarizing techniques to combine a number of existing methods may provide a solution to this problem. Here we used three popular non-parametric methods-Gini, absolute probability difference (APD), and entropy-to develop two novel summary scores, namely principle component score (PCS) and Z-sum score (ZSS), with which to predict disease-associated genetic interactions. We used a simulation study to compare performance of the non-parametric scores, the summary scores, the scaled-sum score (SSS; used in polymorphism interaction analysis (PIA)), and the multifactor dimensionality reduction (MDR). The non-parametric methods achieved high power, but no non-parametric method outperformed all others under a variety of epistatic scenarios. PCS and ZSS, however, outperformed MDR. PCS, ZSS and SSS displayed controlled type-I-errors (<0.05) compared to GS, APDS, ES (>0.05). A real data study using the genetic-analysis-workshop 16 (GAW 16) rheumatoid arthritis dataset identified a number of interesting SNP-SNP interactions. © 2013 Elsevier B.V. All rights reserved.

Empirical intrinsic geometry for nonlinear modeling and time series filtering.

PubMed

Talmon, Ronen; Coifman, Ronald R

2013-07-30

In this paper, we present a method for time series analysis based on empirical intrinsic geometry (EIG). EIG enables one to reveal the low-dimensional parametric manifold as well as to infer the underlying dynamics of high-dimensional time series. By incorporating concepts of information geometry, this method extends existing geometric analysis tools to support stochastic settings and parametrizes the geometry of empirical distributions. However, the statistical models are not required as priors; hence, EIG may be applied to a wide range of real signals without existing definitive models. We show that the inferred model is noise-resilient and invariant under different observation and instrumental modalities. In addition, we show that it can be extended efficiently to newly acquired measurements in a sequential manner. These two advantages enable us to revisit the Bayesian approach and incorporate empirical dynamics and intrinsic geometry into a nonlinear filtering framework. We show applications to nonlinear and non-Gaussian tracking problems as well as to acoustic signal localization.

[Evaluation of using statistical methods in selected national medical journals].

PubMed

Sych, Z

1996-01-01

The paper covers the performed evaluation of frequency with which the statistical methods were applied in analyzed works having been published in six selected, national medical journals in the years 1988-1992. For analysis the following journals were chosen, namely: Klinika Oczna, Medycyna Pracy, Pediatria Polska, Polski Tygodnik Lekarski, Roczniki Państwowego Zakładu Higieny, Zdrowie Publiczne. Appropriate number of works up to the average in the remaining medical journals was randomly selected from respective volumes of Pol. Tyg. Lek. The studies did not include works wherein the statistical analysis was not implemented, which referred both to national and international publications. That exemption was also extended to review papers, casuistic ones, reviews of books, handbooks, monographies, reports from scientific congresses, as well as papers on historical topics. The number of works was defined in each volume. Next, analysis was performed to establish the mode of finding out a suitable sample in respective studies, differentiating two categories: random and target selections. Attention was also paid to the presence of control sample in the individual works. In the analysis attention was also focussed on the existence of sample characteristics, setting up three categories: complete, partial and lacking. In evaluating the analyzed works an effort was made to present the results of studies in tables and figures (Tab. 1, 3). Analysis was accomplished with regard to the rate of employing statistical methods in analyzed works in relevant volumes of six selected, national medical journals for the years 1988-1992, simultaneously determining the number of works, in which no statistical methods were used. Concurrently the frequency of applying the individual statistical methods was analyzed in the scrutinized works. Prominence was given to fundamental statistical methods in the field of descriptive statistics (measures of position, measures of dispersion) as well as most important methods of mathematical statistics such as parametric tests of significance, analysis of variance (in single and dual classifications). non-parametric tests of significance, correlation and regression. The works, in which use was made of either multiple correlation or multiple regression or else more complex methods of studying the relationship for two or more numbers of variables, were incorporated into the works whose statistical methods were constituted by correlation and regression as well as other methods, e.g. statistical methods being used in epidemiology (coefficients of incidence and morbidity, standardization of coefficients, survival tables) factor analysis conducted by Jacobi-Hotellng's method, taxonomic methods and others. On the basis of the performed studies it has been established that the frequency of employing statistical methods in the six selected national, medical journals in the years 1988-1992 was 61.1-66.0% of the analyzed works (Tab. 3), and they generally were almost similar to the frequency provided in English language medical journals. On a whole, no significant differences were disclosed in the frequency of applied statistical methods (Tab. 4) as well as in frequency of random tests (Tab. 3) in the analyzed works, appearing in the medical journals in respective years 1988-1992. The most frequently used statistical methods in analyzed works for 1988-1992 were the measures of position 44.2-55.6% and measures of dispersion 32.5-38.5% as well as parametric tests of significance 26.3-33.1% of the works analyzed (Tab. 4). For the purpose of increasing the frequency and reliability of the used statistical methods, the didactics should be widened in the field of biostatistics at medical studies and postgraduation training designed for physicians and scientific-didactic workers.

Modelling the Effects of Land-Use Changes on Climate: a Case Study on Yamula DAM

NASA Astrophysics Data System (ADS)

Köylü, Ü.; Geymen, A.

2016-10-01

Dams block flow of rivers and cause artificial water reservoirs which affect the climate and the land use characteristics of the river basin. In this research, the effect of the huge water body obtained by Yamula Dam in Kızılırmak Basin is analysed over surrounding spatial's land use and climate change. Mann Kendal non-parametrical statistical test, Theil&Sen Slope method, Inverse Distance Weighting (IDW), Soil Conservation Service-Curve Number (SCS-CN) methods are integrated for spatial and temporal analysis of the research area. For this research humidity, temperature, wind speed, precipitation observations which are collected in 16 weather stations nearby Kızılırmak Basin are analyzed. After that these statistical information is combined by GIS data over years. An application is developed for GIS analysis in Python Programming Language and integrated with ArcGIS software. Statistical analysis calculated in the R Project for Statistical Computing and integrated with developed application. According to the statistical analysis of extracted time series of meteorological parameters, statistical significant spatiotemporal trends are observed for climate change and land use characteristics. In this study, we indicated the effect of big dams in local climate on semi-arid Yamula Dam.

School furniture and work surface lighting impacts on the body posture of Paraíba's public school students.

PubMed

da Silva, Luiz Bueno; Coutinho, Antonio Souto; da Costa Eulálio, Eliza Juliana; Soares, Elaine Victor Gonçalves

2012-01-01

The main objective of this study is to evaluate the impact of school furniture and work surface lighting on the body posture of public Middle School students from Paraíba (Brazil). The survey was carried out in two public schools and the target population for the study included 8th grade groups involving a total of 31 students. Brazilian standards for lighting levels, the CEBRACE standards for furniture measurements and the Postural Assessment Software (SAPO) for the postural misalignment assay were adopted for the measurements comparison. The statistic analysis includes analyses of parametric and non-parametric correlations. The results show that the students' most affected parts of the body were the spine, the regions of the knees and head and neck, with 90% of the total number of students presenting postural misalignment. The lighting levels were usually found below 300 lux, below recommended levels. The statistic analysis show that the more adequate the furniture seems to be to the user, the less the user will complain of pain. Such results indicate the need of investments in more suitable school furniture and structural reforms aimed at improving the lighting in the classrooms, which could fulfill the students' profile and reduce their complaints.

Reference interval estimation: Methodological comparison using extensive simulations and empirical data.

PubMed

Daly, Caitlin H; Higgins, Victoria; Adeli, Khosrow; Grey, Vijay L; Hamid, Jemila S

2017-12-01

To statistically compare and evaluate commonly used methods of estimating reference intervals and to determine which method is best based on characteristics of the distribution of various data sets. Three approaches for estimating reference intervals, i.e. parametric, non-parametric, and robust, were compared with simulated Gaussian and non-Gaussian data. The hierarchy of the performances of each method was examined based on bias and measures of precision. The findings of the simulation study were illustrated through real data sets. In all Gaussian scenarios, the parametric approach provided the least biased and most precise estimates. In non-Gaussian scenarios, no single method provided the least biased and most precise estimates for both limits of a reference interval across all sample sizes, although the non-parametric approach performed the best for most scenarios. The hierarchy of the performances of the three methods was only impacted by sample size and skewness. Differences between reference interval estimates established by the three methods were inflated by variability. Whenever possible, laboratories should attempt to transform data to a Gaussian distribution and use the parametric approach to obtain the most optimal reference intervals. When this is not possible, laboratories should consider sample size and skewness as factors in their choice of reference interval estimation method. The consequences of false positives or false negatives may also serve as factors in this decision. Copyright © 2017 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

Two-Sample Statistics for Testing the Equality of Survival Functions Against Improper Semi-parametric Accelerated Failure Time Alternatives: An Application to the Analysis of a Breast Cancer Clinical Trial

PubMed Central

BROËT, PHILIPPE; TSODIKOV, ALEXANDER; DE RYCKE, YANN; MOREAU, THIERRY

2010-01-01

This paper presents two-sample statistics suited for testing equality of survival functions against improper semi-parametric accelerated failure time alternatives. These tests are designed for comparing either the short- or the long-term effect of a prognostic factor, or both. These statistics are obtained as partial likelihood score statistics from a time-dependent Cox model. As a consequence, the proposed tests can be very easily implemented using widely available software. A breast cancer clinical trial is presented as an example to demonstrate the utility of the proposed tests. PMID:15293627

CORNAS: coverage-dependent RNA-Seq analysis of gene expression data without biological replicates.

PubMed

Low, Joel Z B; Khang, Tsung Fei; Tammi, Martti T

2017-12-28

In current statistical methods for calling differentially expressed genes in RNA-Seq experiments, the assumption is that an adjusted observed gene count represents an unknown true gene count. This adjustment usually consists of a normalization step to account for heterogeneous sample library sizes, and then the resulting normalized gene counts are used as input for parametric or non-parametric differential gene expression tests. A distribution of true gene counts, each with a different probability, can result in the same observed gene count. Importantly, sequencing coverage information is currently not explicitly incorporated into any of the statistical models used for RNA-Seq analysis. We developed a fast Bayesian method which uses the sequencing coverage information determined from the concentration of an RNA sample to estimate the posterior distribution of a true gene count. Our method has better or comparable performance compared to NOISeq and GFOLD, according to the results from simulations and experiments with real unreplicated data. We incorporated a previously unused sequencing coverage parameter into a procedure for differential gene expression analysis with RNA-Seq data. Our results suggest that our method can be used to overcome analytical bottlenecks in experiments with limited number of replicates and low sequencing coverage. The method is implemented in CORNAS (Coverage-dependent RNA-Seq), and is available at https://github.com/joel-lzb/CORNAS .

Privacy-preserving Kruskal-Wallis test.

PubMed

Guo, Suxin; Zhong, Sheng; Zhang, Aidong

2013-10-01

Statistical tests are powerful tools for data analysis. Kruskal-Wallis test is a non-parametric statistical test that evaluates whether two or more samples are drawn from the same distribution. It is commonly used in various areas. But sometimes, the use of the method is impeded by privacy issues raised in fields such as biomedical research and clinical data analysis because of the confidential information contained in the data. In this work, we give a privacy-preserving solution for the Kruskal-Wallis test which enables two or more parties to coordinately perform the test on the union of their data without compromising their data privacy. To the best of our knowledge, this is the first work that solves the privacy issues in the use of the Kruskal-Wallis test on distributed data. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Research design and statistical methods in Pakistan Journal of Medical Sciences (PJMS)

PubMed Central

Akhtar, Sohail; Shah, Syed Wadood Ali; Rafiq, M.; Khan, Ajmal

2016-01-01

Objective: This article compares the study design and statistical methods used in 2005, 2010 and 2015 of Pakistan Journal of Medical Sciences (PJMS). Methods: Only original articles of PJMS were considered for the analysis. The articles were carefully reviewed for statistical methods and designs, and then recorded accordingly. The frequency of each statistical method and research design was estimated and compared with previous years. Results: A total of 429 articles were evaluated (n=74 in 2005, n=179 in 2010, n=176 in 2015) in which 171 (40%) were cross-sectional and 116 (27%) were prospective study designs. A verity of statistical methods were found in the analysis. The most frequent methods include: descriptive statistics (n=315, 73.4%), chi-square/Fisher’s exact tests (n=205, 47.8%) and student t-test (n=186, 43.4%). There was a significant increase in the use of statistical methods over time period: t-test, chi-square/Fisher’s exact test, logistic regression, epidemiological statistics, and non-parametric tests. Conclusion: This study shows that a diverse variety of statistical methods have been used in the research articles of PJMS and frequency improved from 2005 to 2015. However, descriptive statistics was the most frequent method of statistical analysis in the published articles while cross-sectional study design was common study design. PMID:27022365

Accounting for Non-Gaussian Sources of Spatial Correlation in Parametric Functional Magnetic Resonance Imaging Paradigms I: Revisiting Cluster-Based Inferences.

PubMed

Gopinath, Kaundinya; Krishnamurthy, Venkatagiri; Sathian, K

2018-02-01

In a recent study, Eklund et al. employed resting-state functional magnetic resonance imaging data as a surrogate for null functional magnetic resonance imaging (fMRI) datasets and posited that cluster-wise family-wise error (FWE) rate-corrected inferences made by using parametric statistical methods in fMRI studies over the past two decades may have been invalid, particularly for cluster defining thresholds less stringent than p < 0.001; this was principally because the spatial autocorrelation functions (sACF) of fMRI data had been modeled incorrectly to follow a Gaussian form, whereas empirical data suggested otherwise. Here, we show that accounting for non-Gaussian signal components such as those arising from resting-state neural activity as well as physiological responses and motion artifacts in the null fMRI datasets yields first- and second-level general linear model analysis residuals with nearly uniform and Gaussian sACF. Further comparison with nonparametric permutation tests indicates that cluster-based FWE corrected inferences made with Gaussian spatial noise approximations are valid.

Statistical Theory for the "RCT-YES" Software: Design-Based Causal Inference for RCTs. NCEE 2015-4011

ERIC Educational Resources Information Center

Schochet, Peter Z.

2015-01-01

This report presents the statistical theory underlying the "RCT-YES" software that estimates and reports impacts for RCTs for a wide range of designs used in social policy research. The report discusses a unified, non-parametric design-based approach for impact estimation using the building blocks of the Neyman-Rubin-Holland causal…

Estimating and comparing microbial diversity in the presence of sequencing errors

PubMed Central

Chiu, Chun-Huo

2016-01-01

Estimating and comparing microbial diversity are statistically challenging due to limited sampling and possible sequencing errors for low-frequency counts, producing spurious singletons. The inflated singleton count seriously affects statistical analysis and inferences about microbial diversity. Previous statistical approaches to tackle the sequencing errors generally require different parametric assumptions about the sampling model or about the functional form of frequency counts. Different parametric assumptions may lead to drastically different diversity estimates. We focus on nonparametric methods which are universally valid for all parametric assumptions and can be used to compare diversity across communities. We develop here a nonparametric estimator of the true singleton count to replace the spurious singleton count in all methods/approaches. Our estimator of the true singleton count is in terms of the frequency counts of doubletons, tripletons and quadrupletons, provided these three frequency counts are reliable. To quantify microbial alpha diversity for an individual community, we adopt the measure of Hill numbers (effective number of taxa) under a nonparametric framework. Hill numbers, parameterized by an order q that determines the measures’ emphasis on rare or common species, include taxa richness (q = 0), Shannon diversity (q = 1, the exponential of Shannon entropy), and Simpson diversity (q = 2, the inverse of Simpson index). A diversity profile which depicts the Hill number as a function of order q conveys all information contained in a taxa abundance distribution. Based on the estimated singleton count and the original non-singleton frequency counts, two statistical approaches (non-asymptotic and asymptotic) are developed to compare microbial diversity for multiple communities. (1) A non-asymptotic approach refers to the comparison of estimated diversities of standardized samples with a common finite sample size or sample completeness. This approach aims to compare diversity estimates for equally-large or equally-complete samples; it is based on the seamless rarefaction and extrapolation sampling curves of Hill numbers, specifically for q = 0, 1 and 2. (2) An asymptotic approach refers to the comparison of the estimated asymptotic diversity profiles. That is, this approach compares the estimated profiles for complete samples or samples whose size tends to be sufficiently large. It is based on statistical estimation of the true Hill number of any order q ≥ 0. In the two approaches, replacing the spurious singleton count by our estimated count, we can greatly remove the positive biases associated with diversity estimates due to spurious singletons and also make fair comparisons across microbial communities, as illustrated in our simulation results and in applying our method to analyze sequencing data from viral metagenomes. PMID:26855872

Restoration of MRI data for intensity non-uniformities using local high order intensity statistics

PubMed Central

Hadjidemetriou, Stathis; Studholme, Colin; Mueller, Susanne; Weiner, Michael; Schuff, Norbert

2008-01-01

MRI at high magnetic fields (>3.0 T) is complicated by strong inhomogeneous radio-frequency fields, sometimes termed the “bias field”. These lead to non-biological intensity non-uniformities across the image. They can complicate further image analysis such as registration and tissue segmentation. Existing methods for intensity uniformity restoration have been optimized for 1.5 T, but they are less effective for 3.0 T MRI, and not at all satisfactory for higher fields. Also, many of the existing restoration algorithms require a brain template or use a prior atlas, which can restrict their practicalities. In this study an effective intensity uniformity restoration algorithm has been developed based on non-parametric statistics of high order local intensity co-occurrences. These statistics are restored with a non-stationary Wiener filter. The algorithm also assumes a smooth non-uniformity and is stable. It does not require a prior atlas and is robust to variations in anatomy. In geriatric brain imaging it is robust to variations such as enlarged ventricles and low contrast to noise ratio. The co-occurrence statistics improve robustness to whole head images with pronounced non-uniformities present in high field acquisitions. Its significantly improved performance and lower time requirements have been demonstrated by comparing it to the very commonly used N3 algorithm on BrainWeb MR simulator images as well as on real 4 T human head images. PMID:18621568

Component isolation for multi-component signal analysis using a non-parametric gaussian latent feature model

NASA Astrophysics Data System (ADS)

Yang, Yang; Peng, Zhike; Dong, Xingjian; Zhang, Wenming; Clifton, David A.

2018-03-01

A challenge in analysing non-stationary multi-component signals is to isolate nonlinearly time-varying signals especially when they are overlapped in time and frequency plane. In this paper, a framework integrating time-frequency analysis-based demodulation and a non-parametric Gaussian latent feature model is proposed to isolate and recover components of such signals. The former aims to remove high-order frequency modulation (FM) such that the latter is able to infer demodulated components while simultaneously discovering the number of the target components. The proposed method is effective in isolating multiple components that have the same FM behavior. In addition, the results show that the proposed method is superior to generalised demodulation with singular-value decomposition-based method, parametric time-frequency analysis with filter-based method and empirical model decomposition base method, in recovering the amplitude and phase of superimposed components.

Rating locomotive crew diesel emission exposure profiles using statistics and Bayesian Decision Analysis.

PubMed

Hewett, Paul; Bullock, William H

2014-01-01

For more than 20 years CSX Transportation (CSXT) has collected exposure measurements from locomotive engineers and conductors who are potentially exposed to diesel emissions. The database included measurements for elemental and total carbon, polycyclic aromatic hydrocarbons, aromatics, aldehydes, carbon monoxide, and nitrogen dioxide. This database was statistically analyzed and summarized, and the resulting statistics and exposure profiles were compared to relevant occupational exposure limits (OELs) using both parametric and non-parametric descriptive and compliance statistics. Exposure ratings, using the American Industrial Health Association (AIHA) exposure categorization scheme, were determined using both the compliance statistics and Bayesian Decision Analysis (BDA). The statistical analysis of the elemental carbon data (a marker for diesel particulate) strongly suggests that the majority of levels in the cabs of the lead locomotives (n = 156) were less than the California guideline of 0.020 mg/m(3). The sample 95th percentile was roughly half the guideline; resulting in an AIHA exposure rating of category 2/3 (determined using BDA). The elemental carbon (EC) levels in the trailing locomotives tended to be greater than those in the lead locomotive; however, locomotive crews rarely ride in the trailing locomotive. Lead locomotive EC levels were similar to those reported by other investigators studying locomotive crew exposures and to levels measured in urban areas. Lastly, both the EC sample mean and 95%UCL were less than the Environmental Protection Agency (EPA) reference concentration of 0.005 mg/m(3). With the exception of nitrogen dioxide, the overwhelming majority of the measurements for total carbon, polycyclic aromatic hydrocarbons, aromatics, aldehydes, and combustion gases in the cabs of CSXT locomotives were either non-detects or considerably less than the working OELs for the years represented in the database. When compared to the previous American Conference of Governmental Industrial Hygienists (ACGIH) threshold limit value (TLV) of 3 ppm the nitrogen dioxide exposure profile merits an exposure rating of AIHA exposure category 1. However, using the newly adopted TLV of 0.2 ppm the exposure profile receives an exposure rating of category 4. Further evaluation is recommended to determine the current status of nitrogen dioxide exposures. [Supplementary materials are available for this article. Go to the publisher's online edition of Journal of Occupational and Environmental Hygiene for the following free supplemental resource: additional text on OELs, methods, results, and additional figures and tables.].

NIRS-SPM: statistical parametric mapping for near infrared spectroscopy

NASA Astrophysics Data System (ADS)

Tak, Sungho; Jang, Kwang Eun; Jung, Jinwook; Jang, Jaeduck; Jeong, Yong; Ye, Jong Chul

2008-02-01

Even though there exists a powerful statistical parametric mapping (SPM) tool for fMRI, similar public domain tools are not available for near infrared spectroscopy (NIRS). In this paper, we describe a new public domain statistical toolbox called NIRS-SPM for quantitative analysis of NIRS signals. Specifically, NIRS-SPM statistically analyzes the NIRS data using GLM and makes inference as the excursion probability which comes from the random field that are interpolated from the sparse measurement. In order to obtain correct inference, NIRS-SPM offers the pre-coloring and pre-whitening method for temporal correlation estimation. For simultaneous recording NIRS signal with fMRI, the spatial mapping between fMRI image and real coordinate in 3-D digitizer is estimated using Horn's algorithm. These powerful tools allows us the super-resolution localization of the brain activation which is not possible using the conventional NIRS analysis tools.

Oral fennel (Foeniculum vulgare) drop effect on primary dysmenorrhea: Effectiveness of herbal drug.

PubMed

Bokaie, Mahshid; Farajkhoda, Tahmineh; Enjezab, Behnaz; Khoshbin, Azam; Karimi-Zarchi, Mojgan; Zarchi Mojgan, Karimi

2013-03-01

Primary dysmenorrhea refers to the occurrence of painful menstrual cramps of uterus and is considered as a gynecological complaint. The common treatment for this problem is medical therapy such as mefenamic acid [non-steroidal anti-inflammatory drugs (NSAIDs)] and oral contraceptive pills, both of which work by reducing myometrial activity. Fennel contains an antispasmodic and anethol agents and may be helpful for management of primary dysmenorrhea. The aim of this study was to evaluate the effects of oral fennel drop for treating primary dysmenorrhea. Sixty college students suffering from primary dysmenorrhea were randomly assigned to two groups and followed up for two cycles. Statistical analysis was performed using SPSS version 16. P < 0.05 was considered to be statistically significant. Parametric and non-parametric tests were adopted. Comparison of pain intensity in the two groups showed that there was no significant difference in pain relief between the two groups. Comparison of bleeding severity in the study group before and after intervention was demonstrated from the first day to the fifth day (PV on first day, second day, third day, fourth day, and fifth day 0.948, 0.330, 0.508, 0.583, 0.890, respectively). It seems that fennel can be effective in reducing the severity of dysmenorrhea, but it has an unpleasant taste in view of most of the volunteers.

Predictive capacity of a non-radioisotopic local lymph node assay using flow cytometry, LLNA:BrdU-FCM: Comparison of a cutoff approach and inferential statistics.

PubMed

Kim, Da-Eun; Yang, Hyeri; Jang, Won-Hee; Jung, Kyoung-Mi; Park, Miyoung; Choi, Jin Kyu; Jung, Mi-Sook; Jeon, Eun-Young; Heo, Yong; Yeo, Kyung-Wook; Jo, Ji-Hoon; Park, Jung Eun; Sohn, Soo Jung; Kim, Tae Sung; Ahn, Il Young; Jeong, Tae-Cheon; Lim, Kyung-Min; Bae, SeungJin

2016-01-01

In order for a novel test method to be applied for regulatory purposes, its reliability and relevance, i.e., reproducibility and predictive capacity, must be demonstrated. Here, we examine the predictive capacity of a novel non-radioisotopic local lymph node assay, LLNA:BrdU-FCM (5-bromo-2'-deoxyuridine-flow cytometry), with a cutoff approach and inferential statistics as a prediction model. 22 reference substances in OECD TG429 were tested with a concurrent positive control, hexylcinnamaldehyde 25%(PC), and the stimulation index (SI) representing the fold increase in lymph node cells over the vehicle control was obtained. The optimal cutoff SI (2.7≤cutoff <3.5), with respect to predictive capacity, was obtained by a receiver operating characteristic curve, which produced 90.9% accuracy for the 22 substances. To address the inter-test variability in responsiveness, SI values standardized with PC were employed to obtain the optimal percentage cutoff (42.6≤cutoff <57.3% of PC), which produced 86.4% accuracy. A test substance may be diagnosed as a sensitizer if a statistically significant increase in SI is elicited. The parametric one-sided t-test and non-parametric Wilcoxon rank-sum test produced 77.3% accuracy. Similarly, a test substance could be defined as a sensitizer if the SI means of the vehicle control, and of the low, middle, and high concentrations were statistically significantly different, which was tested using ANOVA or Kruskal-Wallis, with post hoc analysis, Dunnett, or DSCF (Dwass-Steel-Critchlow-Fligner), respectively, depending on the equal variance test, producing 81.8% accuracy. The absolute SI-based cutoff approach produced the best predictive capacity, however the discordant decisions between prediction models need to be examined further. Copyright © 2015 Elsevier Inc. All rights reserved.

Non-parametric directionality analysis - Extension for removal of a single common predictor and application to time series.

PubMed

Halliday, David M; Senik, Mohd Harizal; Stevenson, Carl W; Mason, Rob

2016-08-01

The ability to infer network structure from multivariate neuronal signals is central to computational neuroscience. Directed network analyses typically use parametric approaches based on auto-regressive (AR) models, where networks are constructed from estimates of AR model parameters. However, the validity of using low order AR models for neurophysiological signals has been questioned. A recent article introduced a non-parametric approach to estimate directionality in bivariate data, non-parametric approaches are free from concerns over model validity. We extend the non-parametric framework to include measures of directed conditional independence, using scalar measures that decompose the overall partial correlation coefficient summatively by direction, and a set of functions that decompose the partial coherence summatively by direction. A time domain partial correlation function allows both time and frequency views of the data to be constructed. The conditional independence estimates are conditioned on a single predictor. The framework is applied to simulated cortical neuron networks and mixtures of Gaussian time series data with known interactions. It is applied to experimental data consisting of local field potential recordings from bilateral hippocampus in anaesthetised rats. The framework offers a non-parametric approach to estimation of directed interactions in multivariate neuronal recordings, and increased flexibility in dealing with both spike train and time series data. The framework offers a novel alternative non-parametric approach to estimate directed interactions in multivariate neuronal recordings, and is applicable to spike train and time series data. Copyright © 2016 Elsevier B.V. All rights reserved.

Four modes of optical parametric operation for squeezed state generation

NASA Astrophysics Data System (ADS)

Andersen, U. L.; Buchler, B. C.; Lam, P. K.; Wu, J. W.; Gao, J. R.; Bachor, H.-A.

2003-11-01

We report a versatile instrument, based on a monolithic optical parametric amplifier, which reliably generates four different types of squeezed light. We obtained vacuum squeezing, low power amplitude squeezing, phase squeezing and bright amplitude squeezing. We show a complete analysis of this light, including a full quantum state tomography. In addition we demonstrate the direct detection of the squeezed state statistics without the aid of a spectrum analyser. This technique makes the nonclassical properties directly visible and allows complete measurement of the statistical moments of the squeezed quadrature.

Headache in acute ischaemic stroke: a lesion mapping study.

PubMed

Seifert, Christian L; Schönbach, Etienne M; Magon, Stefano; Gross, Elena; Zimmer, Claus; Förschler, Anette; Tölle, Thomas R; Mühlau, Mark; Sprenger, Till; Poppert, Holger

2016-01-01

Headache is a common symptom in acute ischaemic stroke, but the underlying mechanisms are incompletely understood. The aim of this lesion mapping study was to identify brain regions, which are related to the development of headache in acute ischaemic stroke. Patients with acute ischaemic stroke (n = 100) were assessed by brain MRI at 3 T including diffusion weighted imaging. We included 50 patients with stroke and headache as well as 50 patients with stroke but no headache symptoms. Infarcts were manually outlined and images were transformed into standard stereotaxic space using non-linear warping. Voxel-wise overlap and subtraction analyses of lesions as well as non-parametric statistics were conducted. The same analyses were carried out by flipping of left-sided lesions, so that all strokes were transformed to the same hemisphere. Between the headache group as well as the non-headache there was no difference in infarct volumes, in the distribution of affected vascular beds or in the clinical severity of strokes. The headache phenotype was tension-type like in most cases. Subtraction analysis revealed that in headache sufferers infarctions were more often distributed in two well-known areas of the central pain matrix: the insula and the somatosensory cortex. This result was confirmed in the flipped analysis and by non-parametric statistical testing (whole brain corrected P-value < 0.01). To the best of our knowledge, this is the first lesion mapping study investigating potential lesional patterns associated with headache in acute ischaemic stroke. Insular strokes turned out to be strongly associated with headache. As the insular cortex is a well-established region in pain processing, our results suggest that, at least in a subgroup of patients, acute stroke-related headache might be centrally driven. © The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Empirical likelihood method for non-ignorable missing data problems.

PubMed

Guan, Zhong; Qin, Jing

2017-01-01

Missing response problem is ubiquitous in survey sampling, medical, social science and epidemiology studies. It is well known that non-ignorable missing is the most difficult missing data problem where the missing of a response depends on its own value. In statistical literature, unlike the ignorable missing data problem, not many papers on non-ignorable missing data are available except for the full parametric model based approach. In this paper we study a semiparametric model for non-ignorable missing data in which the missing probability is known up to some parameters, but the underlying distributions are not specified. By employing Owen (1988)'s empirical likelihood method we can obtain the constrained maximum empirical likelihood estimators of the parameters in the missing probability and the mean response which are shown to be asymptotically normal. Moreover the likelihood ratio statistic can be used to test whether the missing of the responses is non-ignorable or completely at random. The theoretical results are confirmed by a simulation study. As an illustration, the analysis of a real AIDS trial data shows that the missing of CD4 counts around two years are non-ignorable and the sample mean based on observed data only is biased.

The performance of sample selection estimators to control for attrition bias.

PubMed

Grasdal, A

2001-07-01

Sample attrition is a potential source of selection bias in experimental, as well as non-experimental programme evaluation. For labour market outcomes, such as employment status and earnings, missing data problems caused by attrition can be circumvented by the collection of follow-up data from administrative registers. For most non-labour market outcomes, however, investigators must rely on participants' willingness to co-operate in keeping detailed follow-up records and statistical correction procedures to identify and adjust for attrition bias. This paper combines survey and register data from a Norwegian randomized field trial to evaluate the performance of parametric and semi-parametric sample selection estimators commonly used to correct for attrition bias. The considered estimators work well in terms of producing point estimates of treatment effects close to the experimental benchmark estimates. Results are sensitive to exclusion restrictions. The analysis also demonstrates an inherent paradox in the 'common support' approach, which prescribes exclusion from the analysis of observations outside of common support for the selection probability. The more important treatment status is as a determinant of attrition, the larger is the proportion of treated with support for the selection probability outside the range, for which comparison with untreated counterparts is possible. Copyright 2001 John Wiley & Sons, Ltd.

Evaluation of model-based versus non-parametric monaural noise-reduction approaches for hearing aids.

PubMed

Harlander, Niklas; Rosenkranz, Tobias; Hohmann, Volker

2012-08-01

Single channel noise reduction has been well investigated and seems to have reached its limits in terms of speech intelligibility improvement, however, the quality of such schemes can still be advanced. This study tests to what extent novel model-based processing schemes might improve performance in particular for non-stationary noise conditions. Two prototype model-based algorithms, a speech-model-based, and a auditory-model-based algorithm were compared to a state-of-the-art non-parametric minimum statistics algorithm. A speech intelligibility test, preference rating, and listening effort scaling were performed. Additionally, three objective quality measures for the signal, background, and overall distortions were applied. For a better comparison of all algorithms, particular attention was given to the usage of the similar Wiener-based gain rule. The perceptual investigation was performed with fourteen hearing-impaired subjects. The results revealed that the non-parametric algorithm and the auditory model-based algorithm did not affect speech intelligibility, whereas the speech-model-based algorithm slightly decreased intelligibility. In terms of subjective quality, both model-based algorithms perform better than the unprocessed condition and the reference in particular for highly non-stationary noise environments. Data support the hypothesis that model-based algorithms are promising for improving performance in non-stationary noise conditions.

Inference in the age of big data: Future perspectives on neuroscience.

PubMed

Bzdok, Danilo; Yeo, B T Thomas

2017-07-15

Neuroscience is undergoing faster changes than ever before. Over 100 years our field qualitatively described and invasively manipulated single or few organisms to gain anatomical, physiological, and pharmacological insights. In the last 10 years neuroscience spawned quantitative datasets of unprecedented breadth (e.g., microanatomy, synaptic connections, and optogenetic brain-behavior assays) and size (e.g., cognition, brain imaging, and genetics). While growing data availability and information granularity have been amply discussed, we direct attention to a less explored question: How will the unprecedented data richness shape data analysis practices? Statistical reasoning is becoming more important to distill neurobiological knowledge from healthy and pathological brain measurements. We argue that large-scale data analysis will use more statistical models that are non-parametric, generative, and mixing frequentist and Bayesian aspects, while supplementing classical hypothesis testing with out-of-sample predictions. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

PHOXTRACK-a tool for interpreting comprehensive datasets of post-translational modifications of proteins.

PubMed

Weidner, Christopher; Fischer, Cornelius; Sauer, Sascha

2014-12-01

We introduce PHOXTRACK (PHOsphosite-X-TRacing Analysis of Causal Kinases), a user-friendly freely available software tool for analyzing large datasets of post-translational modifications of proteins, such as phosphorylation, which are commonly gained by mass spectrometry detection. In contrast to other currently applied data analysis approaches, PHOXTRACK uses full sets of quantitative proteomics data and applies non-parametric statistics to calculate whether defined kinase-specific sets of phosphosite sequences indicate statistically significant concordant differences between various biological conditions. PHOXTRACK is an efficient tool for extracting post-translational information of comprehensive proteomics datasets to decipher key regulatory proteins and to infer biologically relevant molecular pathways. PHOXTRACK will be maintained over the next years and is freely available as an online tool for non-commercial use at http://phoxtrack.molgen.mpg.de. Users will also find a tutorial at this Web site and can additionally give feedback at https://groups.google.com/d/forum/phoxtrack-discuss. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Testing averaged cosmology with type Ia supernovae and BAO data

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

Santos, B.; Alcaniz, J.S.; Coley, A.A.

An important problem in precision cosmology is the determination of the effects of averaging and backreaction on observational predictions, particularly in view of the wealth of new observational data and improved statistical techniques. In this paper, we discuss the observational viability of a class of averaged cosmologies which consist of a simple parametrized phenomenological two-scale backreaction model with decoupled spatial curvature parameters. We perform a Bayesian model selection analysis and find that this class of averaged phenomenological cosmological models is favored with respect to the standard ΛCDM cosmological scenario when a joint analysis of current SNe Ia and BAO datamore » is performed. In particular, the analysis provides observational evidence for non-trivial spatial curvature.« less

Parametric and non-parametric species delimitation methods result in the recognition of two new Neotropical woody bamboo species.

PubMed

Ruiz-Sanchez, Eduardo

2015-12-01

The Neotropical woody bamboo genus Otatea is one of five genera in the subtribe Guaduinae. Of the eight described Otatea species, seven are endemic to Mexico and one is also distributed in Central and South America. Otatea acuminata has the widest geographical distribution of the eight species, and two of its recently collected populations do not match the known species morphologically. Parametric and non-parametric methods were used to delimit the species in Otatea using five chloroplast markers, one nuclear marker, and morphological characters. The parametric coalescent method and the non-parametric analysis supported the recognition of two distinct evolutionary lineages. Molecular clock estimates were used to estimate divergence times in Otatea. The results for divergence time in Otatea estimated the origin of the speciation events from the Late Miocene to Late Pleistocene. The species delimitation analyses (parametric and non-parametric) identified that the two populations of O. acuminata from Chiapas and Hidalgo are from two separate evolutionary lineages and these new species have morphological characters that separate them from O. acuminata s.s. The geological activity of the Trans-Mexican Volcanic Belt and the Isthmus of Tehuantepec may have isolated populations and limited the gene flow between Otatea species, driving speciation. Based on the results found here, I describe Otatea rzedowskiorum and Otatea victoriae as two new species, morphologically different from O. acuminata. Copyright © 2015 Elsevier Inc. All rights reserved.

Cluster-level statistical inference in fMRI datasets: The unexpected behavior of random fields in high dimensions.

PubMed

Bansal, Ravi; Peterson, Bradley S

2018-06-01

Identifying regional effects of interest in MRI datasets usually entails testing a priori hypotheses across many thousands of brain voxels, requiring control for false positive findings in these multiple hypotheses testing. Recent studies have suggested that parametric statistical methods may have incorrectly modeled functional MRI data, thereby leading to higher false positive rates than their nominal rates. Nonparametric methods for statistical inference when conducting multiple statistical tests, in contrast, are thought to produce false positives at the nominal rate, which has thus led to the suggestion that previously reported studies should reanalyze their fMRI data using nonparametric tools. To understand better why parametric methods may yield excessive false positives, we assessed their performance when applied both to simulated datasets of 1D, 2D, and 3D Gaussian Random Fields (GRFs) and to 710 real-world, resting-state fMRI datasets. We showed that both the simulated 2D and 3D GRFs and the real-world data contain a small percentage (<6%) of very large clusters (on average 60 times larger than the average cluster size), which were not present in 1D GRFs. These unexpectedly large clusters were deemed statistically significant using parametric methods, leading to empirical familywise error rates (FWERs) as high as 65%: the high empirical FWERs were not a consequence of parametric methods failing to model spatial smoothness accurately, but rather of these very large clusters that are inherently present in smooth, high-dimensional random fields. In fact, when discounting these very large clusters, the empirical FWER for parametric methods was 3.24%. Furthermore, even an empirical FWER of 65% would yield on average less than one of those very large clusters in each brain-wide analysis. Nonparametric methods, in contrast, estimated distributions from those large clusters, and therefore, by construct rejected the large clusters as false positives at the nominal FWERs. Those rejected clusters were outlying values in the distribution of cluster size but cannot be distinguished from true positive findings without further analyses, including assessing whether fMRI signal in those regions correlates with other clinical, behavioral, or cognitive measures. Rejecting the large clusters, however, significantly reduced the statistical power of nonparametric methods in detecting true findings compared with parametric methods, which would have detected most true findings that are essential for making valid biological inferences in MRI data. Parametric analyses, in contrast, detected most true findings while generating relatively few false positives: on average, less than one of those very large clusters would be deemed a true finding in each brain-wide analysis. We therefore recommend the continued use of parametric methods that model nonstationary smoothness for cluster-level, familywise control of false positives, particularly when using a Cluster Defining Threshold of 2.5 or higher, and subsequently assessing rigorously the biological plausibility of the findings, even for large clusters. Finally, because nonparametric methods yielded a large reduction in statistical power to detect true positive findings, we conclude that the modest reduction in false positive findings that nonparametric analyses afford does not warrant a re-analysis of previously published fMRI studies using nonparametric techniques. Copyright © 2018 Elsevier Inc. All rights reserved.

The binned bispectrum estimator: template-based and non-parametric CMB non-Gaussianity searches

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

Bucher, Martin; Racine, Benjamin; Tent, Bartjan van, E-mail: bucher@apc.univ-paris7.fr, E-mail: benjar@uio.no, E-mail: vantent@th.u-psud.fr

2016-05-01

We describe the details of the binned bispectrum estimator as used for the official 2013 and 2015 analyses of the temperature and polarization CMB maps from the ESA Planck satellite. The defining aspect of this estimator is the determination of a map bispectrum (3-point correlation function) that has been binned in harmonic space. For a parametric determination of the non-Gaussianity in the map (the so-called f NL parameters), one takes the inner product of this binned bispectrum with theoretically motivated templates. However, as a complementary approach one can also smooth the binned bispectrum using a variable smoothing scale in ordermore » to suppress noise and make coherent features stand out above the noise. This allows one to look in a model-independent way for any statistically significant bispectral signal. This approach is useful for characterizing the bispectral shape of the galactic foreground emission, for which a theoretical prediction of the bispectral anisotropy is lacking, and for detecting a serendipitous primordial signal, for which a theoretical template has not yet been put forth. Both the template-based and the non-parametric approaches are described in this paper.« less

Tremor Detection Using Parametric and Non-Parametric Spectral Estimation Methods: A Comparison with Clinical Assessment

PubMed Central

Martinez Manzanera, Octavio; Elting, Jan Willem; van der Hoeven, Johannes H.; Maurits, Natasha M.

2016-01-01

In the clinic, tremor is diagnosed during a time-limited process in which patients are observed and the characteristics of tremor are visually assessed. For some tremor disorders, a more detailed analysis of these characteristics is needed. Accelerometry and electromyography can be used to obtain a better insight into tremor. Typically, routine clinical assessment of accelerometry and electromyography data involves visual inspection by clinicians and occasionally computational analysis to obtain objective characteristics of tremor. However, for some tremor disorders these characteristics may be different during daily activity. This variability in presentation between the clinic and daily life makes a differential diagnosis more difficult. A long-term recording of tremor by accelerometry and/or electromyography in the home environment could help to give a better insight into the tremor disorder. However, an evaluation of such recordings using routine clinical standards would take too much time. We evaluated a range of techniques that automatically detect tremor segments in accelerometer data, as accelerometer data is more easily obtained in the home environment than electromyography data. Time can be saved if clinicians only have to evaluate the tremor characteristics of segments that have been automatically detected in longer daily activity recordings. We tested four non-parametric methods and five parametric methods on clinical accelerometer data from 14 patients with different tremor disorders. The consensus between two clinicians regarding the presence or absence of tremor on 3943 segments of accelerometer data was employed as reference. The nine methods were tested against this reference to identify their optimal parameters. Non-parametric methods generally performed better than parametric methods on our dataset when optimal parameters were used. However, one parametric method, employing the high frequency content of the tremor bandwidth under consideration (High Freq) performed similarly to non-parametric methods, but had the highest recall values, suggesting that this method could be employed for automatic tremor detection. PMID:27258018

Discrimination of common Mediterranean plant species using field spectroradiometry

NASA Astrophysics Data System (ADS)

Manevski, Kiril; Manakos, Ioannis; Petropoulos, George P.; Kalaitzidis, Chariton

2011-12-01

Field spectroradiometry of land surface objects supports remote sensing analysis, facilitates the discrimination of vegetation species, and enhances the mapping efficiency. Especially in the Mediterranean, spectral discrimination of common vegetation types, such as phrygana and maquis species, remains a challenge. Both phrygana and maquis may be used as a direct indicator for grazing management, fire history and severity, and the state of the wider ecosystem equilibrium. This study aims to investigate the capability of field spectroradiometry supporting remote sensing analysis of the land cover of a characteristic Mediterranean area. Five common Mediterranean maquis and phrygana species were examined. Spectra acquisition was performed during an intensive field campaign deployed in spring 2010, supported by a novel platform MUFSPEM@MED (Mobile Unit for Field SPEctral Measurements at the MEDiterranean) for high canopy measurements. Parametric and non-parametric statistical tests have been applied to the continuum-removed reflectance of the species in the visible to shortwave infrared spectral range. Interpretation of the results indicated distinct discrimination between the studied species at specific spectral regions. Statistically significant wavelengths were principally found in both the visible and the near infrared regions of the electromagnetic spectrum. Spectral bands in the shortwave infrared demonstrated significant discrimination features for the examined species adapted to Mediterranean drought. All in all, results confirmed the prospect for a more accurate mapping of the species spatial distribution using remote sensing imagery coupled with in situ spectral information.

An Optimal Parameterization Framework for Infrasonic Tomography of the Stratospheric Winds Using Non-Local Sources

DOE PAGES

Blom, Philip Stephen; Marcillo, Omar Eduardo

2016-12-05

A method is developed to apply acoustic tomography methods to a localized network of infrasound arrays with intention of monitoring the atmosphere state in the region around the network using non-local sources without requiring knowledge of the precise source location or non-local atmosphere state. Closely spaced arrays provide a means to estimate phase velocities of signals that can provide limiting bounds on certain characteristics of the atmosphere. Larger spacing between such clusters provide a means to estimate celerity from propagation times along multiple unique stratospherically or thermospherically ducted propagation paths and compute more precise estimates of the atmosphere state. Inmore » order to avoid the commonly encountered complex, multimodal distributions for parametric atmosphere descriptions and to maximize the computational efficiency of the method, an optimal parametrization framework is constructed. This framework identifies the ideal combination of parameters for tomography studies in specific regions of the atmosphere and statistical model selection analysis shows that high quality corrections to the middle atmosphere winds can be obtained using as few as three parameters. Lastly, comparison of the resulting estimates for synthetic data sets shows qualitative agreement between the middle atmosphere winds and those estimated from infrasonic traveltime observations.« less

Bayesian non-parametric inference for stochastic epidemic models using Gaussian Processes.

PubMed

Xu, Xiaoguang; Kypraios, Theodore; O'Neill, Philip D

2016-10-01

This paper considers novel Bayesian non-parametric methods for stochastic epidemic models. Many standard modeling and data analysis methods use underlying assumptions (e.g. concerning the rate at which new cases of disease will occur) which are rarely challenged or tested in practice. To relax these assumptions, we develop a Bayesian non-parametric approach using Gaussian Processes, specifically to estimate the infection process. The methods are illustrated with both simulated and real data sets, the former illustrating that the methods can recover the true infection process quite well in practice, and the latter illustrating that the methods can be successfully applied in different settings. © The Author 2016. Published by Oxford University Press.

Describing spatial pattern in stream networks: A practical approach

USGS Publications Warehouse

Ganio, L.M.; Torgersen, C.E.; Gresswell, R.E.

2005-01-01

The shape and configuration of branched networks influence ecological patterns and processes. Recent investigations of network influences in riverine ecology stress the need to quantify spatial structure not only in a two-dimensional plane, but also in networks. An initial step in understanding data from stream networks is discerning non-random patterns along the network. On the other hand, data collected in the network may be spatially autocorrelated and thus not suitable for traditional statistical analyses. Here we provide a method that uses commercially available software to construct an empirical variogram to describe spatial pattern in the relative abundance of coastal cutthroat trout in headwater stream networks. We describe the mathematical and practical considerations involved in calculating a variogram using a non-Euclidean distance metric to incorporate the network pathway structure in the analysis of spatial variability, and use a non-parametric technique to ascertain if the pattern in the empirical variogram is non-random.

A geostatistical approach for describing spatial pattern in stream networks

USGS Publications Warehouse

Ganio, L.M.; Torgersen, C.E.; Gresswell, R.E.

2005-01-01

The shape and configuration of branched networks influence ecological patterns and processes. Recent investigations of network influences in riverine ecology stress the need to quantify spatial structure not only in a two-dimensional plane, but also in networks. An initial step in understanding data from stream networks is discerning non-random patterns along the network. On the other hand, data collected in the network may be spatially autocorrelated and thus not suitable for traditional statistical analyses. Here we provide a method that uses commercially available software to construct an empirical variogram to describe spatial pattern in the relative abundance of coastal cutthroat trout in headwater stream networks. We describe the mathematical and practical considerations involved in calculating a variogram using a non-Euclidean distance metric to incorporate the network pathway structure in the analysis of spatial variability, and use a non-parametric technique to ascertain if the pattern in the empirical variogram is non-random.

Comparing Pixel- and Object-Based Approaches in Effectively Classifying Wetland-Dominated Landscapes

PubMed Central

Berhane, Tedros M.; Lane, Charles R.; Wu, Qiusheng; Anenkhonov, Oleg A.; Chepinoga, Victor V.; Autrey, Bradley C.; Liu, Hongxing

2018-01-01

Wetland ecosystems straddle both terrestrial and aquatic habitats, performing many ecological functions directly and indirectly benefitting humans. However, global wetland losses are substantial. Satellite remote sensing and classification informs wise wetland management and monitoring. Both pixel- and object-based classification approaches using parametric and non-parametric algorithms may be effectively used in describing wetland structure and habitat, but which approach should one select? We conducted both pixel- and object-based image analyses (OBIA) using parametric (Iterative Self-Organizing Data Analysis Technique, ISODATA, and maximum likelihood, ML) and non-parametric (random forest, RF) approaches in the Barguzin Valley, a large wetland (~500 km2) in the Lake Baikal, Russia, drainage basin. Four Quickbird multispectral bands plus various spatial and spectral metrics (e.g., texture, Non-Differentiated Vegetation Index, slope, aspect, etc.) were analyzed using field-based regions of interest sampled to characterize an initial 18 ISODATA-based classes. Parsimoniously using a three-layer stack (Quickbird band 3, water ratio index (WRI), and mean texture) in the analyses resulted in the highest accuracy, 87.9% with pixel-based RF, followed by OBIA RF (segmentation scale 5, 84.6% overall accuracy), followed by pixel-based ML (83.9% overall accuracy). Increasing the predictors from three to five by adding Quickbird bands 2 and 4 decreased the pixel-based overall accuracy while increasing the OBIA RF accuracy to 90.4%. However, McNemar’s chi-square test confirmed no statistically significant difference in overall accuracy among the classifiers (pixel-based ML, RF, or object-based RF) for either the three- or five-layer analyses. Although potentially useful in some circumstances, the OBIA approach requires substantial resources and user input (such as segmentation scale selection—which was found to substantially affect overall accuracy). Hence, we conclude that pixel-based RF approaches are likely satisfactory for classifying wetland-dominated landscapes. PMID:29707381

Comparing Pixel- and Object-Based Approaches in Effectively Classifying Wetland-Dominated Landscapes.

PubMed

Berhane, Tedros M; Lane, Charles R; Wu, Qiusheng; Anenkhonov, Oleg A; Chepinoga, Victor V; Autrey, Bradley C; Liu, Hongxing

2018-01-01

Wetland ecosystems straddle both terrestrial and aquatic habitats, performing many ecological functions directly and indirectly benefitting humans. However, global wetland losses are substantial. Satellite remote sensing and classification informs wise wetland management and monitoring. Both pixel- and object-based classification approaches using parametric and non-parametric algorithms may be effectively used in describing wetland structure and habitat, but which approach should one select? We conducted both pixel- and object-based image analyses (OBIA) using parametric (Iterative Self-Organizing Data Analysis Technique, ISODATA, and maximum likelihood, ML) and non-parametric (random forest, RF) approaches in the Barguzin Valley, a large wetland (~500 km 2 ) in the Lake Baikal, Russia, drainage basin. Four Quickbird multispectral bands plus various spatial and spectral metrics (e.g., texture, Non-Differentiated Vegetation Index, slope, aspect, etc.) were analyzed using field-based regions of interest sampled to characterize an initial 18 ISODATA-based classes. Parsimoniously using a three-layer stack (Quickbird band 3, water ratio index (WRI), and mean texture) in the analyses resulted in the highest accuracy, 87.9% with pixel-based RF, followed by OBIA RF (segmentation scale 5, 84.6% overall accuracy), followed by pixel-based ML (83.9% overall accuracy). Increasing the predictors from three to five by adding Quickbird bands 2 and 4 decreased the pixel-based overall accuracy while increasing the OBIA RF accuracy to 90.4%. However, McNemar's chi-square test confirmed no statistically significant difference in overall accuracy among the classifiers (pixel-based ML, RF, or object-based RF) for either the three- or five-layer analyses. Although potentially useful in some circumstances, the OBIA approach requires substantial resources and user input (such as segmentation scale selection-which was found to substantially affect overall accuracy). Hence, we conclude that pixel-based RF approaches are likely satisfactory for classifying wetland-dominated landscapes.

Parasites as valuable stock markers for fisheries in Australasia, East Asia and the Pacific Islands.

PubMed

Lester, R J G; Moore, B R

2015-01-01

Over 30 studies in Australasia, East Asia and the Pacific Islands region have collected and analysed parasite data to determine the ranges of individual fish, many leading to conclusions about stock delineation. Parasites used as biological tags have included both those known to have long residence times in the fish and those thought to be relatively transient. In many cases the parasitological conclusions have been supported by other methods especially analysis of the chemical constituents of otoliths, and to a lesser extent, genetic data. In analysing parasite data, authors have applied multiple different statistical methodologies, including summary statistics, and univariate and multivariate approaches. Recently, a growing number of researchers have found non-parametric methods, such as analysis of similarities and cluster analysis, to be valuable. Future studies into the residence times, life cycles and geographical distributions of parasites together with more robust analytical methods will yield much important information to clarify stock structures in the area.

Effect of Injector Geometry on Atomization of a Liquid-Liquid Double Swirl Coaxial Injector using Non-Invasive Laser, Optical and X-ray Techniques

NASA Technical Reports Server (NTRS)

Radke, C. R.; Meyer, T. R.

2014-01-01

The spray characteristics of a Liquid-Liquid Double Swirl Coaxial Injector were studied using noninvasive Optical, Laser, and X-ray diagnostics. A parametric study of injector exit geometry demonstrated that spray breakup time, breakup type and sheet stability could be controlled with exit geometry. Phase Doppler Particle Analysis characterized droplet statistics and non-dimensional droplet parameters over a range of inlet conditions and for various fluids allowing for a study on the role of specific fluid properties in atomization. Further, x-ray radiographs allowed for investigations of sheet thickness and breakup length to be quantified for different recess exits and inlet pressures. Finally Computed Tomography scans revealed that the spray cone was distinctively non-uniform and comprised of several pockets of increased mass flux.

Statistical State Dynamics Based Study of the Role of Nonlinearity in the Maintenance of Turbulence in Couette Flow

NASA Astrophysics Data System (ADS)

Farrell, Brian; Ioannou, Petros; Nikolaidis, Marios-Andreas

2017-11-01

While linear non-normality underlies the mechanism of energy transfer from the externally driven flow to the perturbation field, nonlinearity is also known to play an essential role in sustaining turbulence. We report a study based on the statistical state dynamics of Couette flow turbulence with the goal of better understanding the role of nonlinearity in sustaining turbulence. The statistical state dynamics implementations used are ensemble closures at second order in a cumulant expansion of the Navier-Stokes equations in which the averaging operator is the streamwise mean. Two fundamentally non-normal mechanisms potentially contributing to maintaining the second cumulant are identified. These are essentially parametric perturbation growth arising from interaction of the perturbations with the fluctuating mean flow and transient growth of perturbations arising from nonlinear interaction between components of the perturbation field. By the method of selectively including these mechanisms parametric growth is found to maintain the perturbation field in the turbulent state while the more commonly invoked mechanism associated with transient growth of perturbations arising from scattering by nonlinear interaction is found to suppress perturbation variance. Funded by ERC Coturb Madrid Summer Program and NSF AGS-1246929.

Probabilistic analysis and fatigue damage assessment of offshore mooring system due to non-Gaussian bimodal tension processes

NASA Astrophysics Data System (ADS)

Chang, Anteng; Li, Huajun; Wang, Shuqing; Du, Junfeng

2017-08-01

Both wave-frequency (WF) and low-frequency (LF) components of mooring tension are in principle non-Gaussian due to nonlinearities in the dynamic system. This paper conducts a comprehensive investigation of applicable probability density functions (PDFs) of mooring tension amplitudes used to assess mooring-line fatigue damage via the spectral method. Short-term statistical characteristics of mooring-line tension responses are firstly investigated, in which the discrepancy arising from Gaussian approximation is revealed by comparing kurtosis and skewness coefficients. Several distribution functions based on present analytical spectral methods are selected to express the statistical distribution of the mooring-line tension amplitudes. Results indicate that the Gamma-type distribution and a linear combination of Dirlik and Tovo-Benasciutti formulas are suitable for separate WF and LF mooring tension components. A novel parametric method based on nonlinear transformations and stochastic optimization is then proposed to increase the effectiveness of mooring-line fatigue assessment due to non-Gaussian bimodal tension responses. Using time domain simulation as a benchmark, its accuracy is further validated using a numerical case study of a moored semi-submersible platform.

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.

Quantum state engineering of light with continuous-wave optical parametric oscillators.

PubMed

Morin, Olivier; Liu, Jianli; Huang, Kun; Barbosa, Felippe; Fabre, Claude; Laurat, Julien

2014-05-30

Engineering non-classical states of the electromagnetic field is a central quest for quantum optics(1,2). Beyond their fundamental significance, such states are indeed the resources for implementing various protocols, ranging from enhanced metrology to quantum communication and computing. A variety of devices can be used to generate non-classical states, such as single emitters, light-matter interfaces or non-linear systems(3). We focus here on the use of a continuous-wave optical parametric oscillator(3,4). This system is based on a non-linear χ(2) crystal inserted inside an optical cavity and it is now well-known as a very efficient source of non-classical light, such as single-mode or two-mode squeezed vacuum depending on the crystal phase matching. Squeezed vacuum is a Gaussian state as its quadrature distributions follow a Gaussian statistics. However, it has been shown that number of protocols require non-Gaussian states(5). Generating directly such states is a difficult task and would require strong χ(3) non-linearities. Another procedure, probabilistic but heralded, consists in using a measurement-induced non-linearity via a conditional preparation technique operated on Gaussian states. Here, we detail this generation protocol for two non-Gaussian states, the single-photon state and a superposition of coherent states, using two differently phase-matched parametric oscillators as primary resources. This technique enables achievement of a high fidelity with the targeted state and generation of the state in a well-controlled spatiotemporal mode.

Simulation-based sensitivity analysis for non-ignorably missing data.

PubMed

Yin, Peng; Shi, Jian Q

2017-01-01

Sensitivity analysis is popular in dealing with missing data problems particularly for non-ignorable missingness, where full-likelihood method cannot be adopted. It analyses how sensitively the conclusions (output) may depend on assumptions or parameters (input) about missing data, i.e. missing data mechanism. We call models with the problem of uncertainty sensitivity models. To make conventional sensitivity analysis more useful in practice we need to define some simple and interpretable statistical quantities to assess the sensitivity models and make evidence based analysis. We propose a novel approach in this paper on attempting to investigate the possibility of each missing data mechanism model assumption, by comparing the simulated datasets from various MNAR models with the observed data non-parametrically, using the K-nearest-neighbour distances. Some asymptotic theory has also been provided. A key step of this method is to plug in a plausibility evaluation system towards each sensitivity parameter, to select plausible values and reject unlikely values, instead of considering all proposed values of sensitivity parameters as in the conventional sensitivity analysis method. The method is generic and has been applied successfully to several specific models in this paper including meta-analysis model with publication bias, analysis of incomplete longitudinal data and mean estimation with non-ignorable missing data.

A comparative study between nonlinear regression and nonparametric approaches for modelling Phalaris paradoxa seedling emergence

USDA-ARS?s Scientific Manuscript database

Parametric non-linear regression (PNR) techniques commonly are used to develop weed seedling emergence models. Such techniques, however, require statistical assumptions that are difficult to meet. To examine and overcome these limitations, we compared PNR with a nonparametric estimation technique. F...

Location tests for biomarker studies: a comparison using simulations for the two-sample case.

PubMed

Scheinhardt, M O; Ziegler, A

2013-01-01

Gene, protein, or metabolite expression levels are often non-normally distributed, heavy tailed and contain outliers. Standard statistical approaches may fail as location tests in this situation. In three Monte-Carlo simulation studies, we aimed at comparing the type I error levels and empirical power of standard location tests and three adaptive tests [O'Gorman, Can J Stat 1997; 25: 269 -279; Keselman et al., Brit J Math Stat Psychol 2007; 60: 267- 293; Szymczak et al., Stat Med 2013; 32: 524 - 537] for a wide range of distributions. We simulated two-sample scenarios using the g-and-k-distribution family to systematically vary tail length and skewness with identical and varying variability between groups. All tests kept the type I error level when groups did not vary in their variability. The standard non-parametric U-test performed well in all simulated scenarios. It was outperformed by the two non-parametric adaptive methods in case of heavy tails or large skewness. Most tests did not keep the type I error level for skewed data in the case of heterogeneous variances. The standard U-test was a powerful and robust location test for most of the simulated scenarios except for very heavy tailed or heavy skewed data, and it is thus to be recommended except for these cases. The non-parametric adaptive tests were powerful for both normal and non-normal distributions under sample variance homogeneity. But when sample variances differed, they did not keep the type I error level. The parametric adaptive test lacks power for skewed and heavy tailed distributions.

Modeling and replicating statistical topology and evidence for CMB nonhomogeneity

PubMed Central

Agami, Sarit

2017-01-01

Under the banner of “big data,” the detection and classification of structure in extremely large, high-dimensional, data sets are two of the central statistical challenges of our times. Among the most intriguing new approaches to this challenge is “TDA,” or “topological data analysis,” one of the primary aims of which is providing nonmetric, but topologically informative, preanalyses of data which make later, more quantitative, analyses feasible. While TDA rests on strong mathematical foundations from topology, in applications, it has faced challenges due to difficulties in handling issues of statistical reliability and robustness, often leading to an inability to make scientific claims with verifiable levels of statistical confidence. We propose a methodology for the parametric representation, estimation, and replication of persistence diagrams, the main diagnostic tool of TDA. The power of the methodology lies in the fact that even if only one persistence diagram is available for analysis—the typical case for big data applications—the replications permit conventional statistical hypothesis testing. The methodology is conceptually simple and computationally practical, and provides a broadly effective statistical framework for persistence diagram TDA analysis. We demonstrate the basic ideas on a toy example, and the power of the parametric approach to TDA modeling in an analysis of cosmic microwave background (CMB) nonhomogeneity. PMID:29078301

The mechanism by which nonlinearity sustains turbulence in plane Couette flow

NASA Astrophysics Data System (ADS)

Nikolaidis, M.-A.; Farrell, B. F.; Ioannou, P. J.

2018-04-01

Turbulence in wall-bounded shear flow results from a synergistic interaction between linear non-normality and nonlinearity in which non-normal growth of a subset of perturbations configured to transfer energy from the externally forced component of the turbulent state to the perturbation component maintains the perturbation energy, while the subset of energy-transferring perturbations is replenished by nonlinearity. Although it is accepted that both linear non-normality mediated energy transfer from the forced component of the mean flow and nonlinear interactions among perturbations are required to maintain the turbulent state, the detailed physical mechanism by which these processes interact in maintaining turbulence has not been determined. In this work a statistical state dynamics based analysis is performed on turbulent Couette flow at R = 600 and a comparison to DNS is used to demonstrate that the perturbation component in Couette flow turbulence is replenished by a non-normality mediated parametric growth process in which the fluctuating streamwise mean flow has been adjusted to marginal Lyapunov stability. It is further shown that the alternative mechanism in which the subspace of non-normally growing perturbations is maintained directly by perturbation-perturbation nonlinearity does not contribute to maintaining the turbulent state. This work identifies parametric interaction between the fluctuating streamwise mean flow and the streamwise varying perturbations to be the mechanism of the nonlinear interaction maintaining the perturbation component of the turbulent state, and identifies the associated Lyapunov vectors with positive energetics as the structures of the perturbation subspace supporting the turbulence.

Parametric study for the optimization of ionic liquid pretreatment of corn stover

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

Papa, Gabriella; Feldman, Taya; Sale, Kenneth L.

A parametric study of the efficacy of the ionic liquid (IL) pretreatment (PT) of corn stover (CS) using 1-ethyl-3-methylimidazolium acetate ([C 2C 1Im][OAc] ) and cholinium lysinate ([Ch][Lys] ) was conducted. The impact of 50% and 15% biomass loading for milled and non-milled CS on IL-PT was evaluated, as well the impact of 20 and 5 mg enzyme/g glucan on saccharification efficiency. The glucose and xylose released were generated from 32 conditions – 2 ionic liquids (ILs), 2 temperatures, 2 particle sizes (S), 2 solid loadings, and 2 enzyme loadings. Statistical analysis indicates that sugar yields were correlated with lignin andmore » xylan removal and depends on the factors, where S did not explain variation in sugar yields. Both ILs were effective in pretreating large particle sized CS, without compromising sugar yields. The knowledge from material and energy balances is an essential step in directing optimization of sugar recovery at desirable process conditions.« less

How to Evaluate Phase Differences between Trial Groups in Ongoing Electrophysiological Signals

PubMed Central

VanRullen, Rufin

2016-01-01

A growing number of studies endeavor to reveal periodicities in sensory and cognitive functions, by comparing the distribution of ongoing (pre-stimulus) oscillatory phases between two (or more) trial groups reflecting distinct experimental outcomes. A systematic relation between the phase of spontaneous electrophysiological signals, before a stimulus is even presented, and the eventual result of sensory or cognitive processing for that stimulus, would be indicative of an intrinsic periodicity in the underlying neural process. Prior studies of phase-dependent perception have used a variety of analytical methods to measure and evaluate phase differences, and there is currently no established standard practice in this field. The present report intends to remediate this need, by systematically comparing the statistical power of various measures of “phase opposition” between two trial groups, in a number of real and simulated experimental situations. Seven measures were evaluated: one parametric test (circular Watson-Williams test), and three distinct measures of phase opposition (phase bifurcation index, phase opposition sum, and phase opposition product) combined with two procedures for non-parametric statistical testing (permutation, or a combination of z-score and permutation). While these are obviously not the only existing or conceivable measures, they have all been used in recent studies. All tested methods performed adequately on a previously published dataset (Busch et al., 2009). On a variety of artificially constructed datasets, no single measure was found to surpass all others, but instead the suitability of each measure was contingent on several experimental factors: the time, frequency, and depth of oscillatory phase modulation; the absolute and relative amplitudes of post-stimulus event-related potentials for the two trial groups; the absolute and relative trial numbers for the two groups; and the number of permutations used for non-parametric testing. The concurrent use of two phase opposition measures, the parametric Watson-Williams test and a non-parametric test based on summing inter-trial coherence values for the two trial groups, appears to provide the most satisfactory outcome in all situations tested. Matlab code is provided to automatically compute these phase opposition measures. PMID:27683543

Efficient statistically accurate algorithms for the Fokker-Planck equation in large dimensions

NASA Astrophysics Data System (ADS)

Chen, Nan; Majda, Andrew J.

2018-02-01

Solving the Fokker-Planck equation for high-dimensional complex turbulent dynamical systems is an important and practical issue. However, most traditional methods suffer from the curse of dimensionality and have difficulties in capturing the fat tailed highly intermittent probability density functions (PDFs) of complex systems in turbulence, neuroscience and excitable media. In this article, efficient statistically accurate algorithms are developed for solving both the transient and the equilibrium solutions of Fokker-Planck equations associated with high-dimensional nonlinear turbulent dynamical systems with conditional Gaussian structures. The algorithms involve a hybrid strategy that requires only a small number of ensembles. Here, a conditional Gaussian mixture in a high-dimensional subspace via an extremely efficient parametric method is combined with a judicious non-parametric Gaussian kernel density estimation in the remaining low-dimensional subspace. Particularly, the parametric method provides closed analytical formulae for determining the conditional Gaussian distributions in the high-dimensional subspace and is therefore computationally efficient and accurate. The full non-Gaussian PDF of the system is then given by a Gaussian mixture. Different from traditional particle methods, each conditional Gaussian distribution here covers a significant portion of the high-dimensional PDF. Therefore a small number of ensembles is sufficient to recover the full PDF, which overcomes the curse of dimensionality. Notably, the mixture distribution has significant skill in capturing the transient behavior with fat tails of the high-dimensional non-Gaussian PDFs, and this facilitates the algorithms in accurately describing the intermittency and extreme events in complex turbulent systems. It is shown in a stringent set of test problems that the method only requires an order of O (100) ensembles to successfully recover the highly non-Gaussian transient PDFs in up to 6 dimensions with only small errors.

Application of Transformations in Parametric Inference

ERIC Educational Resources Information Center

Brownstein, Naomi; Pensky, Marianna

2008-01-01

The objective of the present paper is to provide a simple approach to statistical inference using the method of transformations of variables. We demonstrate performance of this powerful tool on examples of constructions of various estimation procedures, hypothesis testing, Bayes analysis and statistical inference for the stress-strength systems.…

A Statistician's View of Upcoming Grand Challenges

NASA Astrophysics Data System (ADS)

Meng, Xiao Li

2010-01-01

In this session we have seen some snapshots of the broad spectrum of challenges, in this age of huge, complex, computer-intensive models, data, instruments,and questions. These challenges bridge astronomy at many wavelengths; basic physics; machine learning; -- and statistics. At one end of our spectrum, we think of 'compressing' the data with non-parametric methods. This raises the question of creating 'pseudo-replicas' of the data for uncertainty estimates. What would be involved in, e.g. boot-strap and related methods? Somewhere in the middle are these non-parametric methods for encapsulating the uncertainty information. At the far end, we find more model-based approaches, with the physics model embedded in the likelihood and analysis. The other distinctive problem is really the 'black-box' problem, where one has a complicated e.g. fundamental physics-based computer code, or 'black box', and one needs to know how changing the parameters at input -- due to uncertainties of any kind -- will map to changing the output. All of these connect to challenges in complexity of data and computation speed. Dr. Meng will highlight ways to 'cut corners' with advanced computational techniques, such as Parallel Tempering and Equal Energy methods. As well, there are cautionary tales of running automated analysis with real data -- where "30 sigma" outliers due to data artifacts can be more common than the astrophysical event of interest.

A two-point diagnostic for the H II galaxy Hubble diagram

NASA Astrophysics Data System (ADS)

Leaf, Kyle; Melia, Fulvio

2018-03-01

A previous analysis of starburst-dominated H II galaxies and H II regions has demonstrated a statistically significant preference for the Friedmann-Robertson-Walker cosmology with zero active mass, known as the Rh = ct universe, over Λcold dark matter (ΛCDM) and its related dark-matter parametrizations. In this paper, we employ a two-point diagnostic with these data to present a complementary statistical comparison of Rh = ct with Planck ΛCDM. Our two-point diagnostic compares, in a pairwise fashion, the difference between the distance modulus measured at two redshifts with that predicted by each cosmology. Our results support the conclusion drawn by a previous comparative analysis demonstrating that Rh = ct is statistically preferred over Planck ΛCDM. But we also find that the reported errors in the H II measurements may not be purely Gaussian, perhaps due to a partial contamination by non-Gaussian systematic effects. The use of H II galaxies and H II regions as standard candles may be improved even further with a better handling of the systematics in these sources.

Effects of the interaction range on structural phases of flexible polymers.

PubMed

Gross, J; Neuhaus, T; Vogel, T; Bachmann, M

2013-02-21

We systematically investigate how the range of interaction between non-bonded monomers influences the formation of structural phases of elastic, flexible polymers. Massively parallel replica-exchange simulations of a generic, coarse-grained model, performed partly on graphics processing units and in multiple-gaussian modified ensembles, pave the way for the construction of the structural phase diagram, parametrized by interaction range and temperature. Conformational transitions between gas-like, liquid, and diverse solid (pseudo) phases are identified by microcanonical statistical inflection-point analysis. We find evidence for finite-size effects that cause the crossover of "collapse" and "freezing" transitions for very short interaction ranges.

Ground-Based Telescope Parametric Cost Model

NASA Technical Reports Server (NTRS)

Stahl, H. Philip; Rowell, Ginger Holmes

2004-01-01

A parametric cost model for ground-based telescopes is developed using multi-variable statistical analysis, The model includes both engineering and performance parameters. While diameter continues to be the dominant cost driver, other significant factors include primary mirror radius of curvature and diffraction limited wavelength. The model includes an explicit factor for primary mirror segmentation and/or duplication (i.e.. multi-telescope phased-array systems). Additionally, single variable models based on aperture diameter are derived. This analysis indicates that recent mirror technology advances have indeed reduced the historical telescope cost curve.

Statistical Analysis of the Exchange Rate of Bitcoin.

PubMed

Chu, Jeffrey; Nadarajah, Saralees; Chan, Stephen

2015-01-01

Bitcoin, the first electronic payment system, is becoming a popular currency. We provide a statistical analysis of the log-returns of the exchange rate of Bitcoin versus the United States Dollar. Fifteen of the most popular parametric distributions in finance are fitted to the log-returns. The generalized hyperbolic distribution is shown to give the best fit. Predictions are given for future values of the exchange rate.

Evaluating sufficient similarity for drinking-water disinfection by-product (DBP) mixtures with bootstrap hypothesis test procedures.

PubMed

Feder, Paul I; Ma, Zhenxu J; Bull, Richard J; Teuschler, Linda K; Rice, Glenn

2009-01-01

In chemical mixtures risk assessment, the use of dose-response data developed for one mixture to estimate risk posed by a second mixture depends on whether the two mixtures are sufficiently similar. While evaluations of similarity may be made using qualitative judgments, this article uses nonparametric statistical methods based on the "bootstrap" resampling technique to address the question of similarity among mixtures of chemical disinfectant by-products (DBP) in drinking water. The bootstrap resampling technique is a general-purpose, computer-intensive approach to statistical inference that substitutes empirical sampling for theoretically based parametric mathematical modeling. Nonparametric, bootstrap-based inference involves fewer assumptions than parametric normal theory based inference. The bootstrap procedure is appropriate, at least in an asymptotic sense, whether or not the parametric, distributional assumptions hold, even approximately. The statistical analysis procedures in this article are initially illustrated with data from 5 water treatment plants (Schenck et al., 2009), and then extended using data developed from a study of 35 drinking-water utilities (U.S. EPA/AMWA, 1989), which permits inclusion of a greater number of water constituents and increased structure in the statistical models.

Power flow analysis of two coupled plates with arbitrary characteristics

NASA Technical Reports Server (NTRS)

Cuschieri, J. M.

1990-01-01

In the last progress report (Feb. 1988) some results were presented for a parametric analysis on the vibrational power flow between two coupled plate structures using the mobility power flow approach. The results reported then were for changes in the structural parameters of the two plates, but with the two plates identical in their structural characteristics. Herein, limitation is removed. The vibrational power input and output are evaluated for different values of the structural damping loss factor for the source and receiver plates. In performing this parametric analysis, the source plate characteristics are kept constant. The purpose of this parametric analysis is to determine the most critical parameters that influence the flow of vibrational power from the source plate to the receiver plate. In the case of the structural damping parametric analysis, the influence of changes in the source plate damping is also investigated. The results obtained from the mobility power flow approach are compared to results obtained using a statistical energy analysis (SEA) approach. The significance of the power flow results are discussed together with a discussion and a comparison between the SEA results and the mobility power flow results. Furthermore, the benefits derived from using the mobility power flow approach are examined.

Daylight exposure and the other predictors of burnout among nurses in a University Hospital.

PubMed

Alimoglu, Mustafa Kemal; Donmez, Levent

2005-07-01

The purpose of the study was to investigate if daylight exposure in work setting could be placed among the predictors of job burnout. The sample was composed of 141 nurses who work in Akdeniz University Hospital in Antalya, Turkey. All participants were asked to complete a personal data collection form, the Maslach Burnout Inventory, the Work Related Strain Inventory and the Work Satisfaction Questionnaire to collect data about their burnout, work-related stress (WRS) and job satisfaction (JS) levels in addition to personal characteristics. Descriptive statistics, parametric and non-parametric tests and correlation analysis were used in statistical analyses. Daylight exposure showed no direct effect on burnout but it was indirectly effective via WRS and JS. Exposure to daylight at least 3h a day was found to cause less stress and higher satisfaction at work. Suffering from sleep disorders, younger age, job-related health problems and educational level were found to have total or partial direct effects on burnout. Night shifts may lead to burnout via work related strain and working in inpatient services and dissatisfaction with annual income may be effective via job dissatisfaction. This study confirmed some established predictors of burnout and provided data on an unexplored area. Daylight exposure may be effective on job burnout.

Associations between host characteristics and antimicrobial resistance of Salmonella typhimurium.

PubMed

Ruddat, I; Tietze, E; Ziehm, D; Kreienbrock, L

2014-10-01

A collection of Salmonella Typhimurium isolates obtained from sporadic salmonellosis cases in humans from Lower Saxony, Germany between June 2008 and May 2010 was used to perform an exploratory risk-factor analysis on antimicrobial resistance (AMR) using comprehensive host information on sociodemographic attributes, medical history, food habits and animal contact. Multivariate resistance profiles of minimum inhibitory concentrations for 13 antimicrobial agents were analysed using a non-parametric approach with multifactorial models adjusted for phage types. Statistically significant associations were observed for consumption of antimicrobial agents, region type and three factors on egg-purchasing behaviour, indicating that besides antimicrobial use the proximity to other community members, health consciousness and other lifestyle-related attributes may play a role in the dissemination of resistances. Furthermore, a statistically significant increase in AMR from the first study year to the second year was observed.

Clinical competence of Guatemalan and Mexican physicians for family dysfunction management.

PubMed

Cabrera-Pivaral, Carlos Enrique; Orozco-Valerio, María de Jesús; Celis-de la Rosa, Alfredo; Covarrubias-Bermúdez, María de Los Ángeles; Zavala-González, Marco Antonio

2017-01-01

To evaluate the clinical competence of Mexican and Guatemalan physicians to management the family dysfunction. Cross comparative study in four care units first in Guadalajara, Mexico, and four in Guatemala, Guatemala, based on a purposeful sampling, involving 117 and 100 physicians, respectively. Clinical competence evaluated by validated instrument integrated for 187 items. Non-parametric descriptive and inferential statistical analysis was performed. The percentage of Mexican physicians with high clinical competence was 13.7%, medium 53%, low 24.8% and defined by random 8.5%. For the Guatemalan physicians'14% was high, average 63%, and 23% defined by random. There were no statistically significant differences between healthcare country units, but between the medium of Mexicans (0.55) and Guatemalans (0.55) (p = 0.02). The proportion of the high clinical competency of Mexican physicians' was as Guatemalans.

Parametric vs. non-parametric daily weather generator: validation and comparison

NASA Astrophysics Data System (ADS)

Dubrovsky, Martin

2016-04-01

As the climate models (GCMs and RCMs) fail to satisfactorily reproduce the real-world surface weather regime, various statistical methods are applied to downscale GCM/RCM outputs into site-specific weather series. The stochastic weather generators are among the most favourite downscaling methods capable to produce realistic (observed like) meteorological inputs for agrological, hydrological and other impact models used in assessing sensitivity of various ecosystems to climate change/variability. To name their advantages, the generators may (i) produce arbitrarily long multi-variate synthetic weather series representing both present and changed climates (in the latter case, the generators are commonly modified by GCM/RCM-based climate change scenarios), (ii) be run in various time steps and for multiple weather variables (the generators reproduce the correlations among variables), (iii) be interpolated (and run also for sites where no weather data are available to calibrate the generator). This contribution will compare two stochastic daily weather generators in terms of their ability to reproduce various features of the daily weather series. M&Rfi is a parametric generator: Markov chain model is used to model precipitation occurrence, precipitation amount is modelled by the Gamma distribution, and the 1st order autoregressive model is used to generate non-precipitation surface weather variables. The non-parametric GoMeZ generator is based on the nearest neighbours resampling technique making no assumption on the distribution of the variables being generated. Various settings of both weather generators will be assumed in the present validation tests. The generators will be validated in terms of (a) extreme temperature and precipitation characteristics (annual and 30 years extremes and maxima of duration of hot/cold/dry/wet spells); (b) selected validation statistics developed within the frame of VALUE project. The tests will be based on observational weather series from several European stations available from the ECA&D database.

Parametric distribution approach for flow availability in small hydro potential analysis

NASA Astrophysics Data System (ADS)

Abdullah, Samizee; Basri, Mohd Juhari Mat; Jamaluddin, Zahrul Zamri; Azrulhisham, Engku Ahmad; Othman, Jamel

2016-10-01

Small hydro system is one of the important sources of renewable energy and it has been recognized worldwide as clean energy sources. Small hydropower generation system uses the potential energy in flowing water to produce electricity is often questionable due to inconsistent and intermittent of power generated. Potential analysis of small hydro system which is mainly dependent on the availability of water requires the knowledge of water flow or stream flow distribution. This paper presented the possibility of applying Pearson system for stream flow availability distribution approximation in the small hydro system. By considering the stochastic nature of stream flow, the Pearson parametric distribution approximation was computed based on the significant characteristic of Pearson system applying direct correlation between the first four statistical moments of the distribution. The advantage of applying various statistical moments in small hydro potential analysis will have the ability to analyze the variation shapes of stream flow distribution.

Nonparametric functional data estimation applied to ozone data: prediction and extreme value analysis.

PubMed

Quintela-del-Río, Alejandro; Francisco-Fernández, Mario

2011-02-01

The study of extreme values and prediction of ozone data is an important topic of research when dealing with environmental problems. Classical extreme value theory is usually used in air-pollution studies. It consists in fitting a parametric generalised extreme value (GEV) distribution to a data set of extreme values, and using the estimated distribution to compute return levels and other quantities of interest. Here, we propose to estimate these values using nonparametric functional data methods. Functional data analysis is a relatively new statistical methodology that generally deals with data consisting of curves or multi-dimensional variables. In this paper, we use this technique, jointly with nonparametric curve estimation, to provide alternatives to the usual parametric statistical tools. The nonparametric estimators are applied to real samples of maximum ozone values obtained from several monitoring stations belonging to the Automatic Urban and Rural Network (AURN) in the UK. The results show that nonparametric estimators work satisfactorily, outperforming the behaviour of classical parametric estimators. Functional data analysis is also used to predict stratospheric ozone concentrations. We show an application, using the data set of mean monthly ozone concentrations in Arosa, Switzerland, and the results are compared with those obtained by classical time series (ARIMA) analysis. Copyright © 2010 Elsevier Ltd. All rights reserved.

Learning Patterns as Criterion for Forming Work Groups in 3D Simulation Learning Environments

ERIC Educational Resources Information Center

Maria Cela-Ranilla, Jose; Molías, Luis Marqués; Cervera, Mercè Gisbert

2016-01-01

This study analyzes the relationship between the use of learning patterns as a grouping criterion to develop learning activities in the 3D simulation environment at University. Participants included 72 Spanish students from the Education and Marketing disciplines. Descriptive statistics and non-parametric tests were conducted. The process was…

Non-parametric early seizure detection in an animal model of temporal lobe epilepsy

NASA Astrophysics Data System (ADS)

Talathi, Sachin S.; Hwang, Dong-Uk; Spano, Mark L.; Simonotto, Jennifer; Furman, Michael D.; Myers, Stephen M.; Winters, Jason T.; Ditto, William L.; Carney, Paul R.

2008-03-01

The performance of five non-parametric, univariate seizure detection schemes (embedding delay, Hurst scale, wavelet scale, nonlinear autocorrelation and variance energy) were evaluated as a function of the sampling rate of EEG recordings, the electrode types used for EEG acquisition, and the spatial location of the EEG electrodes in order to determine the applicability of the measures in real-time closed-loop seizure intervention. The criteria chosen for evaluating the performance were high statistical robustness (as determined through the sensitivity and the specificity of a given measure in detecting a seizure) and the lag in seizure detection with respect to the seizure onset time (as determined by visual inspection of the EEG signal by a trained epileptologist). An optimality index was designed to evaluate the overall performance of each measure. For the EEG data recorded with microwire electrode array at a sampling rate of 12 kHz, the wavelet scale measure exhibited better overall performance in terms of its ability to detect a seizure with high optimality index value and high statistics in terms of sensitivity and specificity.

Measurement of the photon statistics and the noise figure of a fiber-optic parametric amplifier.

PubMed

Voss, Paul L; Tang, Renyong; Kumar, Prem

2003-04-01

We report measurement of the noise statistics of spontaneous parametric fluorescence in a fiber parametric amplifier with single-mode, single-photon resolution. We employ optical homodyne tomography for this purpose, which also provides a self-calibrating measurement of the noise figure of the amplifier. The measured photon statistics agree with quantum-mechanical predictions, and the amplifier's noise figure is found to be almost quantum limited.

The linear transformation model with frailties for the analysis of item response times.

PubMed

Wang, Chun; Chang, Hua-Hua; Douglas, Jeffrey A

2013-02-01

The item response times (RTs) collected from computerized testing represent an underutilized source of information about items and examinees. In addition to knowing the examinees' responses to each item, we can investigate the amount of time examinees spend on each item. In this paper, we propose a semi-parametric model for RTs, the linear transformation model with a latent speed covariate, which combines the flexibility of non-parametric modelling and the brevity as well as interpretability of parametric modelling. In this new model, the RTs, after some non-parametric monotone transformation, become a linear model with latent speed as covariate plus an error term. The distribution of the error term implicitly defines the relationship between the RT and examinees' latent speeds; whereas the non-parametric transformation is able to describe various shapes of RT distributions. The linear transformation model represents a rich family of models that includes the Cox proportional hazards model, the Box-Cox normal model, and many other models as special cases. This new model is embedded in a hierarchical framework so that both RTs and responses are modelled simultaneously. A two-stage estimation method is proposed. In the first stage, the Markov chain Monte Carlo method is employed to estimate the parametric part of the model. In the second stage, an estimating equation method with a recursive algorithm is adopted to estimate the non-parametric transformation. Applicability of the new model is demonstrated with a simulation study and a real data application. Finally, methods to evaluate the model fit are suggested. © 2012 The British Psychological Society.

Parametric modelling of cost data in medical studies.

PubMed

Nixon, R M; Thompson, S G

2004-04-30

The cost of medical resources used is often recorded for each patient in clinical studies in order to inform decision-making. Although cost data are generally skewed to the right, interest is in making inferences about the population mean cost. Common methods for non-normal data, such as data transformation, assuming asymptotic normality of the sample mean or non-parametric bootstrapping, are not ideal. This paper describes possible parametric models for analysing cost data. Four example data sets are considered, which have different sample sizes and degrees of skewness. Normal, gamma, log-normal, and log-logistic distributions are fitted, together with three-parameter versions of the latter three distributions. Maximum likelihood estimates of the population mean are found; confidence intervals are derived by a parametric BC(a) bootstrap and checked by MCMC methods. Differences between model fits and inferences are explored.Skewed parametric distributions fit cost data better than the normal distribution, and should in principle be preferred for estimating the population mean cost. However for some data sets, we find that models that fit badly can give similar inferences to those that fit well. Conversely, particularly when sample sizes are not large, different parametric models that fit the data equally well can lead to substantially different inferences. We conclude that inferences are sensitive to choice of statistical model, which itself can remain uncertain unless there is enough data to model the tail of the distribution accurately. Investigating the sensitivity of conclusions to choice of model should thus be an essential component of analysing cost data in practice. Copyright 2004 John Wiley & Sons, Ltd.

Improving mass-univariate analysis of neuroimaging data by modelling important unknown covariates: Application to Epigenome-Wide Association Studies.

PubMed

Guillaume, Bryan; Wang, Changqing; Poh, Joann; Shen, Mo Jun; Ong, Mei Lyn; Tan, Pei Fang; Karnani, Neerja; Meaney, Michael; Qiu, Anqi

2018-06-01

Statistical inference on neuroimaging data is often conducted using a mass-univariate model, equivalent to fitting a linear model at every voxel with a known set of covariates. Due to the large number of linear models, it is challenging to check if the selection of covariates is appropriate and to modify this selection adequately. The use of standard diagnostics, such as residual plotting, is clearly not practical for neuroimaging data. However, the selection of covariates is crucial for linear regression to ensure valid statistical inference. In particular, the mean model of regression needs to be reasonably well specified. Unfortunately, this issue is often overlooked in the field of neuroimaging. This study aims to adopt the existing Confounder Adjusted Testing and Estimation (CATE) approach and to extend it for use with neuroimaging data. We propose a modification of CATE that can yield valid statistical inferences using Principal Component Analysis (PCA) estimators instead of Maximum Likelihood (ML) estimators. We then propose a non-parametric hypothesis testing procedure that can improve upon parametric testing. Monte Carlo simulations show that the modification of CATE allows for more accurate modelling of neuroimaging data and can in turn yield a better control of False Positive Rate (FPR) and Family-Wise Error Rate (FWER). We demonstrate its application to an Epigenome-Wide Association Study (EWAS) on neonatal brain imaging and umbilical cord DNA methylation data obtained as part of a longitudinal cohort study. Software for this CATE study is freely available at http://www.bioeng.nus.edu.sg/cfa/Imaging_Genetics2.html. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.

Bruxism and dental implant failures: a multilevel mixed effects parametric survival analysis approach.

PubMed

Chrcanovic, B R; Kisch, J; Albrektsson, T; Wennerberg, A

2016-11-01

Recent studies have suggested that the insertion of dental implants in patients being diagnosed with bruxism negatively affected the implant failure rates. The aim of the present study was to investigate the association between the bruxism and the risk of dental implant failure. This retrospective study is based on 2670 patients who received 10 096 implants at one specialist clinic. Implant- and patient-related data were collected. Descriptive statistics were used to describe the patients and implants. Multilevel mixed effects parametric survival analysis was used to test the association between bruxism and risk of implant failure adjusting for several potential confounders. Criteria from a recent international consensus (Lobbezoo et al., J Oral Rehabil, 40, 2013, 2) and from the International Classification of Sleep Disorders (International classification of sleep disorders, revised: diagnostic and coding manual, American Academy of Sleep Medicine, Chicago, 2014) were used to define and diagnose the condition. The number of implants with information available for all variables totalled 3549, placed in 994 patients, with 179 implants reported as failures. The implant failure rates were 13·0% (24/185) for bruxers and 4·6% (155/3364) for non-bruxers (P < 0·001). The statistical model showed that bruxism was a statistically significantly risk factor to implant failure (HR 3·396; 95% CI 1·314, 8·777; P = 0·012), as well as implant length, implant diameter, implant surface, bone quantity D in relation to quantity A, bone quality 4 in relation to quality 1 (Lekholm and Zarb classification), smoking and the intake of proton pump inhibitors. It is suggested that the bruxism may be associated with an increased risk of dental implant failure. © 2016 John Wiley & Sons Ltd.

Pilot study for supervised target detection applied to spatially registered multiparametric MRI in order to non-invasively score prostate cancer.

PubMed

Mayer, Rulon; Simone, Charles B; Skinner, William; Turkbey, Baris; Choykey, Peter

2018-03-01

Gleason Score (GS) is a validated predictor of prostate cancer (PCa) disease progression and outcomes. GS from invasive needle biopsies suffers from significant inter-observer variability and possible sampling error, leading to underestimating disease severity ("underscoring") and can result in possible complications. A robust non-invasive image-based approach is, therefore, needed. Use spatially registered multi-parametric MRI (MP-MRI), signatures, and supervised target detection algorithms (STDA) to non-invasively GS PCa at the voxel level. This study retrospectively analyzed 26 MP-MRI from The Cancer Imaging Archive. The MP-MRI (T2, Diffusion Weighted, Dynamic Contrast Enhanced) were spatially registered to each other, combined into stacks, and stitched together to form hypercubes. Multi-parametric (or multi-spectral) signatures derived from a training set of registered MP-MRI were transformed using statistics-based Whitening-Dewhitening (WD). Transformed signatures were inserted into STDA (having conical decision surfaces) applied to registered MP-MRI determined the tumor GS. The MRI-derived GS was quantitatively compared to the pathologist's assessment of the histology of sectioned whole mount prostates from patients who underwent radical prostatectomy. In addition, a meta-analysis of 17 studies of needle biopsy determined GS with confusion matrices and was compared to the MRI-determined GS. STDA and histology determined GS are highly correlated (R = 0.86, p < 0.02). STDA more accurately determined GS and reduced GS underscoring of PCa relative to needle biopsy as summarized by meta-analysis (p < 0.05). This pilot study found registered MP-MRI, STDA, and WD transforms of signatures shows promise in non-invasively GS PCa and reducing underscoring with high spatial resolution. Copyright © 2018 Elsevier Ltd. All rights reserved.

Parametric and non-parametric modeling of short-term synaptic plasticity. Part I: computational study

PubMed Central

Marmarelis, Vasilis Z.; Berger, Theodore W.

2009-01-01

Parametric and non-parametric modeling methods are combined to study the short-term plasticity (STP) of synapses in the central nervous system (CNS). The nonlinear dynamics of STP are modeled by means: (1) previously proposed parametric models based on mechanistic hypotheses and/or specific dynamical processes, and (2) non-parametric models (in the form of Volterra kernels) that transforms the presynaptic signals into postsynaptic signals. In order to synergistically use the two approaches, we estimate the Volterra kernels of the parametric models of STP for four types of synapses using synthetic broadband input–output data. Results show that the non-parametric models accurately and efficiently replicate the input–output transformations of the parametric models. Volterra kernels provide a general and quantitative representation of the STP. PMID:18506609

Localized N20 Component of Somatosensory Evoked Magnetic Fields in Frontoparietal Brain Tumor Patients Using Noise-Normalized Approaches.

PubMed

Elaina, Nor Safira; Malik, Aamir Saeed; Shams, Wafaa Khazaal; Badruddin, Nasreen; Abdullah, Jafri Malin; Reza, Mohammad Faruque

2018-06-01

To localize sensorimotor cortical activation in 10 patients with frontoparietal tumors using quantitative magnetoencephalography (MEG) with noise-normalized approaches. Somatosensory evoked magnetic fields (SEFs) were elicited in 10 patients with somatosensory tumors and in 10 control participants using electrical stimulation of the median nerve via the right and left wrists. We localized the N20m component of the SEFs using dynamic statistical parametric mapping (dSPM) and standardized low-resolution brain electromagnetic tomography (sLORETA) combined with 3D magnetic resonance imaging (MRI). The obtained coordinates were compared between groups. Finally, we statistically evaluated the N20m parameters across hemispheres using non-parametric statistical tests. The N20m sources were accurately localized to Brodmann area 3b in all members of the control group and in seven of the patients; however, the sources were shifted in three patients relative to locations outside the primary somatosensory cortex (SI). Compared with the affected (tumor) hemispheres in the patient group, N20m amplitudes and the strengths of the current sources were significantly lower in the unaffected hemispheres and in both hemispheres of the control group. These results were consistent for both dSPM and sLORETA approaches. Tumors in the sensorimotor cortex lead to cortical functional reorganization and an increase in N20m amplitude and current-source strengths. Noise-normalized approaches for MEG analysis that are integrated with MRI show accurate and reliable localization of sensorimotor function.

Trans-dimensional inversion of microtremor array dispersion data with hierarchical autoregressive error models

NASA Astrophysics Data System (ADS)

Dettmer, Jan; Molnar, Sheri; Steininger, Gavin; Dosso, Stan E.; Cassidy, John F.

2012-02-01

This paper applies a general trans-dimensional Bayesian inference methodology and hierarchical autoregressive data-error models to the inversion of microtremor array dispersion data for shear wave velocity (vs) structure. This approach accounts for the limited knowledge of the optimal earth model parametrization (e.g. the number of layers in the vs profile) and of the data-error statistics in the resulting vs parameter uncertainty estimates. The assumed earth model parametrization influences estimates of parameter values and uncertainties due to different parametrizations leading to different ranges of data predictions. The support of the data for a particular model is often non-unique and several parametrizations may be supported. A trans-dimensional formulation accounts for this non-uniqueness by including a model-indexing parameter as an unknown so that groups of models (identified by the indexing parameter) are considered in the results. The earth model is parametrized in terms of a partition model with interfaces given over a depth-range of interest. In this work, the number of interfaces (layers) in the partition model represents the trans-dimensional model indexing. In addition, serial data-error correlations are addressed by augmenting the geophysical forward model with a hierarchical autoregressive error model that can account for a wide range of error processes with a small number of parameters. Hence, the limited knowledge about the true statistical distribution of data errors is also accounted for in the earth model parameter estimates, resulting in more realistic uncertainties and parameter values. Hierarchical autoregressive error models do not rely on point estimates of the model vector to estimate data-error statistics, and have no requirement for computing the inverse or determinant of a data-error covariance matrix. This approach is particularly useful for trans-dimensional inverse problems, as point estimates may not be representative of the state space that spans multiple subspaces of different dimensionalities. The order of the autoregressive process required to fit the data is determined here by posterior residual-sample examination and statistical tests. Inference for earth model parameters is carried out on the trans-dimensional posterior probability distribution by considering ensembles of parameter vectors. In particular, vs uncertainty estimates are obtained by marginalizing the trans-dimensional posterior distribution in terms of vs-profile marginal distributions. The methodology is applied to microtremor array dispersion data collected at two sites with significantly different geology in British Columbia, Canada. At both sites, results show excellent agreement with estimates from invasive measurements.

Statistical Analysis of the Exchange Rate of Bitcoin

PubMed Central

Chu, Jeffrey; Nadarajah, Saralees; Chan, Stephen

2015-01-01

Bitcoin, the first electronic payment system, is becoming a popular currency. We provide a statistical analysis of the log-returns of the exchange rate of Bitcoin versus the United States Dollar. Fifteen of the most popular parametric distributions in finance are fitted to the log-returns. The generalized hyperbolic distribution is shown to give the best fit. Predictions are given for future values of the exchange rate. PMID:26222702

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…

Recent advances in parametric neuroreceptor mapping with dynamic PET: basic concepts and graphical analyses.

PubMed

Seo, Seongho; Kim, Su Jin; Lee, Dong Soo; Lee, Jae Sung

2014-10-01

Tracer kinetic modeling in dynamic positron emission tomography (PET) has been widely used to investigate the characteristic distribution patterns or dysfunctions of neuroreceptors in brain diseases. Its practical goal has progressed from regional data quantification to parametric mapping that produces images of kinetic-model parameters by fully exploiting the spatiotemporal information in dynamic PET data. Graphical analysis (GA) is a major parametric mapping technique that is independent on any compartmental model configuration, robust to noise, and computationally efficient. In this paper, we provide an overview of recent advances in the parametric mapping of neuroreceptor binding based on GA methods. The associated basic concepts in tracer kinetic modeling are presented, including commonly-used compartment models and major parameters of interest. Technical details of GA approaches for reversible and irreversible radioligands are described, considering both plasma input and reference tissue input models. Their statistical properties are discussed in view of parametric imaging.

Application of artificial neural network to fMRI regression analysis.

PubMed

Misaki, Masaya; Miyauchi, Satoru

2006-01-15

We used an artificial neural network (ANN) to detect correlations between event sequences and fMRI (functional magnetic resonance imaging) signals. The layered feed-forward neural network, given a series of events as inputs and the fMRI signal as a supervised signal, performed a non-linear regression analysis. This type of ANN is capable of approximating any continuous function, and thus this analysis method can detect any fMRI signals that correlated with corresponding events. Because of the flexible nature of ANNs, fitting to autocorrelation noise is a problem in fMRI analyses. We avoided this problem by using cross-validation and an early stopping procedure. The results showed that the ANN could detect various responses with different time courses. The simulation analysis also indicated an additional advantage of ANN over non-parametric methods in detecting parametrically modulated responses, i.e., it can detect various types of parametric modulations without a priori assumptions. The ANN regression analysis is therefore beneficial for exploratory fMRI analyses in detecting continuous changes in responses modulated by changes in input values.

A multi-instrument non-parametric reconstruction of the electron pressure profile in the galaxy cluster CLJ1226.9+3332

NASA Astrophysics Data System (ADS)

Romero, C.; McWilliam, M.; Macías-Pérez, J.-F.; Adam, R.; Ade, P.; André, P.; Aussel, H.; Beelen, A.; Benoît, A.; Bideaud, A.; Billot, N.; Bourrion, O.; Calvo, M.; Catalano, A.; Coiffard, G.; Comis, B.; de Petris, M.; Désert, F.-X.; Doyle, S.; Goupy, J.; Kramer, C.; Lagache, G.; Leclercq, S.; Lestrade, J.-F.; Mauskopf, P.; Mayet, F.; Monfardini, A.; Pascale, E.; Perotto, L.; Pisano, G.; Ponthieu, N.; Revéret, V.; Ritacco, A.; Roussel, H.; Ruppin, F.; Schuster, K.; Sievers, A.; Triqueneaux, S.; Tucker, C.; Zylka, R.

2018-04-01

Context. In the past decade, sensitive, resolved Sunyaev-Zel'dovich (SZ) studies of galaxy clusters have become common. Whereas many previous SZ studies have parameterized the pressure profiles of galaxy clusters, non-parametric reconstructions will provide insights into the thermodynamic state of the intracluster medium. Aim. We seek to recover the non-parametric pressure profiles of the high redshift (z = 0.89) galaxy cluster CLJ 1226.9+3332 as inferred from SZ data from the MUSTANG, NIKA, Bolocam, and Planck instruments, which all probe different angular scales. Methods: Our non-parametric algorithm makes use of logarithmic interpolation, which under the assumption of ellipsoidal symmetry is analytically integrable. For MUSTANG, NIKA, and Bolocam we derive a non-parametric pressure profile independently and find good agreement among the instruments. In particular, we find that the non-parametric profiles are consistent with a fitted generalized Navaro-Frenk-White (gNFW) profile. Given the ability of Planck to constrain the total signal, we include a prior on the integrated Compton Y parameter as determined by Planck. Results: For a given instrument, constraints on the pressure profile diminish rapidly beyond the field of view. The overlap in spatial scales probed by these four datasets is therefore critical in checking for consistency between instruments. By using multiple instruments, our analysis of CLJ 1226.9+3332 covers a large radial range, from the central regions to the cluster outskirts: 0.05 R500 < r < 1.1 R500. This is a wider range of spatial scales than is typically recovered by SZ instruments. Similar analyses will be possible with the new generation of SZ instruments such as NIKA2 and MUSTANG2.

A comparison of confidence/credible interval methods for the area under the ROC curve for continuous diagnostic tests with small sample size.

PubMed

Feng, Dai; Cortese, Giuliana; Baumgartner, Richard

2017-12-01

The receiver operating characteristic (ROC) curve is frequently used as a measure of accuracy of continuous markers in diagnostic tests. The area under the ROC curve (AUC) is arguably the most widely used summary index for the ROC curve. Although the small sample size scenario is common in medical tests, a comprehensive study of small sample size properties of various methods for the construction of the confidence/credible interval (CI) for the AUC has been by and large missing in the literature. In this paper, we describe and compare 29 non-parametric and parametric methods for the construction of the CI for the AUC when the number of available observations is small. The methods considered include not only those that have been widely adopted, but also those that have been less frequently mentioned or, to our knowledge, never applied to the AUC context. To compare different methods, we carried out a simulation study with data generated from binormal models with equal and unequal variances and from exponential models with various parameters and with equal and unequal small sample sizes. We found that the larger the true AUC value and the smaller the sample size, the larger the discrepancy among the results of different approaches. When the model is correctly specified, the parametric approaches tend to outperform the non-parametric ones. Moreover, in the non-parametric domain, we found that a method based on the Mann-Whitney statistic is in general superior to the others. We further elucidate potential issues and provide possible solutions to along with general guidance on the CI construction for the AUC when the sample size is small. Finally, we illustrate the utility of different methods through real life examples.

Electrical Evaluation of RCA MWS5001D Random Access Memory, Volume 4, Appendix C

NASA Technical Reports Server (NTRS)

Klute, A.

1979-01-01

The electrical characterization and qualification test results are presented for the RCA MWS5001D random access memory. The tests included functional tests, AC and DC parametric tests, AC parametric worst-case pattern selection test, determination of worst-case transition for setup and hold times, and a series of schmoo plots. Statistical analysis data is supplied along with write pulse width, read cycle time, write cycle time, and chip enable time data.

Multivariable Parametric Cost Model for Ground Optical Telescope Assembly

NASA Technical Reports Server (NTRS)

Stahl, H. Philip; Rowell, Ginger Holmes; Reese, Gayle; Byberg, Alicia

2005-01-01

A parametric cost model for ground-based telescopes is developed using multivariable statistical analysis of both engineering and performance parameters. While diameter continues to be the dominant cost driver, diffraction-limited wavelength is found to be a secondary driver. Other parameters such as radius of curvature are examined. The model includes an explicit factor for primary mirror segmentation and/or duplication (i.e., multi-telescope phased-array systems). Additionally, single variable models Based on aperture diameter are derived.

[Changes in cerebrospinal fluid in patients with tuberculosis of the central nervous system].

PubMed

Jedrychowski, Michał; Garlicki, Aleksander

2008-01-01

The aim of the study was to analyze the parameters of the cerebrospinal fluid in patients with tuberculosis of the central nervous system confirmed by culture or molecular methods, in comparison to patients without such confirmation. The analysis of medical documentation of 13 patients with CNS tuberculosis, 10 male and 3 female who were hospitalized at the Clinic of Infectious Diseases in Kraków in years 2001-2006 was performed. Following parameters of the cerebrospinal fluid were taken into account in both groups of patients: cytologic analysis, protein, glucose and chloride concentration. Statistical analysis was done using the non-parametric Mann-Whitney U test. The only parameter for which statistically significant difference between the two groups of patients was found was the level of glucose in CSF (p<0.05). Lower glucose concentration was observed in the group with etiologically confirmed CNS tuberculosis. Moreover additional localisation of tuberculosis was observed in this group of patients. Introduction of the molecular biology methods in diagnosis allowed to detect the etiologic factor more often.

The influence of vegetation height heterogeneity on forest and woodland bird species richness across the United States.

PubMed

Huang, Qiongyu; Swatantran, Anu; Dubayah, Ralph; Goetz, Scott J

2014-01-01

Avian diversity is under increasing pressures. It is thus critical to understand the ecological variables that contribute to large scale spatial distribution of avian species diversity. Traditionally, studies have relied primarily on two-dimensional habitat structure to model broad scale species richness. Vegetation vertical structure is increasingly used at local scales. However, the spatial arrangement of vegetation height has never been taken into consideration. Our goal was to examine the efficacies of three-dimensional forest structure, particularly the spatial heterogeneity of vegetation height in improving avian richness models across forested ecoregions in the U.S. We developed novel habitat metrics to characterize the spatial arrangement of vegetation height using the National Biomass and Carbon Dataset for the year 2000 (NBCD). The height-structured metrics were compared with other habitat metrics for statistical association with richness of three forest breeding bird guilds across Breeding Bird Survey (BBS) routes: a broadly grouped woodland guild, and two forest breeding guilds with preferences for forest edge and for interior forest. Parametric and non-parametric models were built to examine the improvement of predictability. Height-structured metrics had the strongest associations with species richness, yielding improved predictive ability for the woodland guild richness models (r(2) = ∼ 0.53 for the parametric models, 0.63 the non-parametric models) and the forest edge guild models (r(2) = ∼ 0.34 for the parametric models, 0.47 the non-parametric models). All but one of the linear models incorporating height-structured metrics showed significantly higher adjusted-r2 values than their counterparts without additional metrics. The interior forest guild richness showed a consistent low association with height-structured metrics. Our results suggest that height heterogeneity, beyond canopy height alone, supplements habitat characterization and richness models of forest bird species. The metrics and models derived in this study demonstrate practical examples of utilizing three-dimensional vegetation data for improved characterization of spatial patterns in species richness.

The Influence of Vegetation Height Heterogeneity on Forest and Woodland Bird Species Richness across the United States

PubMed Central

Huang, Qiongyu; Swatantran, Anu; Dubayah, Ralph; Goetz, Scott J.

2014-01-01

Avian diversity is under increasing pressures. It is thus critical to understand the ecological variables that contribute to large scale spatial distribution of avian species diversity. Traditionally, studies have relied primarily on two-dimensional habitat structure to model broad scale species richness. Vegetation vertical structure is increasingly used at local scales. However, the spatial arrangement of vegetation height has never been taken into consideration. Our goal was to examine the efficacies of three-dimensional forest structure, particularly the spatial heterogeneity of vegetation height in improving avian richness models across forested ecoregions in the U.S. We developed novel habitat metrics to characterize the spatial arrangement of vegetation height using the National Biomass and Carbon Dataset for the year 2000 (NBCD). The height-structured metrics were compared with other habitat metrics for statistical association with richness of three forest breeding bird guilds across Breeding Bird Survey (BBS) routes: a broadly grouped woodland guild, and two forest breeding guilds with preferences for forest edge and for interior forest. Parametric and non-parametric models were built to examine the improvement of predictability. Height-structured metrics had the strongest associations with species richness, yielding improved predictive ability for the woodland guild richness models (r2 = ∼0.53 for the parametric models, 0.63 the non-parametric models) and the forest edge guild models (r2 = ∼0.34 for the parametric models, 0.47 the non-parametric models). All but one of the linear models incorporating height-structured metrics showed significantly higher adjusted-r2 values than their counterparts without additional metrics. The interior forest guild richness showed a consistent low association with height-structured metrics. Our results suggest that height heterogeneity, beyond canopy height alone, supplements habitat characterization and richness models of forest bird species. The metrics and models derived in this study demonstrate practical examples of utilizing three-dimensional vegetation data for improved characterization of spatial patterns in species richness. PMID:25101782

Significance testing of clinical data using virus dynamics models with a Markov chain Monte Carlo method: application to emergence of lamivudine-resistant hepatitis B virus.

PubMed Central

Burroughs, N J; Pillay, D; Mutimer, D

1999-01-01

Bayesian analysis using a virus dynamics model is demonstrated to facilitate hypothesis testing of patterns in clinical time-series. Our Markov chain Monte Carlo implementation demonstrates that the viraemia time-series observed in two sets of hepatitis B patients on antiviral (lamivudine) therapy, chronic carriers and liver transplant patients, are significantly different, overcoming clinical trial design differences that question the validity of non-parametric tests. We show that lamivudine-resistant mutants grow faster in transplant patients than in chronic carriers, which probably explains the differences in emergence times and failure rates between these two sets of patients. Incorporation of dynamic models into Bayesian parameter analysis is of general applicability in medical statistics. PMID:10643081

Resting-state networks in healthy adult subjects: a comparison between a 32-element and an 8-element phased array head coil at 3.0 Tesla.

PubMed

Paolini, Marco; Keeser, Daniel; Ingrisch, Michael; Werner, Natalie; Kindermann, Nicole; Reiser, Maximilian; Blautzik, Janusch

2015-05-01

Little research exists on the influence of a magnetic resonance imaging (MRI) head coil's channel count on measured resting-state functional connectivity. To compare a 32-element (32ch) and an 8-element (8ch) phased array head coil with respect to their potential to detect functional connectivity within resting-state networks. Twenty-six healthy adults (mean age, 21.7 years; SD, 2.1 years) underwent resting-state functional MRI at 3.0 Tesla with both coils using equal standard imaging parameters and a counterbalanced design. Independent component analysis (ICA) at different model orders and a dual regression approach were performed. Voxel-wise non-parametric statistical between-group contrasts were determined using permutation-based non-parametric inference. Phantom measurements demonstrated a generally higher image signal-to-noise ratio using the 32ch head coil. However, the results showed no significant differences between corresponding resting-state networks derived from both coils (p < 0.05, FWE-corrected). Using the identical standard acquisition parameters, the 32ch head coil does not offer any significant advantages in detecting ICA-based functional connectivity within RSNs. © The Foundation Acta Radiologica 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Effects of ageing on orofacial fine force control and its relationship with parallel change in sensory perception.

PubMed

Etter, Nicole M; Mckeon, Patrick O; Dressler, Emily V; Andreatta, Richard D

2017-05-03

Current theoretical models suggest the importance of a bidirectional relationship between sensation and production in the vocal tract to maintain lifelong speech skills. The purpose of this study was to assess age-related changes in orofacial skilled force production and to begin defining the orofacial perception-action relationship in healthy adults. Low-level orofacial force control measures (reaction time, rise time, peak force, mean hold force (N) and force hold SD) were collected from 60 adults (19-84 years). Non-parametric Kruskal Wallis tests were performed to identify statistical differences between force and group demographics. Non-parametric Spearman's rank correlations were completed to compare force measures against previously published sensory data from the same cohort of participants. Significant group differences in force control were found for age, sex, speech usage and smoking status. Significant correlational relationships were identified between labial vibrotactile thresholds and several low-level force control measures collected during step and ramp-and-hold conditions. These findings demonstrate age-related alterations in orofacial force production. Furthermore, correlational analysis suggests as vibrotactile detection thresholds increase, the ability to maintain low-level force control accuracy decreases. Possible clinical applications and treatment consequences of these findings for speech disorders in the ageing population are provided.

Non-parametric causality detection: An application to social media and financial data

NASA Astrophysics Data System (ADS)

Tsapeli, Fani; Musolesi, Mirco; Tino, Peter

2017-10-01

According to behavioral finance, stock market returns are influenced by emotional, social and psychological factors. Several recent works support this theory by providing evidence of correlation between stock market prices and collective sentiment indexes measured using social media data. However, a pure correlation analysis is not sufficient to prove that stock market returns are influenced by such emotional factors since both stock market prices and collective sentiment may be driven by a third unmeasured factor. Controlling for factors that could influence the study by applying multivariate regression models is challenging given the complexity of stock market data. False assumptions about the linearity or non-linearity of the model and inaccuracies on model specification may result in misleading conclusions. In this work, we propose a novel framework for causal inference that does not require any assumption about a particular parametric form of the model expressing statistical relationships among the variables of the study and can effectively control a large number of observed factors. We apply our method in order to estimate the causal impact that information posted in social media may have on stock market returns of four big companies. Our results indicate that social media data not only correlate with stock market returns but also influence them.

A Comparison of Didactic and Inquiry Teaching Methods in a Rural Community College Earth Science Course

NASA Astrophysics Data System (ADS)

Beam, Margery Elizabeth

The combination of increasing enrollment and the importance of providing transfer students a solid foundation in science calls for science faculty to evaluate teaching methods in rural community colleges. The purpose of this study was to examine and compare the effectiveness of two teaching methods, inquiry teaching methods and didactic teaching methods, applied in a rural community college earth science course. Two groups of students were taught the same content via inquiry and didactic teaching methods. Analysis of quantitative data included a non-parametric ranking statistical testing method in which the difference between the rankings and the median of the post-test scores was analyzed for significance. Results indicated there was not a significant statistical difference between the teaching methods for the group of students participating in the research. The practical and educational significance of this study provides valuable perspectives on teaching methods and student learning styles in rural community colleges.

Empirical estimation of a distribution function with truncated and doubly interval-censored data and its application to AIDS studies.

PubMed

Sun, J

1995-09-01

In this paper we discuss the non-parametric estimation of a distribution function based on incomplete data for which the measurement origin of a survival time or the date of enrollment in a study is known only to belong to an interval. Also the survival time of interest itself is observed from a truncated distribution and is known only to lie in an interval. To estimate the distribution function, a simple self-consistency algorithm, a generalization of Turnbull's (1976, Journal of the Royal Statistical Association, Series B 38, 290-295) self-consistency algorithm, is proposed. This method is then used to analyze two AIDS cohort studies, for which direct use of the EM algorithm (Dempster, Laird and Rubin, 1976, Journal of the Royal Statistical Association, Series B 39, 1-38), which is computationally complicated, has previously been the usual method of the analysis.

Stability analysis of a time-periodic 2-dof MEMS structure

NASA Astrophysics Data System (ADS)

Kniffka, Till Jochen; Welte, Johannes; Ecker, Horst

2012-11-01

Microelectromechanical systems (MEMS) are becoming important for all kinds of industrial applications. Among them are filters in communication devices, due to the growing demand for efficient and accurate filtering of signals. In recent developments single degree of freedom (1-dof) oscillators, that are operated at a parametric resonances, are employed for such tasks. Typically vibration damping is low in such MEM systems. While parametric excitation (PE) is used so far to take advantage of a parametric resonance, this contribution suggests to also exploit parametric anti-resonances in order to improve the damping behavior of such systems. Modeling aspects of a 2-dof MEM system and first results of the analysis of the non-linear and the linearized system are the focus of this paper. In principle the investigated system is an oscillating mechanical system with two degrees of freedom x = [x1x2]T that can be described by Mx+Cx+K1x+K3(x2)x+Fes(x,V(t)) = 0. The system is inherently non-linear because of the cubic mechanical stiffness K3 of the structure, but also because of electrostatic forces (1+cos(ωt))Fes(x) that act on the system. Electrostatic forces are generated by comb drives and are proportional to the applied time-periodic voltage V(t). These drives also provide the means to introduce time-periodic coefficients, i.e. parametric excitation (1+cos(ωt)) with frequency ω. For a realistic MEM system the coefficients of the non-linear set of differential equations need to be scaled for efficient numerical treatment. The final mathematical model is a set of four non-linear time-periodic homogeneous differential equations of first order. Numerical results are obtained from two different methods. The linearized time-periodic (LTP) system is studied by calculating the Monodromy matrix of the system. The eigenvalues of this matrix decide on the stability of the LTP-system. To study the unabridged non-linear system, the bifurcation software ManLab is employed. Continuation analysis including stability evaluations are executed and show the frequency ranges for which the 2-dof system becomes unstable due to parametric resonances. Moreover, the existence of frequency intervals are shown where enhanced damping for the system is observed for this MEMS. The results from the stability studies are confirmed by simulation results.

Applications of quantum entropy to statistics

NASA Astrophysics Data System (ADS)

Silver, R. N.; Martz, H. F.

This paper develops two generalizations of the maximum entropy (ME) principle. First, Shannon classical entropy is replaced by von Neumann quantum entropy to yield a broader class of information divergences (or penalty functions) for statistics applications. Negative relative quantum entropy enforces convexity, positivity, non-local extensivity and prior correlations such as smoothness. This enables the extension of ME methods from their traditional domain of ill-posed in-verse problems to new applications such as non-parametric density estimation. Second, given a choice of information divergence, a combination of ME and Bayes rule is used to assign both prior and posterior probabilities. Hyperparameters are interpreted as Lagrange multipliers enforcing constraints. Conservation principles are proposed to act statistical regularization and other hyperparameters, such as conservation of information and smoothness. ME provides an alternative to hierarchical Bayes methods.

Software Reliability 2002

NASA Technical Reports Server (NTRS)

Wallace, Dolores R.

2003-01-01

In FY01 we learned that hardware reliability models need substantial changes to account for differences in software, thus making software reliability measurements more effective, accurate, and easier to apply. These reliability models are generally based on familiar distributions or parametric methods. An obvious question is 'What new statistical and probability models can be developed using non-parametric and distribution-free methods instead of the traditional parametric method?" Two approaches to software reliability engineering appear somewhat promising. The first study, begin in FY01, is based in hardware reliability, a very well established science that has many aspects that can be applied to software. This research effort has investigated mathematical aspects of hardware reliability and has identified those applicable to software. Currently the research effort is applying and testing these approaches to software reliability measurement, These parametric models require much project data that may be difficult to apply and interpret. Projects at GSFC are often complex in both technology and schedules. Assessing and estimating reliability of the final system is extremely difficult when various subsystems are tested and completed long before others. Parametric and distribution free techniques may offer a new and accurate way of modeling failure time and other project data to provide earlier and more accurate estimates of system reliability.

Quantification of 18FDG in the Normal Colon-A First Step in Investigating Whether Its Presence Is a Marker of a Physiological Process.

PubMed

Bardhan, Karna D; Cullis, James; Williams, Nigel R; Arasaradnam, Ramesh P; Wilson, Adrian J

2016-01-01

The visibility of the colon in positron emission tomography (PET) scans of patients without gastrointestinal disease indicating the presence of 18F Fluorodeoxyglucose (18FDG) is well recognised, but unquantified and unexplained. In this paper a qualitative scoring system was applied to PET scans from 30 randomly selected patients without gastrointestinal disease to detect the presence of 18FDG in 4 different sections of the colon and then both the total pixel value and the pixel value per unit length of each section of the colon were determined to quantify the amount of 18FDG from a randomly selected subset of 10 of these patients. Analysis of the qualitative scores using a non-parametric ANOVA showed that all sections of the colon contained 18FDG but there were differences in the amount of 18FDG present between sections (p<0.05). Wilcoxon matched-pair signed-rank tests between pairs of segments showed statistically significant differences between all pairs (p<0.05) with the exception of the caecum and ascending colon and the descending colon. The same non-parametric statistical analysis of the quantitative measures showed no difference in the total amount of 18FDG between sections (p>0.05), but a difference in the amount/unit length between sections (p<0.01) with only the caecum and ascending colon and the descending colon having a statistically significant difference (p<0.05). These results are consistent since the eye is drawn to focal localisation of the 18FDG when qualitatively scoring the scans. The presence of 18FDG in the colon is counterintuitive since it must be passing from the blood to the lumen through the colonic wall. There is no active mechanism to achieve this and therefore we hypothesise that the transport is a passive process driven by the concentration gradient of 18FDG across the colonic wall. This hypothesis is consistent with the results obtained from the qualitative and quantitative measures analysed.

Quantification of 18FDG in the Normal Colon—A First Step in Investigating Whether Its Presence Is a Marker of a Physiological Process

PubMed Central

Bardhan, Karna D.; Cullis, James; Williams, Nigel R.; Arasaradnam, Ramesh P.; Wilson, Adrian J.

2016-01-01

The visibility of the colon in positron emission tomography (PET) scans of patients without gastrointestinal disease indicating the presence of 18F Fluorodeoxyglucose (18FDG) is well recognised, but unquantified and unexplained. In this paper a qualitative scoring system was applied to PET scans from 30 randomly selected patients without gastrointestinal disease to detect the presence of 18FDG in 4 different sections of the colon and then both the total pixel value and the pixel value per unit length of each section of the colon were determined to quantify the amount of 18FDG from a randomly selected subset of 10 of these patients. Analysis of the qualitative scores using a non-parametric ANOVA showed that all sections of the colon contained 18FDG but there were differences in the amount of 18FDG present between sections (p<0.05). Wilcoxon matched-pair signed-rank tests between pairs of segments showed statistically significant differences between all pairs (p<0.05) with the exception of the caecum and ascending colon and the descending colon. The same non-parametric statistical analysis of the quantitative measures showed no difference in the total amount of 18FDG between sections (p>0.05), but a difference in the amount/unit length between sections (p<0.01) with only the caecum and ascending colon and the descending colon having a statistically significant difference (p<0.05). These results are consistent since the eye is drawn to focal localisation of the 18FDG when qualitatively scoring the scans. The presence of 18FDG in the colon is counterintuitive since it must be passing from the blood to the lumen through the colonic wall. There is no active mechanism to achieve this and therefore we hypothesise that the transport is a passive process driven by the concentration gradient of 18FDG across the colonic wall. This hypothesis is consistent with the results obtained from the qualitative and quantitative measures analysed. PMID:26821281

Multivariable Parametric Cost Model for Ground Optical: Telescope Assembly

NASA Technical Reports Server (NTRS)

Stahl, H. Philip; Rowell, Ginger Holmes; Reese, Gayle; Byberg, Alicia

2004-01-01

A parametric cost model for ground-based telescopes is developed using multi-variable statistical analysis of both engineering and performance parameters. While diameter continues to be the dominant cost driver, diffraction limited wavelength is found to be a secondary driver. Other parameters such as radius of curvature were examined. The model includes an explicit factor for primary mirror segmentation and/or duplication (i.e. multi-telescope phased-array systems). Additionally, single variable models based on aperture diameter were derived.

Comparative Analysis of a Principal Component Analysis-Based and an Artificial Neural Network-Based Method for Baseline Removal.

PubMed

Carvajal, Roberto C; Arias, Luis E; Garces, Hugo O; Sbarbaro, Daniel G

2016-04-01

This work presents a non-parametric method based on a principal component analysis (PCA) and a parametric one based on artificial neural networks (ANN) to remove continuous baseline features from spectra. The non-parametric method estimates the baseline based on a set of sampled basis vectors obtained from PCA applied over a previously composed continuous spectra learning matrix. The parametric method, however, uses an ANN to filter out the baseline. Previous studies have demonstrated that this method is one of the most effective for baseline removal. The evaluation of both methods was carried out by using a synthetic database designed for benchmarking baseline removal algorithms, containing 100 synthetic composed spectra at different signal-to-baseline ratio (SBR), signal-to-noise ratio (SNR), and baseline slopes. In addition to deomonstrating the utility of the proposed methods and to compare them in a real application, a spectral data set measured from a flame radiation process was used. Several performance metrics such as correlation coefficient, chi-square value, and goodness-of-fit coefficient were calculated to quantify and compare both algorithms. Results demonstrate that the PCA-based method outperforms the one based on ANN both in terms of performance and simplicity. © The Author(s) 2016.

Propagation of population pharmacokinetic information using a Bayesian approach: comparison with meta-analysis.

PubMed

Dokoumetzidis, Aristides; Aarons, Leon

2005-08-01

We investigated the propagation of population pharmacokinetic information across clinical studies by applying Bayesian techniques. The aim was to summarize the population pharmacokinetic estimates of a study in appropriate statistical distributions in order to use them as Bayesian priors in consequent population pharmacokinetic analyses. Various data sets of simulated and real clinical data were fitted with WinBUGS, with and without informative priors. The posterior estimates of fittings with non-informative priors were used to build parametric informative priors and the whole procedure was carried on in a consecutive manner. The posterior distributions of the fittings with informative priors where compared to those of the meta-analysis fittings of the respective combinations of data sets. Good agreement was found, for the simulated and experimental datasets when the populations were exchangeable, with the posterior distribution from the fittings with the prior to be nearly identical to the ones estimated with meta-analysis. However, when populations were not exchangeble an alternative parametric form for the prior, the natural conjugate prior, had to be used in order to have consistent results. In conclusion, the results of a population pharmacokinetic analysis may be summarized in Bayesian prior distributions that can be used consecutively with other analyses. The procedure is an alternative to meta-analysis and gives comparable results. It has the advantage that it is faster than the meta-analysis, due to the large datasets used with the latter and can be performed when the data included in the prior are not actually available.

Machine learning patterns for neuroimaging-genetic studies in the cloud.

PubMed

Da Mota, Benoit; Tudoran, Radu; Costan, Alexandru; Varoquaux, Gaël; Brasche, Goetz; Conrod, Patricia; Lemaitre, Herve; Paus, Tomas; Rietschel, Marcella; Frouin, Vincent; Poline, Jean-Baptiste; Antoniu, Gabriel; Thirion, Bertrand

2014-01-01

Brain imaging is a natural intermediate phenotype to understand the link between genetic information and behavior or brain pathologies risk factors. Massive efforts have been made in the last few years to acquire high-dimensional neuroimaging and genetic data on large cohorts of subjects. The statistical analysis of such data is carried out with increasingly sophisticated techniques and represents a great computational challenge. Fortunately, increasing computational power in distributed architectures can be harnessed, if new neuroinformatics infrastructures are designed and training to use these new tools is provided. Combining a MapReduce framework (TomusBLOB) with machine learning algorithms (Scikit-learn library), we design a scalable analysis tool that can deal with non-parametric statistics on high-dimensional data. End-users describe the statistical procedure to perform and can then test the model on their own computers before running the very same code in the cloud at a larger scale. We illustrate the potential of our approach on real data with an experiment showing how the functional signal in subcortical brain regions can be significantly fit with genome-wide genotypes. This experiment demonstrates the scalability and the reliability of our framework in the cloud with a 2 weeks deployment on hundreds of virtual machines.

Improved spatial regression analysis of diffusion tensor imaging for lesion detection during longitudinal progression of multiple sclerosis in individual subjects

NASA Astrophysics Data System (ADS)

Liu, Bilan; Qiu, Xing; Zhu, Tong; Tian, Wei; Hu, Rui; Ekholm, Sven; Schifitto, Giovanni; Zhong, Jianhui

2016-03-01

Subject-specific longitudinal DTI study is vital for investigation of pathological changes of lesions and disease evolution. Spatial Regression Analysis of Diffusion tensor imaging (SPREAD) is a non-parametric permutation-based statistical framework that combines spatial regression and resampling techniques to achieve effective detection of localized longitudinal diffusion changes within the whole brain at individual level without a priori hypotheses. However, boundary blurring and dislocation limit its sensitivity, especially towards detecting lesions of irregular shapes. In the present study, we propose an improved SPREAD (dubbed improved SPREAD, or iSPREAD) method by incorporating a three-dimensional (3D) nonlinear anisotropic diffusion filtering method, which provides edge-preserving image smoothing through a nonlinear scale space approach. The statistical inference based on iSPREAD was evaluated and compared with the original SPREAD method using both simulated and in vivo human brain data. Results demonstrated that the sensitivity and accuracy of the SPREAD method has been improved substantially by adapting nonlinear anisotropic filtering. iSPREAD identifies subject-specific longitudinal changes in the brain with improved sensitivity, accuracy, and enhanced statistical power, especially when the spatial correlation is heterogeneous among neighboring image pixels in DTI.

The effects of compensatory workplace exercises to reduce work-related stress and musculoskeletal pain1

PubMed Central

de Freitas-Swerts, Fabiana Cristina Taubert; Robazzi, Maria Lúcia do Carmo Cruz

2014-01-01

OBJECTIVES: to assess the effect of a compensatory workplace exercise program on workers with the purpose of reducing work-related stress and musculoskeletal pain. METHOD: quasi-experimental research with quantitative analysis of the data, involving 30 administrative workers from a Higher Education Public Institution. For data collection, questionnaires were used to characterize the workers, as well as the Workplace Stress Scale and the Corlett Diagram. The research took place in three stages: first: pre-test with the application of the questionnaires to the subjects; second: Workplace Exercise taking place twice a week, for 15 minutes, during a period of 10 weeks; third: post-test in which the subjects answered the questionnaires again. For data analysis, the descriptive statistics and non-parametric statistics were used through the Wilcoxon Test. RESULTS: work-related stress was present in the assessed workers, but there was no statistically significant reduction in the scores after undergoing Workplace Exercise. However, there was a statistically significant pain reduction in the neck, cervical, upper, middle and lower back, right thigh, left leg, right ankle and feet. CONCLUSION: the Workplace Exercise promoted a significant pain reduction in the spine, but did not result in a significant reduction in the levels of work-related stress. PMID:25296147

Biotrichotomy: The Neuroscientific and Neurobiological Systemology, Epistemology, and Methodology of the Tri-Squared Test and Tri-Center Analysis in Biostatistics

ERIC Educational Resources Information Center

Osler, James Edward

2015-01-01

This monograph provides a neuroscience-based systemological, epistemological, and methodological rational for the design of an advanced and novel parametric statistical analytics designed for the biological sciences referred to as "Biotrichotomy". The aim of this new arena of statistics is to provide dual metrics designed to analyze the…

A robust and efficient statistical method for genetic association studies using case and control samples from multiple cohorts

PubMed Central

2013-01-01

Background The theoretical basis of genome-wide association studies (GWAS) is statistical inference of linkage disequilibrium (LD) between any polymorphic marker and a putative disease locus. Most methods widely implemented for such analyses are vulnerable to several key demographic factors and deliver a poor statistical power for detecting genuine associations and also a high false positive rate. Here, we present a likelihood-based statistical approach that accounts properly for non-random nature of case–control samples in regard of genotypic distribution at the loci in populations under study and confers flexibility to test for genetic association in presence of different confounding factors such as population structure, non-randomness of samples etc. Results We implemented this novel method together with several popular methods in the literature of GWAS, to re-analyze recently published Parkinson’s disease (PD) case–control samples. The real data analysis and computer simulation show that the new method confers not only significantly improved statistical power for detecting the associations but also robustness to the difficulties stemmed from non-randomly sampling and genetic structures when compared to its rivals. In particular, the new method detected 44 significant SNPs within 25 chromosomal regions of size < 1 Mb but only 6 SNPs in two of these regions were previously detected by the trend test based methods. It discovered two SNPs located 1.18 Mb and 0.18 Mb from the PD candidates, FGF20 and PARK8, without invoking false positive risk. Conclusions We developed a novel likelihood-based method which provides adequate estimation of LD and other population model parameters by using case and control samples, the ease in integration of these samples from multiple genetically divergent populations and thus confers statistically robust and powerful analyses of GWAS. On basis of simulation studies and analysis of real datasets, we demonstrated significant improvement of the new method over the non-parametric trend test, which is the most popularly implemented in the literature of GWAS. PMID:23394771

rSeqNP: a non-parametric approach for detecting differential expression and splicing from RNA-Seq data.

PubMed

Shi, Yang; Chinnaiyan, Arul M; Jiang, Hui

2015-07-01

High-throughput sequencing of transcriptomes (RNA-Seq) has become a powerful tool to study gene expression. Here we present an R package, rSeqNP, which implements a non-parametric approach to test for differential expression and splicing from RNA-Seq data. rSeqNP uses permutation tests to access statistical significance and can be applied to a variety of experimental designs. By combining information across isoforms, rSeqNP is able to detect more differentially expressed or spliced genes from RNA-Seq data. The R package with its source code and documentation are freely available at http://www-personal.umich.edu/∼jianghui/rseqnp/. jianghui@umich.edu Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Assessing T cell clonal size distribution: a non-parametric approach.

PubMed

Bolkhovskaya, Olesya V; Zorin, Daniil Yu; Ivanchenko, Mikhail V

2014-01-01

Clonal structure of the human peripheral T-cell repertoire is shaped by a number of homeostatic mechanisms, including antigen presentation, cytokine and cell regulation. Its accurate tuning leads to a remarkable ability to combat pathogens in all their variety, while systemic failures may lead to severe consequences like autoimmune diseases. Here we develop and make use of a non-parametric statistical approach to assess T cell clonal size distributions from recent next generation sequencing data. For 41 healthy individuals and a patient with ankylosing spondylitis, who undergone treatment, we invariably find power law scaling over several decades and for the first time calculate quantitatively meaningful values of decay exponent. It has proved to be much the same among healthy donors, significantly different for an autoimmune patient before the therapy, and converging towards a typical value afterwards. We discuss implications of the findings for theoretical understanding and mathematical modeling of adaptive immunity.

Non-planar vibrations of slightly curved pipes conveying fluid in simple and combination parametric resonances

NASA Astrophysics Data System (ADS)

Czerwiński, Andrzej; Łuczko, Jan

2018-01-01

The paper summarises the experimental investigations and numerical simulations of non-planar parametric vibrations of a statically deformed pipe. Underpinning the theoretical analysis is a 3D dynamic model of curved pipe. The pipe motion is governed by four non-linear partial differential equations with periodically varying coefficients. The Galerkin method was applied, the shape function being that governing the beam's natural vibrations. Experiments were conducted in the range of simple and combination parametric resonances, evidencing the possibility of in-plane and out-of-plane vibrations as well as fully non-planar vibrations in the combination resonance range. It is demonstrated that sub-harmonic and quasi-periodic vibrations are likely to be excited. The method suggested allows the spatial modes to be determined basing on results registered at selected points in the pipe. Results are summarised in the form of time histories, phase trajectory plots and spectral diagrams. Dedicated video materials give us a better insight into the investigated phenomena.

Spectral and cross-spectral analysis of uneven time series with the smoothed Lomb-Scargle periodogram and Monte Carlo evaluation of statistical significance

NASA Astrophysics Data System (ADS)

Pardo-Igúzquiza, Eulogio; Rodríguez-Tovar, Francisco J.

2012-12-01

Many spectral analysis techniques have been designed assuming sequences taken with a constant sampling interval. However, there are empirical time series in the geosciences (sediment cores, fossil abundance data, isotope analysis, …) that do not follow regular sampling because of missing data, gapped data, random sampling or incomplete sequences, among other reasons. In general, interpolating an uneven series in order to obtain a succession with a constant sampling interval alters the spectral content of the series. In such cases it is preferable to follow an approach that works with the uneven data directly, avoiding the need for an explicit interpolation step. The Lomb-Scargle periodogram is a popular choice in such circumstances, as there are programs available in the public domain for its computation. One new computer program for spectral analysis improves the standard Lomb-Scargle periodogram approach in two ways: (1) It explicitly adjusts the statistical significance to any bias introduced by variance reduction smoothing, and (2) it uses a permutation test to evaluate confidence levels, which is better suited than parametric methods when neighbouring frequencies are highly correlated. Another novel program for cross-spectral analysis offers the advantage of estimating the Lomb-Scargle cross-periodogram of two uneven time series defined on the same interval, and it evaluates the confidence levels of the estimated cross-spectra by a non-parametric computer intensive permutation test. Thus, the cross-spectrum, the squared coherence spectrum, the phase spectrum, and the Monte Carlo statistical significance of the cross-spectrum and the squared-coherence spectrum can be obtained. Both of the programs are written in ANSI Fortran 77, in view of its simplicity and compatibility. The program code is of public domain, provided on the website of the journal (http://www.iamg.org/index.php/publisher/articleview/frmArticleID/112/). Different examples (with simulated and real data) are described in this paper to corroborate the methodology and the implementation of these two new programs.

Efficient Statistically Accurate Algorithms for the Fokker-Planck Equation in Large Dimensions

NASA Astrophysics Data System (ADS)

Chen, N.; Majda, A.

2017-12-01

Solving the Fokker-Planck equation for high-dimensional complex turbulent dynamical systems is an important and practical issue. However, most traditional methods suffer from the curse of dimensionality and have difficulties in capturing the fat tailed highly intermittent probability density functions (PDFs) of complex systems in turbulence, neuroscience and excitable media. In this article, efficient statistically accurate algorithms are developed for solving both the transient and the equilibrium solutions of Fokker-Planck equations associated with high-dimensional nonlinear turbulent dynamical systems with conditional Gaussian structures. The algorithms involve a hybrid strategy that requires only a small number of ensembles. Here, a conditional Gaussian mixture in a high-dimensional subspace via an extremely efficient parametric method is combined with a judicious non-parametric Gaussian kernel density estimation in the remaining low-dimensional subspace. Particularly, the parametric method, which is based on an effective data assimilation framework, provides closed analytical formulae for determining the conditional Gaussian distributions in the high-dimensional subspace. Therefore, it is computationally efficient and accurate. The full non-Gaussian PDF of the system is then given by a Gaussian mixture. Different from the traditional particle methods, each conditional Gaussian distribution here covers a significant portion of the high-dimensional PDF. Therefore a small number of ensembles is sufficient to recover the full PDF, which overcomes the curse of dimensionality. Notably, the mixture distribution has a significant skill in capturing the transient behavior with fat tails of the high-dimensional non-Gaussian PDFs, and this facilitates the algorithms in accurately describing the intermittency and extreme events in complex turbulent systems. It is shown in a stringent set of test problems that the method only requires an order of O(100) ensembles to successfully recover the highly non-Gaussian transient PDFs in up to 6 dimensions with only small errors.

Applications of non-parametric statistics and analysis of variance on sample variances

NASA Technical Reports Server (NTRS)

Myers, R. H.

1981-01-01

Nonparametric methods that are available for NASA-type applications are discussed. An attempt will be made here to survey what can be used, to attempt recommendations as to when each would be applicable, and to compare the methods, when possible, with the usual normal-theory procedures that are avavilable for the Gaussion analog. It is important here to point out the hypotheses that are being tested, the assumptions that are being made, and limitations of the nonparametric procedures. The appropriateness of doing analysis of variance on sample variances are also discussed and studied. This procedure is followed in several NASA simulation projects. On the surface this would appear to be reasonably sound procedure. However, difficulties involved center around the normality problem and the basic homogeneous variance assumption that is mase in usual analysis of variance problems. These difficulties discussed and guidelines given for using the methods.

A generalized parametric response mapping method for analysis of multi-parametric imaging: A feasibility study with application to glioblastoma.

PubMed

Lausch, Anthony; Yeung, Timothy Pok-Chi; Chen, Jeff; Law, Elton; Wang, Yong; Urbini, Benedetta; Donelli, Filippo; Manco, Luigi; Fainardi, Enrico; Lee, Ting-Yim; Wong, Eugene

2017-11-01

Parametric response map (PRM) analysis of functional imaging has been shown to be an effective tool for early prediction of cancer treatment outcomes and may also be well-suited toward guiding personalized adaptive radiotherapy (RT) strategies such as sub-volume boosting. However, the PRM method was primarily designed for analysis of longitudinally acquired pairs of single-parameter image data. The purpose of this study was to demonstrate the feasibility of a generalized parametric response map analysis framework, which enables analysis of multi-parametric data while maintaining the key advantages of the original PRM method. MRI-derived apparent diffusion coefficient (ADC) and relative cerebral blood volume (rCBV) maps acquired at 1 and 3-months post-RT for 19 patients with high-grade glioma were used to demonstrate the algorithm. Images were first co-registered and then standardized using normal tissue image intensity values. Tumor voxels were then plotted in a four-dimensional Cartesian space with coordinate values equal to a voxel's image intensity in each of the image volumes and an origin defined as the multi-parametric mean of normal tissue image intensity values. Voxel positions were orthogonally projected onto a line defined by the origin and a pre-determined response vector. The voxels are subsequently classified as positive, negative or nil, according to whether projected positions along the response vector exceeded a threshold distance from the origin. The response vector was selected by identifying the direction in which the standard deviation of tumor image intensity values was maximally different between responding and non-responding patients within a training dataset. Voxel classifications were visualized via familiar three-class response maps and then the fraction of tumor voxels associated with each of the classes was investigated for predictive utility analogous to the original PRM method. Independent PRM and MPRM analyses of the contrast-enhancing lesion (CEL) and a 1 cm shell of surrounding peri-tumoral tissue were performed. Prediction using tumor volume metrics was also investigated. Leave-one-out cross validation (LOOCV) was used in combination with permutation testing to assess preliminary predictive efficacy and estimate statistically robust P-values. The predictive endpoint was overall survival (OS) greater than or equal to the median OS of 18.2 months. Single-parameter PRM and multi-parametric response maps (MPRMs) were generated for each patient and used to predict OS via the LOOCV. Tumor volume metrics (P ≥ 0.071 ± 0.01) and single-parameter PRM analyses (P ≥ 0.170 ± 0.01) were not found to be predictive of OS within this study. MPRM analysis of the peri-tumoral region but not the CEL was found to be predictive of OS with a classification sensitivity, specificity and accuracy of 80%, 100%, and 89%, respectively (P = 0.001 ± 0.01). The feasibility of a generalized MPRM analysis framework was demonstrated with improved prediction of overall survival compared to the original single-parameter method when applied to a glioblastoma dataset. The proposed algorithm takes the spatial heterogeneity in multi-parametric response into consideration and enables visualization. MPRM analysis of peri-tumoral regions was shown to have predictive potential supporting further investigation of a larger glioblastoma dataset. © 2017 American Association of Physicists in Medicine.

Nonlinear Analysis of Mechanical Systems Under Combined Harmonic and Stochastic Excitation

DTIC Science & Technology

1993-05-27

Namachchivaya and Naresh Malhotra Department of Aeronautical and Astronautical Engineering University of Illinois, Urbana-Champaign Urbana, Illinois...Aeronauticai and Astronautical Engineering, University of Illinois, 1991. 2. N. Sri Namachchivaya and N. Malhotra , Parametrically Excited Hopf Bifurcation...Namachchivaya and N. Malhotra , Parametrically Excited Hopf Bifurcation with Non-semisimple 1:1 Resonance, Nonlinear Vibrations, ASME-AMD, Vol. 114, 1992. 3

Activity of left inferior frontal gyrus related to word repetition effects: LORETA imaging with 128-channel EEG and individual MRI.

PubMed

Kim, Young Youn; Lee, Boreom; Shin, Yong Wook; Kwon, Jun Soo; Kim, Myung-Sun

2006-02-01

We investigated the brain substrate of word repetition effects on the implicit memory task using low-resolution electromagnetic tomography (LORETA) with high-density 128-channel EEG and individual MRI as a realistic head model. Thirteen right-handed, healthy subjects performed a word/non-word discrimination task, in which the words and non-words were presented visually, and some of the words appeared twice with a lag of one or five items. All of the subjects exhibited word repetition effects with respect to the behavioral data, in which a faster reaction time was observed to the repeated word (old word) than to the first presentation of the word (new word). The old words elicited more positive-going potentials than the new words, beginning at 200 ms and lasting until 500 ms post-stimulus. We conducted source reconstruction using LORETA at a latency of 400 ms with the peak mean global field potentials and used statistical parametric mapping for the statistical analysis. We found that the source elicited by the old words exhibited a statistically significant current density reduction in the left inferior frontal gyrus. This is the first study to investigate the generators of word repetition effects using voxel-by-voxel statistical mapping of the current density with individual MRI and high-density EEG.

Validation of two (parametric vs non-parametric) daily weather generators

NASA Astrophysics Data System (ADS)

Dubrovsky, M.; Skalak, P.

2015-12-01

As the climate models (GCMs and RCMs) fail to satisfactorily reproduce the real-world surface weather regime, various statistical methods are applied to downscale GCM/RCM outputs into site-specific weather series. The stochastic weather generators are among the most favourite downscaling methods capable to produce realistic (observed-like) meteorological inputs for agrological, hydrological and other impact models used in assessing sensitivity of various ecosystems to climate change/variability. To name their advantages, the generators may (i) produce arbitrarily long multi-variate synthetic weather series representing both present and changed climates (in the latter case, the generators are commonly modified by GCM/RCM-based climate change scenarios), (ii) be run in various time steps and for multiple weather variables (the generators reproduce the correlations among variables), (iii) be interpolated (and run also for sites where no weather data are available to calibrate the generator). This contribution will compare two stochastic daily weather generators in terms of their ability to reproduce various features of the daily weather series. M&Rfi is a parametric generator: Markov chain model is used to model precipitation occurrence, precipitation amount is modelled by the Gamma distribution, and the 1st order autoregressive model is used to generate non-precipitation surface weather variables. The non-parametric GoMeZ generator is based on the nearest neighbours resampling technique making no assumption on the distribution of the variables being generated. Various settings of both weather generators will be assumed in the present validation tests. The generators will be validated in terms of (a) extreme temperature and precipitation characteristics (annual and 30-years extremes and maxima of duration of hot/cold/dry/wet spells); (b) selected validation statistics developed within the frame of VALUE project. The tests will be based on observational weather series from several European stations available from the ECA&D database. Acknowledgements: The weather generator is developed and validated within the frame of projects WG4VALUE (sponsored by the Ministry of Education, Youth and Sports of CR), and VALUE (COST ES 1102 action).

Power flow analysis of two coupled plates with arbitrary characteristics

NASA Technical Reports Server (NTRS)

Cuschieri, J. M.

1988-01-01

The limitation of keeping two plates identical is removed and the vibrational power input and output are evaluated for different area ratios, plate thickness ratios, and for different values of the structural damping loss factor for the source plate (plate with excitation) and the receiver plate. In performing this parametric analysis, the source plate characteristics are kept constant. The purpose of this parametric analysis is to be able to determine the most critical parameters that influence the flow of vibrational power from the source plate to the receiver plate. In the case of the structural damping parametric analysis, the influence of changes in the source plate damping is also investigated. As was done previously, results obtained from the mobility power flow approach will be compared to results obtained using a statistical energy analysis (SEA) approach. The significance of the power flow results are discussed together with a discussion and a comparison between SEA results and the mobility power flow results. Furthermore, the benefits that can be derived from using the mobility power flow approach, are also examined.

Two-sample tests and one-way MANOVA for multivariate biomarker data with nondetects.

PubMed

Thulin, M

2016-09-10

Testing whether the mean vector of a multivariate set of biomarkers differs between several populations is an increasingly common problem in medical research. Biomarker data is often left censored because some measurements fall below the laboratory's detection limit. We investigate how such censoring affects multivariate two-sample and one-way multivariate analysis of variance tests. Type I error rates, power and robustness to increasing censoring are studied, under both normality and non-normality. Parametric tests are found to perform better than non-parametric alternatives, indicating that the current recommendations for analysis of censored multivariate data may have to be revised. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Effects of bacterial infestation caused by human wastes on the skin structures of Mugil platanus Günther, 1880 (Mugilidae).

PubMed

Langer, S L; Vargas, V M F; Flores-Lopes, F; Malabarba, L R

2009-05-01

Manifestation of infectious pathologies in fishes usually increases in environments where organic wastes are disposed. Specimens of Mugil platanus Günther, 1880 and water samples collected at three points of the Tramandaí river were analyzed during a one year period. The macroscopic observation revealed ulcerations in the caudal peduncle area covered with a mass of amorphous and whitened tissues. Histopathologic analysis showed the presence of negative gram bacteria, probably responsible for alterations of the normal structure of the epidermic tissues. Non-parametric statistical analysis for ammonia concentration showed a significant variation among the three collected spots as well as in the multiple comparison between two spots. In this study, we describe cutaneous lesions observed in Mugil platanus specimens and tested their correlation with environmental ammonia concentration.

How to Compare Parametric and Nonparametric Person-Fit Statistics Using Real Data

ERIC Educational Resources Information Center

Sinharay, Sandip

2017-01-01

Person-fit assessment (PFA) is concerned with uncovering atypical test performance as reflected in the pattern of scores on individual items on a test. Existing person-fit statistics (PFSs) include both parametric and nonparametric statistics. Comparison of PFSs has been a popular research topic in PFA, but almost all comparisons have employed…

Optical Parametric Amplification of Single Photon: Statistical Properties and Quantum Interference

NASA Astrophysics Data System (ADS)

Xu, Xue-Xiang; Yuan, Hong-Chun

2014-05-01

By using phase space method, we theoretically investigate the quantum statistical properties and quantum interference of optical parametric amplification of single photon. The statistical properties, such as the Wigner function (WF), average photon number, photon number distribution and parity, are derived analytically for the fields of the two output ports. The results indicate that the fields in the output ports are multiphoton states rather than single photon state due to the amplification of the optical parametric amplifiers (OPA). In addition, the phase sensitivity is also examined by using the detection scheme of parity measurement.

Large-scale subject-specific cerebral arterial tree modeling using automated parametric mesh generation for blood flow simulation.

PubMed

Ghaffari, Mahsa; Tangen, Kevin; Alaraj, Ali; Du, Xinjian; Charbel, Fady T; Linninger, Andreas A

2017-12-01

In this paper, we present a novel technique for automatic parametric mesh generation of subject-specific cerebral arterial trees. This technique generates high-quality and anatomically accurate computational meshes for fast blood flow simulations extending the scope of 3D vascular modeling to a large portion of cerebral arterial trees. For this purpose, a parametric meshing procedure was developed to automatically decompose the vascular skeleton, extract geometric features and generate hexahedral meshes using a body-fitted coordinate system that optimally follows the vascular network topology. To validate the anatomical accuracy of the reconstructed vasculature, we performed statistical analysis to quantify the alignment between parametric meshes and raw vascular images using receiver operating characteristic curve. Geometric accuracy evaluation showed an agreement with area under the curves value of 0.87 between the constructed mesh and raw MRA data sets. Parametric meshing yielded on-average, 36.6% and 21.7% orthogonal and equiangular skew quality improvement over the unstructured tetrahedral meshes. The parametric meshing and processing pipeline constitutes an automated technique to reconstruct and simulate blood flow throughout a large portion of the cerebral arterial tree down to the level of pial vessels. This study is the first step towards fast large-scale subject-specific hemodynamic analysis for clinical applications. Copyright © 2017 Elsevier Ltd. All rights reserved.

Normal Distribution of CD8+ T-Cell-Derived ELISPOT Counts within Replicates Justifies the Reliance on Parametric Statistics for Identifying Positive Responses.

PubMed

Karulin, Alexey Y; Caspell, Richard; Dittrich, Marcus; Lehmann, Paul V

2015-03-02

Accurate assessment of positive ELISPOT responses for low frequencies of antigen-specific T-cells is controversial. In particular, it is still unknown whether ELISPOT counts within replicate wells follow a theoretical distribution function, and thus whether high power parametric statistics can be used to discriminate between positive and negative wells. We studied experimental distributions of spot counts for up to 120 replicate wells of IFN-γ production by CD8+ T-cell responding to EBV LMP2A (426 - 434) peptide in human PBMC. The cells were tested in serial dilutions covering a wide range of average spot counts per condition, from just a few to hundreds of spots per well. Statistical analysis of the data using diagnostic Q-Q plots and the Shapiro-Wilk normality test showed that in the entire dynamic range of ELISPOT spot counts within replicate wells followed a normal distribution. This result implies that the Student t-Test and ANOVA are suited to identify positive responses. We also show experimentally that borderline responses can be reliably detected by involving more replicate wells, plating higher numbers of PBMC, addition of IL-7, or a combination of these. Furthermore, we have experimentally verified that the number of replicates needed for detection of weak responses can be calculated using parametric statistics.

New analysis methods to push the boundaries of diagnostic techniques in the environmental sciences

NASA Astrophysics Data System (ADS)

Lungaroni, M.; Murari, A.; Peluso, E.; Gelfusa, M.; Malizia, A.; Vega, J.; Talebzadeh, S.; Gaudio, P.

2016-04-01

In the last years, new and more sophisticated measurements have been at the basis of the major progress in various disciplines related to the environment, such as remote sensing and thermonuclear fusion. To maximize the effectiveness of the measurements, new data analysis techniques are required. First data processing tasks, such as filtering and fitting, are of primary importance, since they can have a strong influence on the rest of the analysis. Even if Support Vector Regression is a method devised and refined at the end of the 90s, a systematic comparison with more traditional non parametric regression methods has never been reported. In this paper, a series of systematic tests is described, which indicates how SVR is a very competitive method of non-parametric regression that can usefully complement and often outperform more consolidated approaches. The performance of Support Vector Regression as a method of filtering is investigated first, comparing it with the most popular alternative techniques. Then Support Vector Regression is applied to the problem of non-parametric regression to analyse Lidar surveys for the environments measurement of particulate matter due to wildfires. The proposed approach has given very positive results and provides new perspectives to the interpretation of the data.

Parametric interactions in presence of different size colloids in semiconductor quantum plasmas

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

Vanshpal, R., E-mail: ravivanshpal@gmail.com; Sharma, Uttam; Dubey, Swati

2015-07-31

Present work is an attempt to investigate the effect of different size colloids on parametric interaction in semiconductor quantum plasma. Inclusion of quantum effect is being done in this analysis through quantum correction term in classical hydrodynamic model of homogeneous semiconductor plasma. The effect is associated with purely quantum origin using quantum Bohm potential and quantum statistics. Colloidal size and quantum correction term modify the parametric dispersion characteristics of ion implanted semiconductor plasma medium. It is found that quantum effect on colloids is inversely proportional to their size. Moreover critical size of implanted colloids for the effective quantum correction ismore » determined which is found to be equal to the lattice spacing of the crystal.« less

Semiparametric Estimation of the Impacts of Longitudinal Interventions on Adolescent Obesity using Targeted Maximum-Likelihood: Accessible Estimation with the ltmle Package

PubMed Central

Decker, Anna L.; Hubbard, Alan; Crespi, Catherine M.; Seto, Edmund Y.W.; Wang, May C.

2015-01-01

While child and adolescent obesity is a serious public health concern, few studies have utilized parameters based on the causal inference literature to examine the potential impacts of early intervention. The purpose of this analysis was to estimate the causal effects of early interventions to improve physical activity and diet during adolescence on body mass index (BMI), a measure of adiposity, using improved techniques. The most widespread statistical method in studies of child and adolescent obesity is multi-variable regression, with the parameter of interest being the coefficient on the variable of interest. This approach does not appropriately adjust for time-dependent confounding, and the modeling assumptions may not always be met. An alternative parameter to estimate is one motivated by the causal inference literature, which can be interpreted as the mean change in the outcome under interventions to set the exposure of interest. The underlying data-generating distribution, upon which the estimator is based, can be estimated via a parametric or semi-parametric approach. Using data from the National Heart, Lung, and Blood Institute Growth and Health Study, a 10-year prospective cohort study of adolescent girls, we estimated the longitudinal impact of physical activity and diet interventions on 10-year BMI z-scores via a parameter motivated by the causal inference literature, using both parametric and semi-parametric estimation approaches. The parameters of interest were estimated with a recently released R package, ltmle, for estimating means based upon general longitudinal treatment regimes. We found that early, sustained intervention on total calories had a greater impact than a physical activity intervention or non-sustained interventions. Multivariable linear regression yielded inflated effect estimates compared to estimates based on targeted maximum-likelihood estimation and data-adaptive super learning. Our analysis demonstrates that sophisticated, optimal semiparametric estimation of longitudinal treatment-specific means via ltmle provides an incredibly powerful, yet easy-to-use tool, removing impediments for putting theory into practice. PMID:26046009

Modeling gene expression measurement error: a quasi-likelihood approach

PubMed Central

Strimmer, Korbinian

2003-01-01

Background Using suitable error models for gene expression measurements is essential in the statistical analysis of microarray data. However, the true probabilistic model underlying gene expression intensity readings is generally not known. Instead, in currently used approaches some simple parametric model is assumed (usually a transformed normal distribution) or the empirical distribution is estimated. However, both these strategies may not be optimal for gene expression data, as the non-parametric approach ignores known structural information whereas the fully parametric models run the risk of misspecification. A further related problem is the choice of a suitable scale for the model (e.g. observed vs. log-scale). Results Here a simple semi-parametric model for gene expression measurement error is presented. In this approach inference is based an approximate likelihood function (the extended quasi-likelihood). Only partial knowledge about the unknown true distribution is required to construct this function. In case of gene expression this information is available in the form of the postulated (e.g. quadratic) variance structure of the data. As the quasi-likelihood behaves (almost) like a proper likelihood, it allows for the estimation of calibration and variance parameters, and it is also straightforward to obtain corresponding approximate confidence intervals. Unlike most other frameworks, it also allows analysis on any preferred scale, i.e. both on the original linear scale as well as on a transformed scale. It can also be employed in regression approaches to model systematic (e.g. array or dye) effects. Conclusions The quasi-likelihood framework provides a simple and versatile approach to analyze gene expression data that does not make any strong distributional assumptions about the underlying error model. For several simulated as well as real data sets it provides a better fit to the data than competing models. In an example it also improved the power of tests to identify differential expression. PMID:12659637

Potency control of modified live viral vaccines for veterinary use.

PubMed

Terpstra, C; Kroese, A H

1996-04-01

This paper reviews various aspects of efficacy, and methods for assaying the potency of modified live viral vaccines. The pros and cons of parametric versus non-parametric methods for analysis of potency assays are discussed and critical levels of protection, as determined by the target(s) of vaccination, are exemplified. Recommendations are presented for designing potency assays on master virus seeds and vaccine batches.

Potency control of modified live viral vaccines for veterinary use.

PubMed

Terpstra, C; Kroese, A H

1996-01-01

This paper reviews various aspects of efficacy, and methods for assaying the potency of modified live viral vaccines. The pros and cons of parametric versus non-parametric methods for analysis of potency assays are discussed and critical levels of protection, as determined by the target(s) of vaccination, are exemplified. Recommendations are presented for designing potency assays on master virus seeds and vaccine batches.

Joint analysis of input and parametric uncertainties in watershed water quality modeling: A formal Bayesian approach

NASA Astrophysics Data System (ADS)

Han, Feng; Zheng, Yi

2018-06-01

Significant Input uncertainty is a major source of error in watershed water quality (WWQ) modeling. It remains challenging to address the input uncertainty in a rigorous Bayesian framework. This study develops the Bayesian Analysis of Input and Parametric Uncertainties (BAIPU), an approach for the joint analysis of input and parametric uncertainties through a tight coupling of Markov Chain Monte Carlo (MCMC) analysis and Bayesian Model Averaging (BMA). The formal likelihood function for this approach is derived considering a lag-1 autocorrelated, heteroscedastic, and Skew Exponential Power (SEP) distributed error model. A series of numerical experiments were performed based on a synthetic nitrate pollution case and on a real study case in the Newport Bay Watershed, California. The Soil and Water Assessment Tool (SWAT) and Differential Evolution Adaptive Metropolis (DREAM(ZS)) were used as the representative WWQ model and MCMC algorithm, respectively. The major findings include the following: (1) the BAIPU can be implemented and used to appropriately identify the uncertain parameters and characterize the predictive uncertainty; (2) the compensation effect between the input and parametric uncertainties can seriously mislead the modeling based management decisions, if the input uncertainty is not explicitly accounted for; (3) the BAIPU accounts for the interaction between the input and parametric uncertainties and therefore provides more accurate calibration and uncertainty results than a sequential analysis of the uncertainties; and (4) the BAIPU quantifies the credibility of different input assumptions on a statistical basis and can be implemented as an effective inverse modeling approach to the joint inference of parameters and inputs.

Vehicle dynamic analysis using neuronal network algorithms

NASA Astrophysics Data System (ADS)

Oloeriu, Florin; Mocian, Oana

2014-06-01

Theoretical developments of certain engineering areas, the emergence of new investigation tools, which are better and more precise and their implementation on-board the everyday vehicles, all these represent main influence factors that impact the theoretical and experimental study of vehicle's dynamic behavior. Once the implementation of these new technologies onto the vehicle's construction had been achieved, it had led to more and more complex systems. Some of the most important, such as the electronic control of engine, transmission, suspension, steering, braking and traction had a positive impact onto the vehicle's dynamic behavior. The existence of CPU on-board vehicles allows data acquisition and storage and it leads to a more accurate and better experimental and theoretical study of vehicle dynamics. It uses the information offered directly by the already on-board built-in elements of electronic control systems. The technical literature that studies vehicle dynamics is entirely focused onto parametric analysis. This kind of approach adopts two simplifying assumptions. Functional parameters obey certain distribution laws, which are known in classical statistics theory. The second assumption states that the mathematical models are previously known and have coefficients that are not time-dependent. Both the mentioned assumptions are not confirmed in real situations: the functional parameters do not follow any known statistical repartition laws and the mathematical laws aren't previously known and contain families of parameters and are mostly time-dependent. The purpose of the paper is to present a more accurate analysis methodology that can be applied when studying vehicle's dynamic behavior. A method that provides the setting of non-parametrical mathematical models for vehicle's dynamic behavior is relying on neuronal networks. This method contains coefficients that are time-dependent. Neuronal networks are mostly used in various types' system controls, thus being a non-linear process identification algorithm. The common use of neuronal networks for non-linear processes is justified by the fact that both have the ability to organize by themselves. That is why the neuronal networks best define intelligent systems, thus the word `neuronal' is sending one's mind to the biological neuron cell. The paper presents how to better interpret data fed from the on-board computer and a new way of processing that data to better model the real life dynamic behavior of the vehicle.

Parametric analyses of summative scores may lead to conflicting inferences when comparing groups: A simulation study.

PubMed

Khan, Asaduzzaman; Chien, Chi-Wen; Bagraith, Karl S

2015-04-01

To investigate whether using a parametric statistic in comparing groups leads to different conclusions when using summative scores from rating scales compared with using their corresponding Rasch-based measures. A Monte Carlo simulation study was designed to examine between-group differences in the change scores derived from summative scores from rating scales, and those derived from their corresponding Rasch-based measures, using 1-way analysis of variance. The degree of inconsistency between the 2 scoring approaches (i.e. summative and Rasch-based) was examined, using varying sample sizes, scale difficulties and person ability conditions. This simulation study revealed scaling artefacts that could arise from using summative scores rather than Rasch-based measures for determining the changes between groups. The group differences in the change scores were statistically significant for summative scores under all test conditions and sample size scenarios. However, none of the group differences in the change scores were significant when using the corresponding Rasch-based measures. This study raises questions about the validity of the inference on group differences of summative score changes in parametric analyses. Moreover, it provides a rationale for the use of Rasch-based measures, which can allow valid parametric analyses of rating scale data.

Phylogenetic relationships of South American lizards of the genus Stenocercus (Squamata: Iguania): A new approach using a general mixture model for gene sequence data.

PubMed

Torres-Carvajal, Omar; Schulte, James A; Cadle, John E

2006-04-01

The South American iguanian lizard genus Stenocercus includes 54 species occurring mostly in the Andes and adjacent lowland areas from northern Venezuela and Colombia to central Argentina at elevations of 0-4000m. Small taxon or character sampling has characterized all phylogenetic analyses of Stenocercus, which has long been recognized as sister taxon to the Tropidurus Group. In this study, we use mtDNA sequence data to perform phylogenetic analyses that include 32 species of Stenocercus and 12 outgroup taxa. Monophyly of this genus is strongly supported by maximum parsimony and Bayesian analyses. Evolutionary relationships within Stenocercus are further analyzed with a Bayesian implementation of a general mixture model, which accommodates variability in the pattern of evolution across sites. These analyses indicate a basal split of Stenocercus into two clades, one of which receives very strong statistical support. In addition, we test previous hypotheses using non-parametric and parametric statistical methods, and provide a phylogenetic classification for Stenocercus.

Uncertainty in determining extreme precipitation thresholds

NASA Astrophysics Data System (ADS)

Liu, Bingjun; Chen, Junfan; Chen, Xiaohong; Lian, Yanqing; Wu, Lili

2013-10-01

Extreme precipitation events are rare and occur mostly on a relatively small and local scale, which makes it difficult to set the thresholds for extreme precipitations in a large basin. Based on the long term daily precipitation data from 62 observation stations in the Pearl River Basin, this study has assessed the applicability of the non-parametric, parametric, and the detrended fluctuation analysis (DFA) methods in determining extreme precipitation threshold (EPT) and the certainty to EPTs from each method. Analyses from this study show the non-parametric absolute critical value method is easy to use, but unable to reflect the difference of spatial rainfall distribution. The non-parametric percentile method can account for the spatial distribution feature of precipitation, but the problem with this method is that the threshold value is sensitive to the size of rainfall data series and is subjected to the selection of a percentile thus make it difficult to determine reasonable threshold values for a large basin. The parametric method can provide the most apt description of extreme precipitations by fitting extreme precipitation distributions with probability distribution functions; however, selections of probability distribution functions, the goodness-of-fit tests, and the size of the rainfall data series can greatly affect the fitting accuracy. In contrast to the non-parametric and the parametric methods which are unable to provide information for EPTs with certainty, the DFA method although involving complicated computational processes has proven to be the most appropriate method that is able to provide a unique set of EPTs for a large basin with uneven spatio-temporal precipitation distribution. The consistency between the spatial distribution of DFA-based thresholds with the annual average precipitation, the coefficient of variation (CV), and the coefficient of skewness (CS) for the daily precipitation further proves that EPTs determined by the DFA method are more reasonable and applicable for the Pearl River Basin.

Chaotic map clustering algorithm for EEG analysis

NASA Astrophysics Data System (ADS)

Bellotti, R.; De Carlo, F.; Stramaglia, S.

2004-03-01

The non-parametric chaotic map clustering algorithm has been applied to the analysis of electroencephalographic signals, in order to recognize the Huntington's disease, one of the most dangerous pathologies of the central nervous system. The performance of the method has been compared with those obtained through parametric algorithms, as K-means and deterministic annealing, and supervised multi-layer perceptron. While supervised neural networks need a training phase, performed by means of data tagged by the genetic test, and the parametric methods require a prior choice of the number of classes to find, the chaotic map clustering gives a natural evidence of the pathological class, without any training or supervision, thus providing a new efficient methodology for the recognition of patterns affected by the Huntington's disease.

Inference of median difference based on the Box-Cox model in randomized clinical trials.

PubMed

Maruo, K; Isogawa, N; Gosho, M

2015-05-10

In randomized clinical trials, many medical and biological measurements are not normally distributed and are often skewed. The Box-Cox transformation is a powerful procedure for comparing two treatment groups for skewed continuous variables in terms of a statistical test. However, it is difficult to directly estimate and interpret the location difference between the two groups on the original scale of the measurement. We propose a helpful method that infers the difference of the treatment effect on the original scale in a more easily interpretable form. We also provide statistical analysis packages that consistently include an estimate of the treatment effect, covariance adjustments, standard errors, and statistical hypothesis tests. The simulation study that focuses on randomized parallel group clinical trials with two treatment groups indicates that the performance of the proposed method is equivalent to or better than that of the existing non-parametric approaches in terms of the type-I error rate and power. We illustrate our method with cluster of differentiation 4 data in an acquired immune deficiency syndrome clinical trial. Copyright © 2015 John Wiley & Sons, Ltd.

The l z ( p ) * Person-Fit Statistic in an Unfolding Model Context.

PubMed

Tendeiro, Jorge N

2017-01-01

Although person-fit analysis has a long-standing tradition within item response theory, it has been applied in combination with dominance response models almost exclusively. In this article, a popular log likelihood-based parametric person-fit statistic under the framework of the generalized graded unfolding model is used. Results from a simulation study indicate that the person-fit statistic performed relatively well in detecting midpoint response style patterns and not so well in detecting extreme response style patterns.

A detailed view on Model-Based Multifactor Dimensionality Reduction for detecting gene-gene interactions in case-control data in the absence and presence of noise

PubMed Central

CATTAERT, TOM; CALLE, M. LUZ; DUDEK, SCOTT M.; MAHACHIE JOHN, JESTINAH M.; VAN LISHOUT, FRANÇOIS; URREA, VICTOR; RITCHIE, MARYLYN D.; VAN STEEN, KRISTEL

2010-01-01

SUMMARY Analyzing the combined effects of genes and/or environmental factors on the development of complex diseases is a great challenge from both the statistical and computational perspective, even using a relatively small number of genetic and non-genetic exposures. Several data mining methods have been proposed for interaction analysis, among them, the Multifactor Dimensionality Reduction Method (MDR), which has proven its utility in a variety of theoretical and practical settings. Model-Based Multifactor Dimensionality Reduction (MB-MDR), a relatively new MDR-based technique that is able to unify the best of both non-parametric and parametric worlds, was developed to address some of the remaining concerns that go along with an MDR-analysis. These include the restriction to univariate, dichotomous traits, the absence of flexible ways to adjust for lower-order effects and important confounders, and the difficulty to highlight epistasis effects when too many multi-locus genotype cells are pooled into two new genotype groups. Whereas the true value of MB-MDR can only reveal itself by extensive applications of the method in a variety of real-life scenarios, here we investigate the empirical power of MB-MDR to detect gene-gene interactions in the absence of any noise and in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity. For the considered simulation settings, we show that the power is generally higher for MB-MDR than for MDR, in particular in the presence of genetic heterogeneity, phenocopy, or low minor allele frequencies. PMID:21158747

Parametric and experimental analysis using a power flow approach

NASA Technical Reports Server (NTRS)

Cuschieri, J. M.

1990-01-01

A structural power flow approach for the analysis of structure-borne transmission of vibrations is used to analyze the influence of structural parameters on transmitted power. The parametric analysis is also performed using the Statistical Energy Analysis approach and the results are compared with those obtained using the power flow approach. The advantages of structural power flow analysis are demonstrated by comparing the type of results that are obtained by the two analytical methods. Also, to demonstrate that the power flow results represent a direct physical parameter that can be measured on a typical structure, an experimental study of structural power flow is presented. This experimental study presents results for an L shaped beam for which an available solution was already obtained. Various methods to measure vibrational power flow are compared to study their advantages and disadvantages.

Introduction to multivariate discrimination

NASA Astrophysics Data System (ADS)

Kégl, Balázs

2013-07-01

Multivariate discrimination or classification is one of the best-studied problem in machine learning, with a plethora of well-tested and well-performing algorithms. There are also several good general textbooks [1-9] on the subject written to an average engineering, computer science, or statistics graduate student; most of them are also accessible for an average physics student with some background on computer science and statistics. Hence, instead of writing a generic introduction, we concentrate here on relating the subject to a practitioner experimental physicist. After a short introduction on the basic setup (Section 1) we delve into the practical issues of complexity regularization, model selection, and hyperparameter optimization (Section 2), since it is this step that makes high-complexity non-parametric fitting so different from low-dimensional parametric fitting. To emphasize that this issue is not restricted to classification, we illustrate the concept on a low-dimensional but non-parametric regression example (Section 2.1). Section 3 describes the common algorithmic-statistical formal framework that unifies the main families of multivariate classification algorithms. We explain here the large-margin principle that partly explains why these algorithms work. Section 4 is devoted to the description of the three main (families of) classification algorithms, neural networks, the support vector machine, and AdaBoost. We do not go into the algorithmic details; the goal is to give an overview on the form of the functions these methods learn and on the objective functions they optimize. Besides their technical description, we also make an attempt to put these algorithm into a socio-historical context. We then briefly describe some rather heterogeneous applications to illustrate the pattern recognition pipeline and to show how widespread the use of these methods is (Section 5). We conclude the chapter with three essentially open research problems that are either relevant to or even motivated by certain unorthodox applications of multivariate discrimination in experimental physics.

Parametric study of the swimming performance of a fish robot propelled by a flexible caudal fin.

PubMed

Low, K H; Chong, C W

2010-12-01

In this paper, we aim to study the swimming performance of fish robots by using a statistical approach. A fish robot employing a carangiform swimming mode had been used as an experimental platform for the performance study. The experiments conducted aim to investigate the effect of various design parameters on the thrust capability of the fish robot with a flexible caudal fin. The controllable parameters associated with the fin include frequency, amplitude of oscillation, aspect ratio and the rigidity of the caudal fin. The significance of these parameters was determined in the first set of experiments by using a statistical approach. A more detailed parametric experimental study was then conducted with only those significant parameters. As a result, the parametric study could be completed with a reduced number of experiments and time spent. With the obtained experimental result, we were able to understand the relationship between various parameters and a possible adjustment of parameters to obtain a higher thrust. The proposed statistical method for experimentation provides an objective and thorough analysis of the effects of individual or combinations of parameters on the swimming performance. Such an efficient experimental design helps to optimize the process and determine factors that influence variability.

Preliminary results of using ALAnerv® in subacute motor incomplete paraplegics.

PubMed

Andone, I; Anghelescu, A; Daia, C; Onose, G

2015-01-01

To assess whether using ALAnerv® contributes to improvements of outcomes obtained in post SCI patients. A prospective controlled clinical survey also to evaluate the safety and efficacy of ALAnerv® (2cps/ day for 28 days) in motor incomplete (AIS/ Frankel C) paraplegic subacute patients. 59 patients divided in study (treated with ALAnerv®) and control, groups. This survey's follow-up duration was of 28 days. Most of the studied patients were mid-aged (mean 43.75 years old) and respectively, men (64,29% in the study group; 58,06% in controls). We used descriptive statistics (functions: minimum, maximum, mean, median, standard deviation) and for related comparisons, parametric (Student t) and non-parametric (Mann-Whitney, Fisher's exact, chi-square) tests. The primary end-point: AIS motor values' evolution (P= 0.015 - Mann-Whitney), showed that patients treated with ALAnerv® - vs. controls - had a statistically significant better increase of such scores at discharge. Paraclinical parameters, mainly exploring systemic inflammatory status (secondary end-point): ESR dynamics (P=0.13) had no statistical significance; the plasma leucocytes number (P=0.018), the neutrophils' percentage (P=0.001) and fibrinogenemia (P= 0,017) proved in the treated group to have a statistically significant better evolution. We used "Statistical Package for Social Sciences" (SPSS). As there is actually no effective curative solution for the devastating pathology following SCI, any medical approach susceptible to bring even limited improvements, such as treatment with ALAnerv® seemed to have proven, is worth being surveyed, under strict circumstances of ethics and research methodology. Considering the necessity for more statistical power concerning primary, secondary end-points, and safety issues, as well, continuing this research is needed. SCI = spinal cord injury, TSCI = traumatic spinal cord injury, BBB = blood brain barrier, CNS = central nervous system, SC = spinal cord, NSAIDs = non-steroidal anti-inflammatory drugs, SAIDs = steroidal anti-inflammatory drugs, AIS = American Spinal Injury Association Impairment Scale, SPSS = Statistical Package for Social Sciences, BATEH = Bagdasar-Arseni Teaching Emergency Hospital.

Testing in semiparametric models with interaction, with applications to gene-environment interactions.

PubMed

Maity, Arnab; Carroll, Raymond J; Mammen, Enno; Chatterjee, Nilanjan

2009-01-01

Motivated from the problem of testing for genetic effects on complex traits in the presence of gene-environment interaction, we develop score tests in general semiparametric regression problems that involves Tukey style 1 degree-of-freedom form of interaction between parametrically and non-parametrically modelled covariates. We find that the score test in this type of model, as recently developed by Chatterjee and co-workers in the fully parametric setting, is biased and requires undersmoothing to be valid in the presence of non-parametric components. Moreover, in the presence of repeated outcomes, the asymptotic distribution of the score test depends on the estimation of functions which are defined as solutions of integral equations, making implementation difficult and computationally taxing. We develop profiled score statistics which are unbiased and asymptotically efficient and can be performed by using standard bandwidth selection methods. In addition, to overcome the difficulty of solving functional equations, we give easy interpretations of the target functions, which in turn allow us to develop estimation procedures that can be easily implemented by using standard computational methods. We present simulation studies to evaluate type I error and power of the method proposed compared with a naive test that does not consider interaction. Finally, we illustrate our methodology by analysing data from a case-control study of colorectal adenoma that was designed to investigate the association between colorectal adenoma and the candidate gene NAT2 in relation to smoking history.

Hybrid Evidence Theory-based Finite Element/Statistical Energy Analysis method for mid-frequency analysis of built-up systems with epistemic uncertainties

NASA Astrophysics Data System (ADS)

Yin, Shengwen; Yu, Dejie; Yin, Hui; Lü, Hui; Xia, Baizhan

2017-09-01

Considering the epistemic uncertainties within the hybrid Finite Element/Statistical Energy Analysis (FE/SEA) model when it is used for the response analysis of built-up systems in the mid-frequency range, the hybrid Evidence Theory-based Finite Element/Statistical Energy Analysis (ETFE/SEA) model is established by introducing the evidence theory. Based on the hybrid ETFE/SEA model and the sub-interval perturbation technique, the hybrid Sub-interval Perturbation and Evidence Theory-based Finite Element/Statistical Energy Analysis (SIP-ETFE/SEA) approach is proposed. In the hybrid ETFE/SEA model, the uncertainty in the SEA subsystem is modeled by a non-parametric ensemble, while the uncertainty in the FE subsystem is described by the focal element and basic probability assignment (BPA), and dealt with evidence theory. Within the hybrid SIP-ETFE/SEA approach, the mid-frequency response of interest, such as the ensemble average of the energy response and the cross-spectrum response, is calculated analytically by using the conventional hybrid FE/SEA method. Inspired by the probability theory, the intervals of the mean value, variance and cumulative distribution are used to describe the distribution characteristics of mid-frequency responses of built-up systems with epistemic uncertainties. In order to alleviate the computational burdens for the extreme value analysis, the sub-interval perturbation technique based on the first-order Taylor series expansion is used in ETFE/SEA model to acquire the lower and upper bounds of the mid-frequency responses over each focal element. Three numerical examples are given to illustrate the feasibility and effectiveness of the proposed method.

Statistical testing and power analysis for brain-wide association study.

PubMed

Gong, Weikang; Wan, Lin; Lu, Wenlian; Ma, Liang; Cheng, Fan; Cheng, Wei; Grünewald, Stefan; Feng, Jianfeng

2018-04-05

The identification of connexel-wise associations, which involves examining functional connectivities between pairwise voxels across the whole brain, is both statistically and computationally challenging. Although such a connexel-wise methodology has recently been adopted by brain-wide association studies (BWAS) to identify connectivity changes in several mental disorders, such as schizophrenia, autism and depression, the multiple correction and power analysis methods designed specifically for connexel-wise analysis are still lacking. Therefore, we herein report the development of a rigorous statistical framework for connexel-wise significance testing based on the Gaussian random field theory. It includes controlling the family-wise error rate (FWER) of multiple hypothesis testings using topological inference methods, and calculating power and sample size for a connexel-wise study. Our theoretical framework can control the false-positive rate accurately, as validated empirically using two resting-state fMRI datasets. Compared with Bonferroni correction and false discovery rate (FDR), it can reduce false-positive rate and increase statistical power by appropriately utilizing the spatial information of fMRI data. Importantly, our method bypasses the need of non-parametric permutation to correct for multiple comparison, thus, it can efficiently tackle large datasets with high resolution fMRI images. The utility of our method is shown in a case-control study. Our approach can identify altered functional connectivities in a major depression disorder dataset, whereas existing methods fail. A software package is available at https://github.com/weikanggong/BWAS. Copyright © 2018 Elsevier B.V. All rights reserved.

The contribution of Raman spectroscopy to the analytical quality control of cytotoxic drugs in a hospital environment: eliminating the exposure risks for staff members and their work environment.

PubMed

Bourget, Philippe; Amin, Alexandre; Vidal, Fabrice; Merlette, Christophe; Troude, Pénélope; Baillet-Guffroy, Arlette

2014-08-15

The purpose of the study was to perform a comparative analysis of the technical performance, respective costs and environmental effect of two invasive analytical methods (HPLC and UV/visible-FTIR) as compared to a new non-invasive analytical technique (Raman spectroscopy). Three pharmacotherapeutic models were used to compare the analytical performances of the three analytical techniques. Statistical inter-method correlation analysis was performed using non-parametric correlation rank tests. The study's economic component combined calculations relative to the depreciation of the equipment and the estimated cost of an AQC unit of work. In any case, analytical validation parameters of the three techniques were satisfactory, and strong correlations between the two spectroscopic techniques vs. HPLC were found. In addition, Raman spectroscopy was found to be superior as compared to the other techniques for numerous key criteria including a complete safety for operators and their occupational environment, a non-invasive procedure, no need for consumables, and a low operating cost. Finally, Raman spectroscopy appears superior for technical, economic and environmental objectives, as compared with the other invasive analytical methods. Copyright © 2014 Elsevier B.V. All rights reserved.

Illiquidity premium and expected stock returns in the UK: A new approach

NASA Astrophysics Data System (ADS)

Chen, Jiaqi; Sherif, Mohamed

2016-09-01

This study examines the relative importance of liquidity risk for the time-series and cross-section of stock returns in the UK. We propose a simple way to capture the multidimensionality of illiquidity. Our analysis indicates that existing illiquidity measures have considerable asset specific components, which justifies our new approach. Further, we use an alternative test of the Amihud (2002) measure and parametric and non-parametric methods to investigate whether liquidity risk is priced in the UK. We find that the inclusion of the illiquidity factor in the capital asset pricing model plays a significant role in explaining the cross-sectional variation in stock returns, in particular with the Fama-French three-factor model. Further, using Hansen-Jagannathan non-parametric bounds, we find that the illiquidity-augmented capital asset pricing models yield a small distance error, other non-liquidity based models fail to yield economically plausible distance values. Our findings have important implications for managing the liquidity risk of equity portfolios.

Non-Parametric Collision Probability for Low-Velocity Encounters

NASA Technical Reports Server (NTRS)

Carpenter, J. Russell

2007-01-01

An implicit, but not necessarily obvious, assumption in all of the current techniques for assessing satellite collision probability is that the relative position uncertainty is perfectly correlated in time. If there is any mis-modeling of the dynamics in the propagation of the relative position error covariance matrix, time-wise de-correlation of the uncertainty will increase the probability of collision over a given time interval. The paper gives some examples that illustrate this point. This paper argues that, for the present, Monte Carlo analysis is the best available tool for handling low-velocity encounters, and suggests some techniques for addressing the issues just described. One proposal is for the use of a non-parametric technique that is widely used in actuarial and medical studies. The other suggestion is that accurate process noise models be used in the Monte Carlo trials to which the non-parametric estimate is applied. A further contribution of this paper is a description of how the time-wise decorrelation of uncertainty increases the probability of collision.

Can you trust the parametric standard errors in nonlinear least squares? Yes, with provisos.

PubMed

Tellinghuisen, Joel

2018-04-01

Questions about the reliability of parametric standard errors (SEs) from nonlinear least squares (LS) algorithms have led to a general mistrust of these precision estimators that is often unwarranted. The importance of non-Gaussian parameter distributions is illustrated by converting linear models to nonlinear by substituting e A , ln A, and 1/A for a linear parameter a. Monte Carlo (MC) simulations characterize parameter distributions in more complex cases, including when data have varying uncertainty and should be weighted, but weights are neglected. This situation leads to loss of precision and erroneous parametric SEs, as is illustrated for the Lineweaver-Burk analysis of enzyme kinetics data and the analysis of isothermal titration calorimetry data. Non-Gaussian parameter distributions are generally asymmetric and biased. However, when the parametric SE is <10% of the magnitude of the parameter, both the bias and the asymmetry can usually be ignored. Sometimes nonlinear estimators can be redefined to give more normal distributions and better convergence properties. Variable data uncertainty, or heteroscedasticity, can sometimes be handled by data transforms but more generally requires weighted LS, which in turn require knowledge of the data variance. Parametric SEs are rigorously correct in linear LS under the usual assumptions, and are a trustworthy approximation in nonlinear LS provided they are sufficiently small - a condition favored by the abundant, precise data routinely collected in many modern instrumental methods. Copyright © 2018 Elsevier B.V. All rights reserved.

Mathematical models of cytotoxic effects in endpoint tumor cell line assays: critical assessment of the application of a single parametric value as a standard criterion to quantify the dose-response effects and new unexplored proposal formats.

PubMed

Calhelha, Ricardo C; Martínez, Mireia A; Prieto, M A; Ferreira, Isabel C F R

2017-10-23

The development of convenient tools for describing and quantifying the effects of standard and novel therapeutic agents is essential for the research community, to perform more precise evaluations. Although mathematical models and quantification criteria have been exchanged in the last decade between different fields of study, there are relevant methodologies that lack proper mathematical descriptions and standard criteria to quantify their responses. Therefore, part of the relevant information that can be drawn from the experimental results obtained and the quantification of its statistical reliability are lost. Despite its relevance, there is not a standard form for the in vitro endpoint tumor cell lines' assays (TCLA) that enables the evaluation of the cytotoxic dose-response effects of anti-tumor drugs. The analysis of all the specific problems associated with the diverse nature of the available TCLA used is unfeasible. However, since most TCLA share the main objectives and similar operative requirements, we have chosen the sulforhodamine B (SRB) colorimetric assay for cytotoxicity screening of tumor cell lines as an experimental case study. In this work, the common biological and practical non-linear dose-response mathematical models are tested against experimental data and, following several statistical analyses, the model based on the Weibull distribution was confirmed as the convenient approximation to test the cytotoxic effectiveness of anti-tumor compounds. Then, the advantages and disadvantages of all the different parametric criteria derived from the model, which enable the quantification of the dose-response drug-effects, are extensively discussed. Therefore, model and standard criteria for easily performing the comparisons between different compounds are established. The advantages include a simple application, provision of parametric estimations that characterize the response as standard criteria, economization of experimental effort and enabling rigorous comparisons among the effects of different compounds and experimental approaches. In all experimental data fitted, the calculated parameters were always statistically significant, the equations proved to be consistent and the correlation coefficient of determination was, in most of the cases, higher than 0.98.

Methods to assess pecan scab

USDA-ARS?s Scientific Manuscript database

Pecan scab (Fusicladium effusum [G. Winter]) is the most important disease of pecan in the U.S. Measuring the severity of scab accurately and reliably and providing data amenable to analysis using parametric statistics is important where treatments are being compared to minimize the risk of Type II ...

A Non-parametric Cutout Index for Robust Evaluation of Identified Proteins*

PubMed Central

Serang, Oliver; Paulo, Joao; Steen, Hanno; Steen, Judith A.

2013-01-01

This paper proposes a novel, automated method for evaluating sets of proteins identified using mass spectrometry. The remaining peptide-spectrum match score distributions of protein sets are compared to an empirical absent peptide-spectrum match score distribution, and a Bayesian non-parametric method reminiscent of the Dirichlet process is presented to accurately perform this comparison. Thus, for a given protein set, the process computes the likelihood that the proteins identified are correctly identified. First, the method is used to evaluate protein sets chosen using different protein-level false discovery rate (FDR) thresholds, assigning each protein set a likelihood. The protein set assigned the highest likelihood is used to choose a non-arbitrary protein-level FDR threshold. Because the method can be used to evaluate any protein identification strategy (and is not limited to mere comparisons of different FDR thresholds), we subsequently use the method to compare and evaluate multiple simple methods for merging peptide evidence over replicate experiments. The general statistical approach can be applied to other types of data (e.g. RNA sequencing) and generalizes to multivariate problems. PMID:23292186

Temperature, Not Fine Particulate Matter (PM2.5), is Causally Associated with Short-Term Acute Daily Mortality Rates: Results from One Hundred United States Cities

PubMed Central

Cox, Tony; Popken, Douglas; Ricci, Paolo F

2013-01-01

Exposures to fine particulate matter (PM2.5) in air (C) have been suspected of contributing causally to increased acute (e.g., same-day or next-day) human mortality rates (R). We tested this causal hypothesis in 100 United States cities using the publicly available NMMAPS database. Although a significant, approximately linear, statistical C-R association exists in simple statistical models, closer analysis suggests that it is not causal. Surprisingly, conditioning on other variables that have been extensively considered in previous analyses (usually using splines or other smoothers to approximate their effects), such as month of the year and mean daily temperature, suggests that they create strong, nonlinear confounding that explains the statistical association between PM2.5 and mortality rates in this data set. As this finding disagrees with conventional wisdom, we apply several different techniques to examine it. Conditional independence tests for potential causation, non-parametric classification tree analysis, Bayesian Model Averaging (BMA), and Granger-Sims causality testing, show no evidence that PM2.5 concentrations have any causal impact on increasing mortality rates. This apparent absence of a causal C-R relation, despite their statistical association, has potentially important implications for managing and communicating the uncertain health risks associated with, but not necessarily caused by, PM2.5 exposures. PMID:23983662

3D Simulation as a Learning Environment for Acquiring the Skill of Self-Management: An Experience Involving Spanish University Students of Education

ERIC Educational Resources Information Center

Cela-Ranilla, Jose María; Esteve-Gonzalez, Vanessa; Esteve-Mon, Francesc; Gisbert-Cervera, Merce

2014-01-01

In this study we analyze how 57 Spanish university students of Education developed a learning process in a virtual world by conducting activities that involved the skill of self-management. The learning experience comprised a serious game designed in a 3D simulation environment. Descriptive statistics and non-parametric tests were used in the…

Distinct pattern of cerebral blood flow alterations specific to schizophrenics experiencing auditory verbal hallucinations with and without insight: a pilot study.

PubMed

Jing, Rixing; Huang, Jiangjie; Jiang, Deguo; Lin, Xiaodong; Ma, Xiaolei; Tian, Hongjun; Li, Jie; Zhuo, Chuanjun

2018-01-23

Schizophrenia is associated with widespread and complex cerebral blood flow (CBF) disturbance. Auditory verbal hallucinations (AVH) and insight are the core symptoms of schizophrenia. However, to the best of our knowledge, very few studies have assessed the CBF characteristics of the AVH suffered by schizophrenic patients with and without insight. Based on our previous findings, Using a 3D pseudo-continuous ASL (pcASL) technique, we investigated the differences in AVH-related CBF alterations in schizophrenia patients with and without insight. We used statistical parametric mapping (SPM8) and statistical non-parametric mapping (SnPM13) to perform the fMRI analysis. We found that AVH-schizophrenia patients without insight showed an increased CBF in the left temporal pole and a decreased CBF in the right middle frontal gyrus when compared to AVH-schizophrenia patients with insight. Our novel findings suggest that AVH-schizophrenia patients without insight possess a more complex CBF disturbance. Simultaneously, our findings also incline to support the idea that the CBF aberrant in some specific brain regions may be the common neural basis of insight and AVH. Our findings support the mostly current hypotheses regarding AVH to some extent. Although our findings come from a small sample, it provide the evidence that indicate us to conduct a larger study to thoroughly explore the mechanisms of schizophrenia, especially the core symptoms of AVHs and insight.

BROCCOLI: Software for fast fMRI analysis on many-core CPUs and GPUs

PubMed Central

Eklund, Anders; Dufort, Paul; Villani, Mattias; LaConte, Stephen

2014-01-01

Analysis of functional magnetic resonance imaging (fMRI) data is becoming ever more computationally demanding as temporal and spatial resolutions improve, and large, publicly available data sets proliferate. Moreover, methodological improvements in the neuroimaging pipeline, such as non-linear spatial normalization, non-parametric permutation tests and Bayesian Markov Chain Monte Carlo approaches, can dramatically increase the computational burden. Despite these challenges, there do not yet exist any fMRI software packages which leverage inexpensive and powerful graphics processing units (GPUs) to perform these analyses. Here, we therefore present BROCCOLI, a free software package written in OpenCL (Open Computing Language) that can be used for parallel analysis of fMRI data on a large variety of hardware configurations. BROCCOLI has, for example, been tested with an Intel CPU, an Nvidia GPU, and an AMD GPU. These tests show that parallel processing of fMRI data can lead to significantly faster analysis pipelines. This speedup can be achieved on relatively standard hardware, but further, dramatic speed improvements require only a modest investment in GPU hardware. BROCCOLI (running on a GPU) can perform non-linear spatial normalization to a 1 mm3 brain template in 4–6 s, and run a second level permutation test with 10,000 permutations in about a minute. These non-parametric tests are generally more robust than their parametric counterparts, and can also enable more sophisticated analyses by estimating complicated null distributions. Additionally, BROCCOLI includes support for Bayesian first-level fMRI analysis using a Gibbs sampler. The new software is freely available under GNU GPL3 and can be downloaded from github (https://github.com/wanderine/BROCCOLI/). PMID:24672471

A pilot DTI analysis in patients with recent onset post-traumatic stress disorder

NASA Astrophysics Data System (ADS)

Liu, Yang; Li, Liang; Li, Baojuan; Zhang, Xi; Lu, Hongbing

2016-03-01

To explore the alteration in white matter between survivors with recent onset post-traumatic stress disorder (PTSD) and without PTSD, who survived from the same coal mine flood disaster, the diffusion tensor imaging (DTI) sequences were analyzed using DTI studio and statistical parametric mapping (SPM) packages in this paper. From DTI sequence, the fractional anisotropy (FA) value describes the degree of anisotropy of a diffusion process, while the apparent diffusion coefficient (ADC) value reflects the magnitude of water diffusion. The DTI analyses between PTSD and non-PTSD indicate lower FA values in the right caudate nucleus, right middle temporal gyrus, right fusiform gyrus, and right superior temporal gyrus, and higher ADC values in the right superior temporal gyrus and right corpus callosum of the subjects with PTSD. These results are partly in line with our previous volume and cortical thickness analyses, indicating the importance of multi-modality analysis for PTSD.

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

Efficiency, Technology and Productivity Change in Australian Universities, 1998-2003

ERIC Educational Resources Information Center

Worthington, Andrew C.; Lee, Boon L.

2008-01-01

In this study, productivity growth in 35 Australian universities is investigated using non-parametric frontier techniques over the period 1998-2003. The five inputs included in the analysis are full-time equivalent academic and non-academic staff, non-labor expenditure and undergraduate and postgraduate student load while the six outputs are…

Identification of curriculum content for a renewable energy graduate degree program

NASA Astrophysics Data System (ADS)

Haughery, John R.

There currently exists a disconnect between renewable energy industry workforce needs and academic program proficiencies. This is evidenced by an absence of clear curriculum content on renewable energy graduate program websites. The purpose of this study was to identify a set of curriculum content for graduate degrees in renewable energy. At the conclusion, a clear list of 42 content items was identified and statistically ranked. The content items identified were based on a review of literature from government initiatives, professional society's body of knowledge, and related research studies. Leaders and experts in the field of renewable energy and sustainability were surveyed, using a five-point Likert-Scale model. This allowed each item's importance level to be analyzed and prioritized based on non-parametric statistical analysis methods. The study found seven competency items to be very important , 30 to be important, and five to be somewhat important. The results were also appropriate for use as a framework in developing or improving renewable energy graduate programs.

Marginally specified priors for non-parametric Bayesian estimation

PubMed Central

Kessler, David C.; Hoff, Peter D.; Dunson, David B.

2014-01-01

Summary Prior specification for non-parametric Bayesian inference involves the difficult task of quantifying prior knowledge about a parameter of high, often infinite, dimension. A statistician is unlikely to have informed opinions about all aspects of such a parameter but will have real information about functionals of the parameter, such as the population mean or variance. The paper proposes a new framework for non-parametric Bayes inference in which the prior distribution for a possibly infinite dimensional parameter is decomposed into two parts: an informative prior on a finite set of functionals, and a non-parametric conditional prior for the parameter given the functionals. Such priors can be easily constructed from standard non-parametric prior distributions in common use and inherit the large support of the standard priors on which they are based. Additionally, posterior approximations under these informative priors can generally be made via minor adjustments to existing Markov chain approximation algorithms for standard non-parametric prior distributions. We illustrate the use of such priors in the context of multivariate density estimation using Dirichlet process mixture models, and in the modelling of high dimensional sparse contingency tables. PMID:25663813

Statistical variation in progressive scrambling

NASA Astrophysics Data System (ADS)

Clark, Robert D.; Fox, Peter C.

2004-07-01

The two methods most often used to evaluate the robustness and predictivity of partial least squares (PLS) models are cross-validation and response randomization. Both methods may be overly optimistic for data sets that contain redundant observations, however. The kinds of perturbation analysis widely used for evaluating model stability in the context of ordinary least squares regression are only applicable when the descriptors are independent of each other and errors are independent and normally distributed; neither assumption holds for QSAR in general and for PLS in particular. Progressive scrambling is a novel, non-parametric approach to perturbing models in the response space in a way that does not disturb the underlying covariance structure of the data. Here, we introduce adjustments for two of the characteristic values produced by a progressive scrambling analysis - the deprecated predictivity (Q_s^{ast^2}) and standard error of prediction (SDEP s * ) - that correct for the effect of introduced perturbation. We also explore the statistical behavior of the adjusted values (Q_0^{ast^2} and SDEP 0 * ) and the sensitivity to perturbation (d q 2/d r yy ' 2). It is shown that the three statistics are all robust for stable PLS models, in terms of the stochastic component of their determination and of their variation due to sampling effects involved in training set selection.

Accounting for Non-Gaussian Sources of Spatial Correlation in Parametric Functional Magnetic Resonance Imaging Paradigms II: A Method to Obtain First-Level Analysis Residuals with Uniform and Gaussian Spatial Autocorrelation Function and Independent and Identically Distributed Time-Series.

PubMed

Gopinath, Kaundinya; Krishnamurthy, Venkatagiri; Lacey, Simon; Sathian, K

2018-02-01

In a recent study Eklund et al. have shown that cluster-wise family-wise error (FWE) rate-corrected inferences made in parametric statistical method-based functional magnetic resonance imaging (fMRI) studies over the past couple of decades may have been invalid, particularly for cluster defining thresholds less stringent than p < 0.001; principally because the spatial autocorrelation functions (sACFs) of fMRI data had been modeled incorrectly to follow a Gaussian form, whereas empirical data suggest otherwise. Hence, the residuals from general linear model (GLM)-based fMRI activation estimates in these studies may not have possessed a homogenously Gaussian sACF. Here we propose a method based on the assumption that heterogeneity and non-Gaussianity of the sACF of the first-level GLM analysis residuals, as well as temporal autocorrelations in the first-level voxel residual time-series, are caused by unmodeled MRI signal from neuronal and physiological processes as well as motion and other artifacts, which can be approximated by appropriate decompositions of the first-level residuals with principal component analysis (PCA), and removed. We show that application of this method yields GLM residuals with significantly reduced spatial correlation, nearly Gaussian sACF and uniform spatial smoothness across the brain, thereby allowing valid cluster-based FWE-corrected inferences based on assumption of Gaussian spatial noise. We further show that application of this method renders the voxel time-series of first-level GLM residuals independent, and identically distributed across time (which is a necessary condition for appropriate voxel-level GLM inference), without having to fit ad hoc stochastic colored noise models. Furthermore, the detection power of individual subject brain activation analysis is enhanced. This method will be especially useful for case studies, which rely on first-level GLM analysis inferences.

Two types of exercise-induced neuroplasticity in congenital hemiparesis: a transcranial magnetic stimulation, functional MRI, and magnetoencephalography study.

PubMed

Juenger, Hendrik; Kuhnke, Nicola; Braun, Christoph; Ummenhofer, Frank; Wilke, Marko; Walther, Michael; Koerte, Inga; Delvendahl, Igor; Jung, Nikolai H; Berweck, Steffen; Staudt, Martin; Mall, Volker

2013-10-01

Early unilateral brain lesions can lead to a persistence of ipsilateral corticospinal projections from the contralesional hemisphere, which can enable the contralesional hemisphere to exert motor control over the paretic hand. In contrast to the primary motor representation (M1), the primary somatosensory representation (S1) of the paretic hand always remains in the lesioned hemisphere. Here, we report on differences in exercise-induced neuroplasticity between individuals with such ipsilateral motor projections (ipsi) and individuals with early unilateral lesions but 'healthy' contralateral motor projections (contra). Sixteen children and young adults with congenital hemiparesis participated in the study (contralateral [Contra] group: n=7, four females, three males; age range 10-30y, median age 16y; ipsilateral [Ipsi] group: n=9, four females, five males; age range 11-31y, median age 12y; Manual Ability Classification System levels I to II in all individuals in both groups). The participants underwent a 12-day intervention of constraint-induced movement therapy (CIMT), consisting of individual training (2h/d) and group training (8h/d). Before and after CIMT, hand function was tested using the Wolf Motor Function Test (WMFT) and diverging neuroplastic effects were observed by transcranial magnetic stimulation (TMS), functional magnetic resonance imaging (fMRI), and magnetoencephalography (MEG). Statistical analysis of TMS data was performed using the non-parametric Wilcoxon signed-rank test for pair-wise comparison; for fMRI standard statistical parametric and non-parametric mapping (SPM5, SnPM3) procedures (first level/second level) were carried out. Statistical analyses of MEG data involved analyses of variance (ANOVA) and t-tests. While MEG demonstrated a significant increase in S1 activation in both groups (p=0.012), TMS showed a decrease in M1 excitability in the Ipsi group (p=0.036), but an increase in M1 excitability in the Contra group (p=0.043). Similarly, fMRI showed a decrease in M1 activation in the Ipsi group, but an increase in activation in the M1-S1 region in the Contra group (for both groups p<0.001 [SnPM3] within the search volume). Different patterns of sensorimotor (re)organization in individuals with early unilateral lesions show, on a cortical level, different patterns of exercise-induced neuroplasticity. The findings help to improve the understanding of the general principles of sensorimotor learning and will help to develop more specific therapies for different pathologies in congenital hemiparesis. © 2013 Mac Keith Press.

A New Hybrid-Multiscale SSA Prediction of Non-Stationary Time Series

NASA Astrophysics Data System (ADS)

Ghanbarzadeh, Mitra; Aminghafari, Mina

2016-02-01

Singular spectral analysis (SSA) is a non-parametric method used in the prediction of non-stationary time series. It has two parameters, which are difficult to determine and very sensitive to their values. Since, SSA is a deterministic-based method, it does not give good results when the time series is contaminated with a high noise level and correlated noise. Therefore, we introduce a novel method to handle these problems. It is based on the prediction of non-decimated wavelet (NDW) signals by SSA and then, prediction of residuals by wavelet regression. The advantages of our method are the automatic determination of parameters and taking account of the stochastic structure of time series. As shown through the simulated and real data, we obtain better results than SSA, a non-parametric wavelet regression method and Holt-Winters method.

Actigraphic investigations on the activity-rest behavior of right- and left-handed students.

PubMed

Lehnkering, Hanna; Strauss, Andreas; Wegner, Brigitte; Siegmund, Renate

2006-01-01

The aim of this study was to explore differences between left-and right-handed subjects in sleep duration. Sleep and activity patterns were continuously registered for 12 days using actometers on 20 left-handed and 20 right-handed medical students in Berlin. Handedness was determined by a modified version of the Edinburgh handedness inventory. Each participant wore one actometer on each wrist. Actiwatch Sleep Analysis Software (CNT, UK) was used to evaluate the data, and statistical calculations were performed with a non-parametric variance analysis. A significant difference in mean sleep duration between left-handers (7.9 h) and right-handers (7.3 h) was determined (p=0.025 for measurement made on the dominant hand and p=0.013 for ones made on the non-dominant hand). In contrast, the maximal phase of daily activity (acrophase) did not show any difference between the two groups. The difference in sleep duration might be caused by either the greater effort required for left-handers to cope in a right-handed world or by structural brain differences.

Improved statistical assessment of a long-term groundwater-quality dataset with a non-parametric permutation method

NASA Astrophysics Data System (ADS)

Thomas, M. A.

2016-12-01

The Waste Isolation Pilot Plant (WIPP) is the only deep geological repository for transuranic waste in the United States. As the Science Advisor for the WIPP, Sandia National Laboratories annually evaluates site data against trigger values (TVs), metrics whose violation is indicative of conditions that may impact long-term repository performance. This study focuses on a groundwater-quality dataset used to redesign a TV for the Culebra Dolomite Member (Culebra) of the Permian-age Rustler Formation. Prior to this study, a TV violation occurred if the concentration of a major ion fell outside a range defined as the mean +/- two standard deviations. The ranges were thought to denote conditions that 95% of future values would fall within. Groundwater-quality data used in evaluating compliance, however, are rarely normally distributed. To create a more robust Culebra groundwater-quality TV, this study employed the randomization test, a non-parametric permutation method. Recent groundwater compositions considered TV violations under the original ion concentration ranges are now interpreted as false positives in light of the insignificant p-values calculated with the randomization test. This work highlights that the normality assumption can weaken as the size of a groundwater-quality dataset grows over time. Non-parametric permutation methods are an attractive option because no assumption about the statistical distribution is required and calculating all combinations of the data is an increasingly tractable problem with modern workstations. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000. This research is funded by WIPP programs administered by the Office of Environmental Management (EM) of the U.S. Department of Energy. SAND2016-7306A

Language Learning Strategy Use and Reading Achievement

ERIC Educational Resources Information Center

Ghafournia, Narjes

2014-01-01

The current study investigated the differences across the varying levels of EFL learners in the frequency and choice of learning strategies. Using a reading test, questionnaire, and parametric statistical analysis, the findings yielded up discrepancies among the participants in the implementation of language-learning strategies concerning their…

Analysis of the chronic lower limb injuries occurrence in step aerobic instructors in relation to their working step class profile: a three year longitudinal prospective study.

PubMed

Malliou, P; Rokka, S; Beneka, A; Gioftsidou, A; Mavromoustakos, S; Godolias, G

2014-01-01

There is limited information on injury patterns in Step Aerobic Instructors (SAI) who exclusively execute "step" aerobic classes. To record the type and the anatomical position in relation to diagnosis of muscular skeletal injuries in step aerobic instructors. Also, to analyse the days of absence due to chronic injury in relation to weekly working hours, height of the step platform, working experience and working surface and footwear during the step class. The Step Aerobic Instructors Injuries Questionnaire was developed, and then validity and reliability indices were calculated. 63 SAI completed the questionnaire. For the statistical analysis of the data, the method used was the analysis of frequencies, the non-parametric test χ^{2} (chi square distribution), correlation and linear and logistic regressions analysis from the SPSS statistical package. 63 SAI reported 115 injuries that required more than 2 days absence from step aerobic classes. The chronic lower extremity injuries were 73.5%, with the leg pain, the anterior knee pain, the plantar tendinopathy and the Achilles tendinopathy being most common overuse syndromes. The working hours, the platform height, the years of aerobic dance seem to affect the days of absence due to chronic lower limb injury occurrence in SAI.

Synthetic data sets for the identification of key ingredients for RNA-seq differential analysis.

PubMed

Rigaill, Guillem; Balzergue, Sandrine; Brunaud, Véronique; Blondet, Eddy; Rau, Andrea; Rogier, Odile; Caius, José; Maugis-Rabusseau, Cathy; Soubigou-Taconnat, Ludivine; Aubourg, Sébastien; Lurin, Claire; Martin-Magniette, Marie-Laure; Delannoy, Etienne

2018-01-01

Numerous statistical pipelines are now available for the differential analysis of gene expression measured with RNA-sequencing technology. Most of them are based on similar statistical frameworks after normalization, differing primarily in the choice of data distribution, mean and variance estimation strategy and data filtering. We propose an evaluation of the impact of these choices when few biological replicates are available through the use of synthetic data sets. This framework is based on real data sets and allows the exploration of various scenarios differing in the proportion of non-differentially expressed genes. Hence, it provides an evaluation of the key ingredients of the differential analysis, free of the biases associated with the simulation of data using parametric models. Our results show the relevance of a proper modeling of the mean by using linear or generalized linear modeling. Once the mean is properly modeled, the impact of the other parameters on the performance of the test is much less important. Finally, we propose to use the simple visualization of the raw P-value histogram as a practical evaluation criterion of the performance of differential analysis methods on real data sets. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Identifying training needs of logging truck drivers using a skill inventory.

PubMed

Carnahan, B J

2004-11-01

The purpose of this research was to determine if the Driver Skill Inventory (DSI) could be used to characterize the self-assessed driving performance of commercial logging truck drivers. The DSI requires respondents to subjectively evaluate their own ability in regard to 15 different driving skills. The DSI responses of 1000 logging truck drivers were collected across three southeastern states. The underlying hypothesis in the current study was that DSI responses of these drivers would have similar reliability and factor structure as those DSI responses collected from non-commercial drivers in previous studies. Factor analysis of the data confirmed this hypothesis. Statistical analysis revealed that low self-ratings on various safety skill items within the DSI inventory were associated with: (1) inconsistency in using seat belts, (2) inconsistency in performing pre-trip inspections on logging trucks, and (3) committing moving violations. Conversely, high self-ratings ratings on various perceptual-motor skill items were associated with these same at-risk behaviors. The perceptual-motor skill items were also positively associated with negative attitudes toward driving regulations and the number of moving violations incurred over a three-year period. Non-parametric statistical analysis revealed that self-assessments were lowest for DSI skills pertaining to controlling one's anger while driving and managing the truck through a skid or slide. Results of the study confirmed that the DSI can be successfully applied to commercial logging truck drivers as part of an overall comprehensive training needs assessment.

Temporal Expression of Peripheral Blood Leukocyte Biomarkers in a Macaca fascicularis Infection Model of Tuberculosis; Comparison with Human Datasets and Analysis with Parametric/Non-parametric Tools for Improved Diagnostic Biomarker Identification

PubMed Central

Wareham, Alice; Lewandowski, Kuiama S.; Williams, Ann; Dennis, Michael J.; Sharpe, Sally; Vipond, Richard; Silman, Nigel; Ball, Graham

2016-01-01

A temporal study of gene expression in peripheral blood leukocytes (PBLs) from a Mycobacterium tuberculosis primary, pulmonary challenge model Macaca fascicularis has been conducted. PBL samples were taken prior to challenge and at one, two, four and six weeks post-challenge and labelled, purified RNAs hybridised to Operon Human Genome AROS V4.0 slides. Data analyses revealed a large number of differentially regulated gene entities, which exhibited temporal profiles of expression across the time course study. Further data refinements identified groups of key markers showing group-specific expression patterns, with a substantial reprogramming event evident at the four to six week interval. Selected statistically-significant gene entities from this study and other immune and apoptotic markers were validated using qPCR, which confirmed many of the results obtained using microarray hybridisation. These showed evidence of a step-change in gene expression from an ‘early’ FOS-associated response, to a ‘late’ predominantly type I interferon-driven response, with coincident reduction of expression of other markers. Loss of T-cell-associate marker expression was observed in responsive animals, with concordant elevation of markers which may be associated with a myeloid suppressor cell phenotype e.g. CD163. The animals in the study were of different lineages and these Chinese and Mauritian cynomolgous macaque lines showed clear evidence of differing susceptibilities to Tuberculosis challenge. We determined a number of key differences in response profiles between the groups, particularly in expression of T-cell and apoptotic makers, amongst others. These have provided interesting insights into innate susceptibility related to different host `phenotypes. Using a combination of parametric and non-parametric artificial neural network analyses we have identified key genes and regulatory pathways which may be important in early and adaptive responses to TB. Using comparisons between data outputs of each analytical pipeline and comparisons with previously published Human TB datasets, we have delineated a subset of gene entities which may be of use for biomarker diagnostic test development. PMID:27228113

Application of multivariate Gaussian detection theory to known non-Gaussian probability density functions

NASA Astrophysics Data System (ADS)

Schwartz, Craig R.; Thelen, Brian J.; Kenton, Arthur C.

1995-06-01

A statistical parametric multispectral sensor performance model was developed by ERIM to support mine field detection studies, multispectral sensor design/performance trade-off studies, and target detection algorithm development. The model assumes target detection algorithms and their performance models which are based on data assumed to obey multivariate Gaussian probability distribution functions (PDFs). The applicability of these algorithms and performance models can be generalized to data having non-Gaussian PDFs through the use of transforms which convert non-Gaussian data to Gaussian (or near-Gaussian) data. An example of one such transform is the Box-Cox power law transform. In practice, such a transform can be applied to non-Gaussian data prior to the introduction of a detection algorithm that is formally based on the assumption of multivariate Gaussian data. This paper presents an extension of these techniques to the case where the joint multivariate probability density function of the non-Gaussian input data is known, and where the joint estimate of the multivariate Gaussian statistics, under the Box-Cox transform, is desired. The jointly estimated multivariate Gaussian statistics can then be used to predict the performance of a target detection algorithm which has an associated Gaussian performance model.

Assessment of the quality and content of website health information about herbal remedies for menopausal symptoms.

PubMed

Sowter, Julie; Astin, Felicity; Dye, Louise; Marshall, Paul; Knapp, Peter

2016-06-01

To assess the quality, readability and coverage of website information about herbal remedies for menopausal symptoms. A purposive sample of commercial and non-commercial websites was assessed for quality (DISCERN), readability (SMOG) and information coverage. Non-parametric and parametric tests were used to explain the variability of these factors across types of websites and to assess associations between website quality and information coverage. 39 sites were assessed. Median quality and information coverage scores were 44/80 and 11/30 respectively. The median readability score was 18.7, similar to UK broadsheets. Commercial websites scored significantly lower on quality (p=0.014), but there were no statistical differences for information coverage or readability. There was a significant positive correlation between information quality and coverage scores irrespective of website provider (r=0.69, p<0.001, n=39). Overall website quality and information coverage are poor and the required reading level high. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Comparing of Cox model and parametric models in analysis of effective factors on event time of neuropathy in patients with type 2 diabetes.

PubMed

Kargarian-Marvasti, Sadegh; Rimaz, Shahnaz; Abolghasemi, Jamileh; Heydari, Iraj

2017-01-01

Cox proportional hazard model is the most common method for analyzing the effects of several variables on survival time. However, under certain circumstances, parametric models give more precise estimates to analyze survival data than Cox. The purpose of this study was to investigate the comparative performance of Cox and parametric models in a survival analysis of factors affecting the event time of neuropathy in patients with type 2 diabetes. This study included 371 patients with type 2 diabetes without neuropathy who were registered at Fereydunshahr diabetes clinic. Subjects were followed up for the development of neuropathy between 2006 to March 2016. To investigate the factors influencing the event time of neuropathy, significant variables in univariate model ( P < 0.20) were entered into the multivariate Cox and parametric models ( P < 0.05). In addition, Akaike information criterion (AIC) and area under ROC curves were used to evaluate the relative goodness of fitted model and the efficiency of each procedure, respectively. Statistical computing was performed using R software version 3.2.3 (UNIX platforms, Windows and MacOS). Using Kaplan-Meier, survival time of neuropathy was computed 76.6 ± 5 months after initial diagnosis of diabetes. After multivariate analysis of Cox and parametric models, ethnicity, high-density lipoprotein and family history of diabetes were identified as predictors of event time of neuropathy ( P < 0.05). According to AIC, "log-normal" model with the lowest Akaike's was the best-fitted model among Cox and parametric models. According to the results of comparison of survival receiver operating characteristics curves, log-normal model was considered as the most efficient and fitted model.

Improvement of Olfactory Function With High Frequency Non-invasive Auricular Electrostimulation in Healthy Humans

PubMed Central

Maharjan, Ashim; Wang, Eunice; Peng, Mei; Cakmak, Yusuf O.

2018-01-01

In past literature on animal models, invasive vagal nerve stimulation using high frequencies has shown to be effective at modulating the activity of the olfactory bulb (OB). Recent advances in invasive vagal nerve stimulation in humans, despite previous findings in animal models, used low frequency stimulation and found no effect on the olfactory functioning. The present article aimed to test potential effects of non-invasive, high and low frequency vagal nerve stimulation in humans, with supplementary exploration of the orbitofrontal cortex using near-infrared spectroscopy (NIRS). Healthy, male adult participants (n = 18) performed two olfactory tests [odor threshold test (OTT) and supra-threshold test (STT)] before and after receiving high-, low frequency vagal nerve stimulation and placebo (no stimulation). Participant's olfactory functioning was monitored using NIRS, and assessed with two behavioral olfactory tests. NIRS data of separate stimulation parameters were statistically analyzed using repeated-measures ANOVA across different stages. Data from olfactory tests were analyzed using paired parametric and non-parametric statistical tests. Only high frequency, non-invasive vagal nerve stimulation was able to positively modulate the performance of the healthy participants in the STT (p = 0.021, Wilcoxon sign-ranked test), with significant differences in NIRS (p = 0.014, post-hoc with Bonferroni correction) recordings of the right hemispheric, orbitofrontal cortex. The results from the current article implore further exploration of the neurocircuitry involved under vagal nerve stimulation and the effects of non-invasive, high frequency, vagal nerve stimulation toward olfactory dysfunction which showcase in Parkinson's and Alzheimer's Diseases. Despite the sufficient effect size (moderate effect, correlation coefficient (r): 0.39 for the STT) of the current study, future research should replicate the current findings with a larger cohort. PMID:29740266

Improvement of Olfactory Function With High Frequency Non-invasive Auricular Electrostimulation in Healthy Humans.

PubMed

Maharjan, Ashim; Wang, Eunice; Peng, Mei; Cakmak, Yusuf O

2018-01-01

In past literature on animal models, invasive vagal nerve stimulation using high frequencies has shown to be effective at modulating the activity of the olfactory bulb (OB). Recent advances in invasive vagal nerve stimulation in humans, despite previous findings in animal models, used low frequency stimulation and found no effect on the olfactory functioning. The present article aimed to test potential effects of non-invasive, high and low frequency vagal nerve stimulation in humans, with supplementary exploration of the orbitofrontal cortex using near-infrared spectroscopy (NIRS). Healthy, male adult participants ( n = 18) performed two olfactory tests [odor threshold test (OTT) and supra-threshold test (STT)] before and after receiving high-, low frequency vagal nerve stimulation and placebo (no stimulation). Participant's olfactory functioning was monitored using NIRS, and assessed with two behavioral olfactory tests. NIRS data of separate stimulation parameters were statistically analyzed using repeated-measures ANOVA across different stages. Data from olfactory tests were analyzed using paired parametric and non-parametric statistical tests. Only high frequency, non-invasive vagal nerve stimulation was able to positively modulate the performance of the healthy participants in the STT ( p = 0.021, Wilcoxon sign-ranked test), with significant differences in NIRS ( p = 0.014, post-hoc with Bonferroni correction ) recordings of the right hemispheric, orbitofrontal cortex. The results from the current article implore further exploration of the neurocircuitry involved under vagal nerve stimulation and the effects of non-invasive, high frequency, vagal nerve stimulation toward olfactory dysfunction which showcase in Parkinson's and Alzheimer's Diseases. Despite the sufficient effect size (moderate effect, correlation coefficient (r): 0.39 for the STT) of the current study, future research should replicate the current findings with a larger cohort.

Hammerstein system represention of financial volatility processes

NASA Astrophysics Data System (ADS)

Capobianco, E.

2002-05-01

We show new modeling aspects of stock return volatility processes, by first representing them through Hammerstein Systems, and by then approximating the observed and transformed dynamics with wavelet-based atomic dictionaries. We thus propose an hybrid statistical methodology for volatility approximation and non-parametric estimation, and aim to use the information embedded in a bank of volatility sources obtained by decomposing the observed signal with multiresolution techniques. Scale dependent information refers both to market activity inherent to different temporally aggregated trading horizons, and to a variable degree of sparsity in representing the signal. A decomposition of the expansion coefficients in least dependent coordinates is then implemented through Independent Component Analysis. Based on the described steps, the features of volatility can be more effectively detected through global and greedy algorithms.

Linkage analysis of chromosome 22q12-13 in a United Kingdom/Icelandic sample of 23 multiplex schizophrenia families

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

Kalsi, G.; Read, T.; Butler, R.

A possible linkage to a genetic subtype of schizophrenia and related disorders has been reported on the long arm of chromosome 22 at q12-13. However formal statistical tests in a combined sample could not reject homogeneity and prove that there was linked subgroup of families. We have studied 23 schizophrenia pedigrees to test whether some multiplex schizophrenia families may be linked to the microsatellite markers D22S274 and D22S283 which span the 22q12-13 region. Two point followed by multipoint lod and non-parametric linkage analyses under the assumption of heterogeneity provided no evidence for linkage over the relevant region. 16 refs., 4more » tabs.« less

Latest astronomical constraints on some non-linear parametric dark energy models

NASA Astrophysics Data System (ADS)

Yang, Weiqiang; Pan, Supriya; Paliathanasis, Andronikos

2018-04-01

We consider non-linear redshift-dependent equation of state parameters as dark energy models in a spatially flat Friedmann-Lemaître-Robertson-Walker universe. To depict the expansion history of the universe in such cosmological scenarios, we take into account the large-scale behaviour of such parametric models and fit them using a set of latest observational data with distinct origin that includes cosmic microwave background radiation, Supernove Type Ia, baryon acoustic oscillations, redshift space distortion, weak gravitational lensing, Hubble parameter measurements from cosmic chronometers, and finally the local Hubble constant from Hubble space telescope. The fitting technique avails the publicly available code Cosmological Monte Carlo (COSMOMC), to extract the cosmological information out of these parametric dark energy models. From our analysis, it follows that those models could describe the late time accelerating phase of the universe, while they are distinguished from the Λ-cosmology.

Assessing the Kansas water-level monitoring program: An example of the application of classical statistics to a geological problem

USGS Publications Warehouse

Davis, J.C.

2000-01-01

Geologists may feel that geological data are not amenable to statistical analysis, or at best require specialized approaches such as nonparametric statistics and geostatistics. However, there are many circumstances, particularly in systematic studies conducted for environmental or regulatory purposes, where traditional parametric statistical procedures can be beneficial. An example is the application of analysis of variance to data collected in an annual program of measuring groundwater levels in Kansas. Influences such as well conditions, operator effects, and use of the water can be assessed and wells that yield less reliable measurements can be identified. Such statistical studies have resulted in yearly improvements in the quality and reliability of the collected hydrologic data. Similar benefits may be achieved in other geological studies by the appropriate use of classical statistical tools.

Investigation of the photon statistics of parametric fluorescence in a traveling-wave parametric amplifier by means of self-homodyne tomography.

PubMed

Vasilyev, M; Choi, S K; Kumar, P; D'Ariano, G M

1998-09-01

Photon-number distributions for parametric fluorescence from a nondegenerate optical parametric amplifier are measured with a novel self-homodyne technique. These distributions exhibit the thermal-state character predicted by theory. However, a difference between the fluorescence gain and the signal gain of the parametric amplifier is observed. We attribute this difference to a change in the signal-beam profile during the traveling-wave pulsed amplification process.

Intraoperative identification of the facial nerve by needle electromyography stimulation with a burr

PubMed Central

KHAMGUSHKEEVA, N.N.; ANIKIN, I.A.; KORNEYENKOV, A.A.

2016-01-01

The purpose of this research is to improve the safety of surgery for patients with a pathology of the middle and inner ear by preventing damage to the facial nerve by conducting intraoperative monitoring of the facial nerve by needle electromyography with continuous stimulation with a burr. Patients and Methods The clinical part of the prospective study was carried out on 48 patients that were diagnosed with suppurative otitis media. After the surgery with intraoperative monitoring, the facial nerve with an intact bone wall was stimulated electrically in the potentially dangerous places of damage. Minimum (threshold) stimulation (mA) of the facial nerve with a threshold event of 100 μV was used to register EMG events. The anatomical part of the study was carried out on 30 unformalinized cadaver temporal bones from adult bodies. The statistical analysis of obtained data was carried out with parametric methods (Student’s t-test), non-parametric correlation (Spearman’s method) and regression analysis. Results It was found that 1 mA of threshold amperage corresponded to 0.8 mm thickness of the bone wall of the facial canal. Values of transosseous threshold stimulation in potentially dangerous sections of the injury to the facial nerve were obtained. Conclusion These data lower the risk of paresis (paralysis) of the facial muscles during otologic surgery. PMID:27142821

Review of Statistical Methods for Analysing Healthcare Resources and Costs

PubMed Central

Mihaylova, Borislava; Briggs, Andrew; O'Hagan, Anthony; Thompson, Simon G

2011-01-01

We review statistical methods for analysing healthcare resource use and costs, their ability to address skewness, excess zeros, multimodality and heavy right tails, and their ease for general use. We aim to provide guidance on analysing resource use and costs focusing on randomised trials, although methods often have wider applicability. Twelve broad categories of methods were identified: (I) methods based on the normal distribution, (II) methods following transformation of data, (III) single-distribution generalized linear models (GLMs), (IV) parametric models based on skewed distributions outside the GLM family, (V) models based on mixtures of parametric distributions, (VI) two (or multi)-part and Tobit models, (VII) survival methods, (VIII) non-parametric methods, (IX) methods based on truncation or trimming of data, (X) data components models, (XI) methods based on averaging across models, and (XII) Markov chain methods. Based on this review, our recommendations are that, first, simple methods are preferred in large samples where the near-normality of sample means is assured. Second, in somewhat smaller samples, relatively simple methods, able to deal with one or two of above data characteristics, may be preferable but checking sensitivity to assumptions is necessary. Finally, some more complex methods hold promise, but are relatively untried; their implementation requires substantial expertise and they are not currently recommended for wider applied work. Copyright © 2010 John Wiley & Sons, Ltd. PMID:20799344

Statistical properties of light from optical parametric oscillators

NASA Astrophysics Data System (ADS)

Vyas, Reeta; Singh, Surendra

2009-12-01

Coherence properties of light beams generated by optical parametric oscillators (OPOs) are discussed in the region of threshold. Analytic expressions, that are valid throughout the threshold region, for experimentally measurable quantities such as the mean and variance of photon number fluctuations, squeezing of field quadratures, and photon counting distributions are derived. These expressions describe non-Gaussian fluctuations of light in the region of threshold and reproduce Gaussian fluctuations below and above threshold, thus providing a bridge between below and above threshold regimes of operation. They are used to study the transformation of fluctuation properties of light as the OPOs make a transition from below to above threshold. The results for the OPOs are compared to those for the single-mode and two-mode lasers and their similarities and differences are discussed.

A Maximum Entropy Method for Particle Filtering

NASA Astrophysics Data System (ADS)

Eyink, Gregory L.; Kim, Sangil

2006-06-01

Standard ensemble or particle filtering schemes do not properly represent states of low priori probability when the number of available samples is too small, as is often the case in practical applications. We introduce here a set of parametric resampling methods to solve this problem. Motivated by a general H-theorem for relative entropy, we construct parametric models for the filter distributions as maximum-entropy/minimum-information models consistent with moments of the particle ensemble. When the prior distributions are modeled as mixtures of Gaussians, our method naturally generalizes the ensemble Kalman filter to systems with highly non-Gaussian statistics. We apply the new particle filters presented here to two simple test cases: a one-dimensional diffusion process in a double-well potential and the three-dimensional chaotic dynamical system of Lorenz.

Evaluating the Impact of the Number of Satellite Altimeters Used in an Assimilative Ocean Prediction System

DTIC Science & Technology

2010-01-01

indicates the scaled MB, MB95 MB 1 N N j51 (O j O)2 2 4 3 5 1/2 , (12) or the biweight version, MBbw9 5 MBbw hhO j iibw , (13) and the x axis denotes...RMSEbwunbiased hhO j iibw . (15) To investigate the impact of outliers, results from both the Gaussian statistics [Eqs. (12) and (14)] and the non- parametric

Acceleration and sensitivity analysis of lattice kinetic Monte Carlo simulations using parallel processing and rate constant rescaling

NASA Astrophysics Data System (ADS)

Núñez, M.; Robie, T.; Vlachos, D. G.

2017-10-01

Kinetic Monte Carlo (KMC) simulation provides insights into catalytic reactions unobtainable with either experiments or mean-field microkinetic models. Sensitivity analysis of KMC models assesses the robustness of the predictions to parametric perturbations and identifies rate determining steps in a chemical reaction network. Stiffness in the chemical reaction network, a ubiquitous feature, demands lengthy run times for KMC models and renders efficient sensitivity analysis based on the likelihood ratio method unusable. We address the challenge of efficiently conducting KMC simulations and performing accurate sensitivity analysis in systems with unknown time scales by employing two acceleration techniques: rate constant rescaling and parallel processing. We develop statistical criteria that ensure sufficient sampling of non-equilibrium steady state conditions. Our approach provides the twofold benefit of accelerating the simulation itself and enabling likelihood ratio sensitivity analysis, which provides further speedup relative to finite difference sensitivity analysis. As a result, the likelihood ratio method can be applied to real chemistry. We apply our methodology to the water-gas shift reaction on Pt(111).

HRV analysis in local anesthesia using Continuous Wavelet Transform (CWT).

PubMed

Shafqat, K; Pal, S K; Kumari, S; Kyriacou, P A

2011-01-01

Spectral analysis of Heart Rate Variability (HRV) is used for the assessment of cardiovascular autonomic control. In this study Continuous Wavelet Transform (CWT) has been used to evaluate the effect of local anesthesia on HRV parameters in a group of fourteen patients undergoing axillary brachial plexus block. A new method which takes signal characteristics into account has been presented for the estimation of the variable boundaries associated with the low and the high frequency band of the HRV signal. The variable boundary method might be useful in cases when the power related to respiration component extends beyond the traditionally excepted range of the high frequency band (0.15-0.4 Hz). The statistical analysis (non-parametric Wilcoxon signed rank test) showed that the LF/HF ratio decreased within an hour of the application of the brachial plexus block compared to the values fifteen minutes prior to the application of the block. These changes were observed in thirteen of the fourteen patients included in this study.

Describing Function Techniques for the Non-Linear Analysis of the Dynamics of a Rail Vehicle Wheelset

DOT National Transportation Integrated Search

1975-07-01

The describing function method of analysis is applied to investigate the influence of parametric variations on wheelset critical velocity. In addition, the relationship between the amplitude of sustained lateral oscillations and critical speed is der...

Formation of parametric images using mixed-effects models: a feasibility study.

PubMed

Huang, Husan-Ming; Shih, Yi-Yu; Lin, Chieh

2016-03-01

Mixed-effects models have been widely used in the analysis of longitudinal data. By presenting the parameters as a combination of fixed effects and random effects, mixed-effects models incorporating both within- and between-subject variations are capable of improving parameter estimation. In this work, we demonstrate the feasibility of using a non-linear mixed-effects (NLME) approach for generating parametric images from medical imaging data of a single study. By assuming that all voxels in the image are independent, we used simulation and animal data to evaluate whether NLME can improve the voxel-wise parameter estimation. For testing purposes, intravoxel incoherent motion (IVIM) diffusion parameters including perfusion fraction, pseudo-diffusion coefficient and true diffusion coefficient were estimated using diffusion-weighted MR images and NLME through fitting the IVIM model. The conventional method of non-linear least squares (NLLS) was used as the standard approach for comparison of the resulted parametric images. In the simulated data, NLME provides more accurate and precise estimates of diffusion parameters compared with NLLS. Similarly, we found that NLME has the ability to improve the signal-to-noise ratio of parametric images obtained from rat brain data. These data have shown that it is feasible to apply NLME in parametric image generation, and the parametric image quality can be accordingly improved with the use of NLME. With the flexibility to be adapted to other models or modalities, NLME may become a useful tool to improve the parametric image quality in the future. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

Covariate analysis of bivariate survival data

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

Bennett, L.E.

1992-01-01

The methods developed are used to analyze the effects of covariates on bivariate survival data when censoring and ties are present. The proposed method provides models for bivariate survival data that include differential covariate effects and censored observations. The proposed models are based on an extension of the univariate Buckley-James estimators which replace censored data points by their expected values, conditional on the censoring time and the covariates. For the bivariate situation, it is necessary to determine the expectation of the failure times for one component conditional on the failure or censoring time of the other component. Two different methodsmore » have been developed to estimate these expectations. In the semiparametric approach these expectations are determined from a modification of Burke's estimate of the bivariate empirical survival function. In the parametric approach censored data points are also replaced by their conditional expected values where the expected values are determined from a specified parametric distribution. The model estimation will be based on the revised data set, comprised of uncensored components and expected values for the censored components. The variance-covariance matrix for the estimated covariate parameters has also been derived for both the semiparametric and parametric methods. Data from the Demographic and Health Survey was analyzed by these methods. The two outcome variables are post-partum amenorrhea and breastfeeding; education and parity were used as the covariates. Both the covariate parameter estimates and the variance-covariance estimates for the semiparametric and parametric models will be compared. In addition, a multivariate test statistic was used in the semiparametric model to examine contrasts. The significance of the statistic was determined from a bootstrap distribution of the test statistic.« less

Markovian Dynamics of Josephson Parametric Amplification

NASA Astrophysics Data System (ADS)

Kaiser, Waldemar; Haider, Michael; Russer, Johannes A.; Russer, Peter; Jirauschek, Christian

2017-09-01

In this work, we derive the dynamics of the lossy DC pumped non-degenerate Josephson parametric amplifier (DCPJPA). The main element in a DCPJPA is the superconducting Josephson junction. The DC bias generates the AC Josephson current varying the nonlinear inductance of the junction. By this way the Josephson junction acts as the pump oscillator as well as the time varying reactance of the parametric amplifier. In quantum-limited amplification, losses and noise have an increased impact on the characteristics of an amplifier. We outline the classical model of the lossy DCPJPA and derive the available noise power spectral densities. A classical treatment is not capable of including properties like spontaneous emission which is mandatory in case of amplification at the quantum limit. Thus, we derive a quantum mechanical model of the lossy DCPJPA. Thermal losses are modeled by the quantum Langevin approach, by coupling the quantized system to a photon heat bath in thermodynamic equilibrium. The mode occupation in the bath follows the Bose-Einstein statistics. Based on the second quantization formalism, we derive the Heisenberg equations of motion of both resonator modes. We assume the dynamics of the system to follow the Markovian approximation, i.e. the system only depends on its actual state and is memory-free. We explicitly compute the time evolution of the contributions to the signal mode energy and give numeric examples based on different damping and coupling constants. Our analytic results show, that this model is capable of including thermal noise into the description of the DC pumped non-degenerate Josephson parametric amplifier.

Computational Analysis for Rocket-Based Combined-Cycle Systems During Rocket-Only Operation

NASA Technical Reports Server (NTRS)

Steffen, C. J., Jr.; Smith, T. D.; Yungster, S.; Keller, D. J.

2000-01-01

A series of Reynolds-averaged Navier-Stokes calculations were employed to study the performance of rocket-based combined-cycle systems operating in an all-rocket mode. This parametric series of calculations were executed within a statistical framework, commonly known as design of experiments. The parametric design space included four geometric and two flowfield variables set at three levels each, for a total of 729 possible combinations. A D-optimal design strategy was selected. It required that only 36 separate computational fluid dynamics (CFD) solutions be performed to develop a full response surface model, which quantified the linear, bilinear, and curvilinear effects of the six experimental variables. The axisymmetric, Reynolds-averaged Navier-Stokes simulations were executed with the NPARC v3.0 code. The response used in the statistical analysis was created from Isp efficiency data integrated from the 36 CFD simulations. The influence of turbulence modeling was analyzed by using both one- and two-equation models. Careful attention was also given to quantify the influence of mesh dependence, iterative convergence, and artificial viscosity upon the resulting statistical model. Thirteen statistically significant effects were observed to have an influence on rocket-based combined-cycle nozzle performance. It was apparent that the free-expansion process, directly downstream of the rocket nozzle, can influence the Isp efficiency. Numerical schlieren images and particle traces have been used to further understand the physical phenomena behind several of the statistically significant results.

Enhanced detection and visualization of anomalies in spectral imagery

NASA Astrophysics Data System (ADS)

Basener, William F.; Messinger, David W.

2009-05-01

Anomaly detection algorithms applied to hyperspectral imagery are able to reliably identify man-made objects from a natural environment based on statistical/geometric likelyhood. The process is more robust than target identification, which requires precise prior knowledge of the object of interest, but has an inherently higher false alarm rate. Standard anomaly detection algorithms measure deviation of pixel spectra from a parametric model (either statistical or linear mixing) estimating the image background. The topological anomaly detector (TAD) creates a fully non-parametric, graph theory-based, topological model of the image background and measures deviation from this background using codensity. In this paper we present a large-scale comparative test of TAD against 80+ targets in four full HYDICE images using the entire canonical target set for generation of ROC curves. TAD will be compared against several statistics-based detectors including local RX and subspace RX. Even a perfect anomaly detection algorithm would have a high practical false alarm rate in most scenes simply because the user/analyst is not interested in every anomalous object. To assist the analyst in identifying and sorting objects of interest, we investigate coloring of the anomalies with principle components projections using statistics computed from the anomalies. This gives a very useful colorization of anomalies in which objects of similar material tend to have the same color, enabling an analyst to quickly sort and identify anomalies of highest interest.

Robust non-parametric one-sample tests for the analysis of recurrent events.

PubMed

Rebora, Paola; Galimberti, Stefania; Valsecchi, Maria Grazia

2010-12-30

One-sample non-parametric tests are proposed here for inference on recurring events. The focus is on the marginal mean function of events and the basis for inference is the standardized distance between the observed and the expected number of events under a specified reference rate. Different weights are considered in order to account for various types of alternative hypotheses on the mean function of the recurrent events process. A robust version and a stratified version of the test are also proposed. The performance of these tests was investigated through simulation studies under various underlying event generation processes, such as homogeneous and nonhomogeneous Poisson processes, autoregressive and renewal processes, with and without frailty effects. The robust versions of the test have been shown to be suitable in a wide variety of event generating processes. The motivating context is a study on gene therapy in a very rare immunodeficiency in children, where a major end-point is the recurrence of severe infections. Robust non-parametric one-sample tests for recurrent events can be useful to assess efficacy and especially safety in non-randomized studies or in epidemiological studies for comparison with a standard population. Copyright © 2010 John Wiley & Sons, Ltd.

Trend analysis of annual precipitation of Mauritius for the period 1981-2010

NASA Astrophysics Data System (ADS)

Raja, Nussaïbah B.; Aydin, Olgu

2018-04-01

This study researched the precipitation variability across 53 meteorological stations in Mauritius and different subregions of the island, over a 30-year study period (1981-2010). Time series was investigated for each 5-year interval and also for the whole study period. Non-parametric Mann-Kendall and Spearman's rho statistical tests were used to detect trends in annual precipitation. A mix of positive (increasing) and negative (decreasing) trends was highlighted for the 5-year interval analysis. The statistical tests nevertheless agreed on the overall trend for Mauritius and the subregions. Most regions showed a decrease in precipitation during the period 1996-2000. This is attributed to the 1998-2000 drought period which was brought about by a moderate La Niña event. In general, an increase in precipitation levels was observed across the country during the study period. This increase is the result of an increase in extreme precipitation events in the region. On the other hand, two subregions, both located in the highlands, experienced a decline in precipitation levels. Since most of the reservoirs in Mauritius are located in these two subregions, this implies serious consequences for water availability in the country if existing storage capacities are kept.

Perceptual reversals during binocular rivalry: ERP components and their concomitant source differences.

PubMed

Britz, Juliane; Pitts, Michael A

2011-11-01

We used an intermittent stimulus presentation to investigate event-related potential (ERP) components associated with perceptual reversals during binocular rivalry. The combination of spatiotemporal ERP analysis with source imaging and statistical parametric mapping of the concomitant source differences yielded differences in three time windows: reversals showed increased activity in early visual (∼120 ms) and in inferior frontal and anterior temporal areas (∼400-600 ms) and decreased activity in the ventral stream (∼250-350 ms). The combination of source imaging and statistical parametric mapping suggests that these differences were due to differences in generator strength and not generator configuration, unlike the initiation of reversals in right inferior parietal areas. These results are discussed within the context of the extensive network of brain areas that has been implicated in the initiation, implementation, and appraisal of bistable perceptual reversals. Copyright © 2011 Society for Psychophysiological Research.

Linkage analysis of high myopia susceptibility locus in 26 families.

PubMed

Paget, Sandrine; Julia, Sophie; Vitezica, Zulma G; Soler, Vincent; Malecaze, François; Calvas, Patrick

2008-01-01

We conducted a linkage analysis in high myopia families to replicate suggestive results from chromosome 7q36 using a model of autosomal dominant inheritance and genetic heterogeneity. We also performed a genome-wide scan to identify novel loci. Twenty-six families, with at least two high-myopic subjects (ie. refractive value in the less affected eye of -5 diopters) in each family, were included. Phenotypic examination included standard autorefractometry, ultrasonographic eye length measurement, and clinical confirmation of the non-syndromic character of the refractive disorder. Nine families were collected de novo including 136 available members of whom 34 were highly myopic subjects. Twenty new subjects were added in 5 of the 17 remaining families. A total of 233 subjects were submitted to a genome scan using ABI linkage mapping set LMSv2-MD-10, additional markers in all regions where preliminary LOD scores were greater than 1.5 were used. Multipoint parametric and non-parametric analyses were conducted with the software packages Genehunter 2.0 and Merlin 1.0.1. Two autosomal recessive, two autosomal dominant, and four autosomal additive models were used in the parametric linkage analyses. No linkage was found using the subset of nine newly collected families. Study of the entire population of 26 families with a parametric model did not yield a significant LOD score (>3), even for the previously suggestive locus on 7q36. A non-parametric model demonstrated significant linkage to chromosome 7p15 in the entire population (Z-NPL=4.07, p=0.00002). The interval is 7.81 centiMorgans (cM) between markers D7S2458 and D7S2515. The significant interval reported here needs confirmation in other cohorts. Among possible susceptibility genes in the interval, certain candidates are likely to be involved in eye growth and development.

A general framework for parametric survival analysis.

PubMed

Crowther, Michael J; Lambert, Paul C

2014-12-30

Parametric survival models are being increasingly used as an alternative to the Cox model in biomedical research. Through direct modelling of the baseline hazard function, we can gain greater understanding of the risk profile of patients over time, obtaining absolute measures of risk. Commonly used parametric survival models, such as the Weibull, make restrictive assumptions of the baseline hazard function, such as monotonicity, which is often violated in clinical datasets. In this article, we extend the general framework of parametric survival models proposed by Crowther and Lambert (Journal of Statistical Software 53:12, 2013), to incorporate relative survival, and robust and cluster robust standard errors. We describe the general framework through three applications to clinical datasets, in particular, illustrating the use of restricted cubic splines, modelled on the log hazard scale, to provide a highly flexible survival modelling framework. Through the use of restricted cubic splines, we can derive the cumulative hazard function analytically beyond the boundary knots, resulting in a combined analytic/numerical approach, which substantially improves the estimation process compared with only using numerical integration. User-friendly Stata software is provided, which significantly extends parametric survival models available in standard software. Copyright © 2014 John Wiley & Sons, Ltd.

Parametric-Studies and Data-Plotting Modules for the SOAP

NASA Technical Reports Server (NTRS)

2008-01-01

"Parametric Studies" and "Data Table Plot View" are the names of software modules in the Satellite Orbit Analysis Program (SOAP). Parametric Studies enables parameterization of as many as three satellite or ground-station attributes across a range of values and computes the average, minimum, and maximum of a specified metric, the revisit time, or 21 other functions at each point in the parameter space. This computation produces a one-, two-, or three-dimensional table of data representing statistical results across the parameter space. Inasmuch as the output of a parametric study in three dimensions can be a very large data set, visualization is a paramount means of discovering trends in the data (see figure). Data Table Plot View enables visualization of the data table created by Parametric Studies or by another data source: this module quickly generates a display of the data in the form of a rotatable three-dimensional-appearing plot, making it unnecessary to load the SOAP output data into a separate plotting program. The rotatable three-dimensionalappearing plot makes it easy to determine which points in the parameter space are most desirable. Both modules provide intuitive user interfaces for ease of use.

An Exploratory Data Analysis System for Support in Medical Decision-Making

PubMed Central

Copeland, J. A.; Hamel, B.; Bourne, J. R.

1979-01-01

An experimental system was developed to allow retrieval and analysis of data collected during a study of neurobehavioral correlates of renal disease. After retrieving data organized in a relational data base, simple bivariate statistics of parametric and nonparametric nature could be conducted. An “exploratory” mode in which the system provided guidance in selection of appropriate statistical analyses was also available to the user. The system traversed a decision tree using the inherent qualities of the data (e.g., the identity and number of patients, tests, and time epochs) to search for the appropriate analyses to employ.

Variational Bayesian Parameter Estimation Techniques for the General Linear Model

PubMed Central

Starke, Ludger; Ostwald, Dirk

2017-01-01

Variational Bayes (VB), variational maximum likelihood (VML), restricted maximum likelihood (ReML), and maximum likelihood (ML) are cornerstone parametric statistical estimation techniques in the analysis of functional neuroimaging data. However, the theoretical underpinnings of these model parameter estimation techniques are rarely covered in introductory statistical texts. Because of the widespread practical use of VB, VML, ReML, and ML in the neuroimaging community, we reasoned that a theoretical treatment of their relationships and their application in a basic modeling scenario may be helpful for both neuroimaging novices and practitioners alike. In this technical study, we thus revisit the conceptual and formal underpinnings of VB, VML, ReML, and ML and provide a detailed account of their mathematical relationships and implementational details. We further apply VB, VML, ReML, and ML to the general linear model (GLM) with non-spherical error covariance as commonly encountered in the first-level analysis of fMRI data. To this end, we explicitly derive the corresponding free energy objective functions and ensuing iterative algorithms. Finally, in the applied part of our study, we evaluate the parameter and model recovery properties of VB, VML, ReML, and ML, first in an exemplary setting and then in the analysis of experimental fMRI data acquired from a single participant under visual stimulation. PMID:28966572

Applying Statistical Models and Parametric Distance Measures for Music Similarity Search

NASA Astrophysics Data System (ADS)

Lukashevich, Hanna; Dittmar, Christian; Bastuck, Christoph

Automatic deriving of similarity relations between music pieces is an inherent field of music information retrieval research. Due to the nearly unrestricted amount of musical data, the real-world similarity search algorithms have to be highly efficient and scalable. The possible solution is to represent each music excerpt with a statistical model (ex. Gaussian mixture model) and thus to reduce the computational costs by applying the parametric distance measures between the models. In this paper we discuss the combinations of applying different parametric modelling techniques and distance measures and weigh the benefits of each one against the others.

Statistical modelling of networked human-automation performance using working memory capacity.

PubMed

Ahmed, Nisar; de Visser, Ewart; Shaw, Tyler; Mohamed-Ameen, Amira; Campbell, Mark; Parasuraman, Raja

2014-01-01

This study examines the challenging problem of modelling the interaction between individual attentional limitations and decision-making performance in networked human-automation system tasks. Analysis of real experimental data from a task involving networked supervision of multiple unmanned aerial vehicles by human participants shows that both task load and network message quality affect performance, but that these effects are modulated by individual differences in working memory (WM) capacity. These insights were used to assess three statistical approaches for modelling and making predictions with real experimental networked supervisory performance data: classical linear regression, non-parametric Gaussian processes and probabilistic Bayesian networks. It is shown that each of these approaches can help designers of networked human-automated systems cope with various uncertainties in order to accommodate future users by linking expected operating conditions and performance from real experimental data to observable cognitive traits like WM capacity. Practitioner Summary: Working memory (WM) capacity helps account for inter-individual variability in operator performance in networked unmanned aerial vehicle supervisory tasks. This is useful for reliable performance prediction near experimental conditions via linear models; robust statistical prediction beyond experimental conditions via Gaussian process models and probabilistic inference about unknown task conditions/WM capacities via Bayesian network models.

Brain serotonin transporter density and aggression in abstinent methamphetamine abusers.

PubMed

Sekine, Yoshimoto; Ouchi, Yasuomi; Takei, Nori; Yoshikawa, Etsuji; Nakamura, Kazuhiko; Futatsubashi, Masami; Okada, Hiroyuki; Minabe, Yoshio; Suzuki, Katsuaki; Iwata, Yasuhide; Tsuchiya, Kenji J; Tsukada, Hideo; Iyo, Masaomi; Mori, Norio

2006-01-01

In animals, methamphetamine is known to have a neurotoxic effect on serotonin neurons, which have been implicated in the regulation of mood, anxiety, and aggression. It remains unknown whether methamphetamine damages serotonin neurons in humans. To investigate the status of brain serotonin neurons and their possible relationship with clinical characteristics in currently abstinent methamphetamine abusers. Case-control analysis. A hospital research center. Twelve currently abstinent former methamphetamine abusers (5 women and 7 men) and 12 age-, sex-, and education-matched control subjects recruited from the community. The brain regional density of the serotonin transporter, a structural component of serotonin neurons, was estimated using positron emission tomography and trans-1,2,3,5,6,10-beta-hexahydro-6-[4-(methylthio)phenyl]pyrrolo-[2,1-a]isoquinoline ([(11)C](+)McN-5652). Estimates were derived from region-of-interest and statistical parametric mapping methods, followed by within-case analysis using the measures of clinical variables. The duration of methamphetamine use, the magnitude of aggression and depressive symptoms, and changes in serotonin transporter density represented by the [(11)C](+)McN-5652 distribution volume. Methamphetamine abusers showed increased levels of aggression compared with controls. Region-of-interest and statistical parametric mapping analyses revealed that the serotonin transporter density in global brain regions (eg, the midbrain, thalamus, caudate, putamen, cerebral cortex, and cerebellum) was significantly lower in methamphetamine abusers than in control subjects, and this reduction was significantly inversely correlated with the duration of methamphetamine use. Furthermore, statistical parametric mapping analyses indicated that the density in the orbitofrontal, temporal, and anterior cingulate areas was closely associated with the magnitude of aggression in methamphetamine abusers. Protracted abuse of methamphetamine may reduce the density of the serotonin transporter in the brain, leading to elevated aggression, even in currently abstinent abusers.

The Absolute Stability Analysis in Fuzzy Control Systems with Parametric Uncertainties and Reference Inputs

NASA Astrophysics Data System (ADS)

Wu, Bing-Fei; Ma, Li-Shan; Perng, Jau-Woei

This study analyzes the absolute stability in P and PD type fuzzy logic control systems with both certain and uncertain linear plants. Stability analysis includes the reference input, actuator gain and interval plant parameters. For certain linear plants, the stability (i.e. the stable equilibriums of error) in P and PD types is analyzed with the Popov or linearization methods under various reference inputs and actuator gains. The steady state errors of fuzzy control systems are also addressed in the parameter plane. The parametric robust Popov criterion for parametric absolute stability based on Lur'e systems is also applied to the stability analysis of P type fuzzy control systems with uncertain plants. The PD type fuzzy logic controller in our approach is a single-input fuzzy logic controller and is transformed into the P type for analysis. In our work, the absolute stability analysis of fuzzy control systems is given with respect to a non-zero reference input and an uncertain linear plant with the parametric robust Popov criterion unlike previous works. Moreover, a fuzzy current controlled RC circuit is designed with PSPICE models. Both numerical and PSPICE simulations are provided to verify the analytical results. Furthermore, the oscillation mechanism in fuzzy control systems is specified with various equilibrium points of view in the simulation example. Finally, the comparisons are also given to show the effectiveness of the analysis method.

Non-linear wave interaction in a magnetoplasma column. I - Theory. II Experiment

NASA Technical Reports Server (NTRS)

Larsen, J.-M.; Crawford, F. W.

1979-01-01

The paper presents an analysis of non-linear three-wave interaction for propagation along a cylindrical plasma column surrounded either by a metallic boundary, or by an infinite dielectric, and immersed in an infinite, static, axial magnetic field. An averaged Lagrangian method is used and the results are specialized to parametric amplification and mode conversion, assuming an undepleted pump wave. Computations are presented for a magneto-plasma column surrounded by free space, indicating that parametric growth rates of the order of a fraction of a decibel per centimeter should be obtainable for plausible laboratory plasma parameters. In addition, experiments on non-linear mode conversion in a cylindrical magnetoplasma column are described. The results are compared with the theoretical predictions and good qualitative agreement is demonstrated.

The challenge for genetic epidemiologists: how to analyze large numbers of SNPs in relation to complex diseases.

PubMed

Heidema, A Geert; Boer, Jolanda M A; Nagelkerke, Nico; Mariman, Edwin C M; van der A, Daphne L; Feskens, Edith J M

2006-04-21

Genetic epidemiologists have taken the challenge to identify genetic polymorphisms involved in the development of diseases. Many have collected data on large numbers of genetic markers but are not familiar with available methods to assess their association with complex diseases. Statistical methods have been developed for analyzing the relation between large numbers of genetic and environmental predictors to disease or disease-related variables in genetic association studies. In this commentary we discuss logistic regression analysis, neural networks, including the parameter decreasing method (PDM) and genetic programming optimized neural networks (GPNN) and several non-parametric methods, which include the set association approach, combinatorial partitioning method (CPM), restricted partitioning method (RPM), multifactor dimensionality reduction (MDR) method and the random forests approach. The relative strengths and weaknesses of these methods are highlighted. Logistic regression and neural networks can handle only a limited number of predictor variables, depending on the number of observations in the dataset. Therefore, they are less useful than the non-parametric methods to approach association studies with large numbers of predictor variables. GPNN on the other hand may be a useful approach to select and model important predictors, but its performance to select the important effects in the presence of large numbers of predictors needs to be examined. Both the set association approach and random forests approach are able to handle a large number of predictors and are useful in reducing these predictors to a subset of predictors with an important contribution to disease. The combinatorial methods give more insight in combination patterns for sets of genetic and/or environmental predictor variables that may be related to the outcome variable. As the non-parametric methods have different strengths and weaknesses we conclude that to approach genetic association studies using the case-control design, the application of a combination of several methods, including the set association approach, MDR and the random forests approach, will likely be a useful strategy to find the important genes and interaction patterns involved in complex diseases.

Global scaling for semi-quantitative analysis in FP-CIT SPECT.

PubMed

Kupitz, D; Apostolova, I; Lange, C; Ulrich, G; Amthauer, H; Brenner, W; Buchert, R

2014-01-01

Semi-quantitative characterization of dopamine transporter availability from single photon emission computed tomography (SPECT) with 123I-ioflupane (FP-CIT) is based on uptake ratios relative to a reference region. The aim of this study was to evaluate the whole brain as reference region for semi-quantitative analysis of FP-CIT SPECT. The rationale was that this might reduce statistical noise associated with the estimation of non-displaceable FP-CIT uptake. 150 FP-CIT SPECTs were categorized as neurodegenerative or non-neurodegenerative by an expert. Semi-quantitative analysis of specific binding ratios (SBR) was performed with a custom-made tool based on the Statistical Parametric Mapping software package using predefined regions of interest (ROIs) in the anatomical space of the Montreal Neurological Institute. The following reference regions were compared: predefined ROIs for frontal and occipital lobe and whole brain (without striata, thalamus and brainstem). Tracer uptake in the reference region was characterized by the mean, median or 75th percentile of its voxel intensities. The area (AUC) under the receiver operating characteristic curve was used as performance measure. The highest AUC of 0.973 was achieved by the SBR of the putamen with the 75th percentile in the whole brain as reference. The lowest AUC for the putamen SBR of 0.937 was obtained with the mean in the frontal lobe as reference. We recommend the 75th percentile in the whole brain as reference for semi-quantitative analysis in FP-CIT SPECT. This combination provided the best agreement of the semi-quantitative analysis with visual evaluation of the SPECT images by an expert and, therefore, is appropriate to support less experienced physicians.

Exposure-response analysis of alectinib in crizotinib-resistant ALK-positive non-small cell lung cancer.

PubMed

Morcos, Peter N; Nueesch, Eveline; Jaminion, Felix; Guerini, Elena; Hsu, Joy C; Bordogna, Walter; Balas, Bogdana; Mercier, Francois

2018-05-10

Alectinib is a selective and potent anaplastic lymphoma kinase (ALK) inhibitor that is active in the central nervous system (CNS). Alectinib demonstrated robust efficacy in a pooled analysis of two single-arm, open-label phase II studies (NP28673, NCT01801111; NP28761, NCT01871805) in crizotinib-resistant ALK-positive non-small-cell lung cancer (NSCLC): median overall survival (OS) 29.1 months (95% confidence interval [CI]: 21.3-39.0) for alectinib 600 mg twice daily (BID). We investigated exposure-response relationships from final pooled phase II OS and safety data to assess alectinib dose selection. A semi-parametric Cox proportional hazards model analyzed relationships between individual median observed steady-state trough concentrations (C trough,ss ) for combined exposure of alectinib and its major metabolite (M4), baseline covariates (demographics and disease characteristics) and OS. Univariate logistic regression analysis analyzed relationships between C trough,ss and incidence of adverse events (AEs: serious and Grade ≥ 3). Overall, 92% of patients (n = 207/225) had C trough,ss data and were included in the analysis. No statistically significant relationship was found between C trough,ss and OS following alectinib treatment. The only baseline covariates that statistically influenced OS were baseline tumor size and prior crizotinib treatment duration. Larger baseline tumor size and shorter prior crizotinib treatment were both associated with shorter OS. Logistic regression confirmed no significant relationship between C trough,ss and AEs. Alectinib 600 mg BID provides systemic exposures at plateau of response for OS while maintaining a well-tolerated safety profile. This analysis confirms alectinib 600 mg BID as the recommended global dose for patients with crizotinib-resistant ALK-positive NSCLC.

Increased sensorimotor network activity in DYT1 dystonia: a functional imaging study

PubMed Central

Argyelan, Miklos; Habeck, Christian; Ghilardi, M. Felice; Fitzpatrick, Toni; Dhawan, Vijay; Pourfar, Michael; Bressman, Susan B.; Eidelberg, David

2010-01-01

Neurophysiological studies have provided evidence of primary motor cortex hyperexcitability in primary dystonia, but several functional imaging studies suggest otherwise. To address this issue, we measured sensorimotor activation at both the regional and network levels in carriers of the DYT1 dystonia mutation and in control subjects. We used 15Oxygen-labelled water and positron emission tomography to scan nine manifesting DYT1 carriers, 10 non-manifesting DYT1 carriers and 12 age-matched controls while they performed a kinematically controlled motor task; they were also scanned in a non-motor audio-visual control condition. Within- and between-group contrasts were analysed with statistical parametric mapping. For network analysis, we first identified a normal motor-related activation pattern in a set of 39 motor and audio-visual scans acquired in an independent cohort of 18 healthy volunteer subjects. The expression of this pattern was prospectively quantified in the motor and control scans acquired in each of the gene carriers and controls. Network values for the three groups were compared with ANOVA and post hoc contrasts. Voxel-wise comparison of DYT1 carriers and controls revealed abnormally increased motor activation responses in the former group (P < 0.05, corrected; statistical parametric mapping), localized to the sensorimotor cortex, dorsal premotor cortex, supplementary motor area and the inferior parietal cortex. Network analysis of the normative derivation cohort revealed a significant normal motor-related activation pattern topography (P < 0.0001) characterized by covarying neural activity in the sensorimotor cortex, dorsal premotor cortex, supplementary motor area and cerebellum. In the study cohort, normal motor-related activation pattern expression measured during movement was abnormally elevated in the manifesting gene carriers (P < 0.001) but not in their non-manifesting counterparts. In contrast, in the non-motor control condition, abnormal increases in network activity were present in both groups of gene carriers (P < 0.001). In this condition, normal motor-related activation pattern expression in non-manifesting carriers was greater than in controls, but lower than in affected carriers. In the latter group, measures of normal motor-related activation pattern expression in the audio-visual condition correlated with independent dystonia clinical ratings (r = 0.70, P = 0.04). These findings confirm that overexcitability of the sensorimotor system is a robust feature of dystonia. The presence of elevated normal motor-related activation pattern expression in the non-motor condition suggests that abnormal integration of audio-visual input with sensorimotor network activity is an important trait feature of this disorder. Lastly, quantification of normal motor-related activation pattern expression in individual cases may have utility as an objective descriptor of therapeutic response in trials of new treatments for dystonia and related disorders. PMID:20207699

Preliminary Multi-Variable Parametric Cost Model for Space Telescopes

NASA Technical Reports Server (NTRS)

Stahl, H. Philip; Hendrichs, Todd

2010-01-01

This slide presentation reviews creating a preliminary multi-variable cost model for the contract costs of making a space telescope. There is discussion of the methodology for collecting the data, definition of the statistical analysis methodology, single variable model results, testing of historical models and an introduction of the multi variable models.

The effect of pumping noise on the characteristics of a single-stage parametric amplifier

NASA Astrophysics Data System (ADS)

Medvedev, S. Iu.; Muzychuk, O. V.

1983-10-01

An analysis is made of the operation of a single-stage parametric amplifier based on a varactor with a sharp transition. Analytical expressions are obtained for the statistical moments of the output signal, the signal-noise ratio, and other characteristics in the case when the output signal and the pump are a mixture of harmonic oscillation and Gaussian noise. It is shown that, when a noise component is present in the pump, an increase of its harmonic component to values close to the threshold leads to a sharp decrease in the signal-noise ratio at the amplifier output.

Parametric Cost Models for Space Telescopes

NASA Technical Reports Server (NTRS)

Stahl, H. Philip

2010-01-01

A study is in-process to develop a multivariable parametric cost model for space telescopes. Cost and engineering parametric data has been collected on 30 different space telescopes. Statistical correlations have been developed between 19 variables of 59 variables sampled. Single Variable and Multi-Variable Cost Estimating Relationships have been developed. Results are being published.

Detecting correlation changes in multivariate time series: A comparison of four non-parametric change point detection methods.

PubMed

Cabrieto, Jedelyn; Tuerlinckx, Francis; Kuppens, Peter; Grassmann, Mariel; Ceulemans, Eva

2017-06-01

Change point detection in multivariate time series is a complex task since next to the mean, the correlation structure of the monitored variables may also alter when change occurs. DeCon was recently developed to detect such changes in mean and\\or correlation by combining a moving windows approach and robust PCA. However, in the literature, several other methods have been proposed that employ other non-parametric tools: E-divisive, Multirank, and KCP. Since these methods use different statistical approaches, two issues need to be tackled. First, applied researchers may find it hard to appraise the differences between the methods. Second, a direct comparison of the relative performance of all these methods for capturing change points signaling correlation changes is still lacking. Therefore, we present the basic principles behind DeCon, E-divisive, Multirank, and KCP and the corresponding algorithms, to make them more accessible to readers. We further compared their performance through extensive simulations using the settings of Bulteel et al. (Biological Psychology, 98 (1), 29-42, 2014) implying changes in mean and in correlation structure and those of Matteson and James (Journal of the American Statistical Association, 109 (505), 334-345, 2014) implying different numbers of (noise) variables. KCP emerged as the best method in almost all settings. However, in case of more than two noise variables, only DeCon performed adequately in detecting correlation changes.

Integrative genetic risk prediction using non-parametric empirical Bayes classification.

PubMed

Zhao, Sihai Dave

2017-06-01

Genetic risk prediction is an important component of individualized medicine, but prediction accuracies remain low for many complex diseases. A fundamental limitation is the sample sizes of the studies on which the prediction algorithms are trained. One way to increase the effective sample size is to integrate information from previously existing studies. However, it can be difficult to find existing data that examine the target disease of interest, especially if that disease is rare or poorly studied. Furthermore, individual-level genotype data from these auxiliary studies are typically difficult to obtain. This article proposes a new approach to integrative genetic risk prediction of complex diseases with binary phenotypes. It accommodates possible heterogeneity in the genetic etiologies of the target and auxiliary diseases using a tuning parameter-free non-parametric empirical Bayes procedure, and can be trained using only auxiliary summary statistics. Simulation studies show that the proposed method can provide superior predictive accuracy relative to non-integrative as well as integrative classifiers. The method is applied to a recent study of pediatric autoimmune diseases, where it substantially reduces prediction error for certain target/auxiliary disease combinations. The proposed method is implemented in the R package ssa. © 2016, The International Biometric Society.

Optimizing the static-dynamic performance of the body-in-white using a modified non-dominated sorting genetic algorithm coupled with grey relational analysis

NASA Astrophysics Data System (ADS)

Wang, Dengfeng; Cai, Kefang

2018-04-01

This article presents a hybrid method combining a modified non-dominated sorting genetic algorithm (MNSGA-II) with grey relational analysis (GRA) to improve the static-dynamic performance of a body-in-white (BIW). First, an implicit parametric model of the BIW was built using SFE-CONCEPT software, and then the validity of the implicit parametric model was verified by physical testing. Eight shape design variables were defined for BIW beam structures based on the implicit parametric technology. Subsequently, MNSGA-II was used to determine the optimal combination of the design parameters that can improve the bending stiffness, torsion stiffness and low-order natural frequencies of the BIW without considerable increase in the mass. A set of non-dominated solutions was then obtained in the multi-objective optimization design. Finally, the grey entropy theory and GRA were applied to rank all non-dominated solutions from best to worst to determine the best trade-off solution. The comparison between the GRA and the technique for order of preference by similarity to ideal solution (TOPSIS) illustrated the reliability and rationality of GRA. Moreover, the effectiveness of the hybrid method was verified by the optimal results such that the bending stiffness, torsion stiffness, first order bending and first order torsion natural frequency were improved by 5.46%, 9.30%, 7.32% and 5.73%, respectively, with the mass of the BIW increasing by 1.30%.

Multilevel mixed effects parametric survival models using adaptive Gauss-Hermite quadrature with application to recurrent events and individual participant data meta-analysis.

PubMed

Crowther, Michael J; Look, Maxime P; Riley, Richard D

2014-09-28

Multilevel mixed effects survival models are used in the analysis of clustered survival data, such as repeated events, multicenter clinical trials, and individual participant data (IPD) meta-analyses, to investigate heterogeneity in baseline risk and covariate effects. In this paper, we extend parametric frailty models including the exponential, Weibull and Gompertz proportional hazards (PH) models and the log logistic, log normal, and generalized gamma accelerated failure time models to allow any number of normally distributed random effects. Furthermore, we extend the flexible parametric survival model of Royston and Parmar, modeled on the log-cumulative hazard scale using restricted cubic splines, to include random effects while also allowing for non-PH (time-dependent effects). Maximum likelihood is used to estimate the models utilizing adaptive or nonadaptive Gauss-Hermite quadrature. The methods are evaluated through simulation studies representing clinically plausible scenarios of a multicenter trial and IPD meta-analysis, showing good performance of the estimation method. The flexible parametric mixed effects model is illustrated using a dataset of patients with kidney disease and repeated times to infection and an IPD meta-analysis of prognostic factor studies in patients with breast cancer. User-friendly Stata software is provided to implement the methods. Copyright © 2014 John Wiley & Sons, Ltd.

Random forests in non-invasive sensorimotor rhythm brain-computer interfaces: a practical and convenient non-linear classifier.

PubMed

Steyrl, David; Scherer, Reinhold; Faller, Josef; Müller-Putz, Gernot R

2016-02-01

There is general agreement in the brain-computer interface (BCI) community that although non-linear classifiers can provide better results in some cases, linear classifiers are preferable. Particularly, as non-linear classifiers often involve a number of parameters that must be carefully chosen. However, new non-linear classifiers were developed over the last decade. One of them is the random forest (RF) classifier. Although popular in other fields of science, RFs are not common in BCI research. In this work, we address three open questions regarding RFs in sensorimotor rhythm (SMR) BCIs: parametrization, online applicability, and performance compared to regularized linear discriminant analysis (LDA). We found that the performance of RF is constant over a large range of parameter values. We demonstrate - for the first time - that RFs are applicable online in SMR-BCIs. Further, we show in an offline BCI simulation that RFs statistically significantly outperform regularized LDA by about 3%. These results confirm that RFs are practical and convenient non-linear classifiers for SMR-BCIs. Taking into account further properties of RFs, such as independence from feature distributions, maximum margin behavior, multiclass and advanced data mining capabilities, we argue that RFs should be taken into consideration for future BCIs.

Optimized statistical parametric mapping procedure for NIRS data contaminated by motion artifacts : Neurometric analysis of body schema extension.

PubMed

Suzuki, Satoshi

2017-09-01

This study investigated the spatial distribution of brain activity on body schema (BS) modification induced by natural body motion using two versions of a hand-tracing task. In Task 1, participants traced Japanese Hiragana characters using the right forefinger, requiring no BS expansion. In Task 2, participants performed the tracing task with a long stick, requiring BS expansion. Spatial distribution was analyzed using general linear model (GLM)-based statistical parametric mapping of near-infrared spectroscopy data contaminated with motion artifacts caused by the hand-tracing task. Three methods were utilized in series to counter the artifacts, and optimal conditions and modifications were investigated: a model-free method (Step 1), a convolution matrix method (Step 2), and a boxcar-function-based Gaussian convolution method (Step 3). The results revealed four methodological findings: (1) Deoxyhemoglobin was suitable for the GLM because both Akaike information criterion and the variance against the averaged hemodynamic response function were smaller than for other signals, (2) a high-pass filter with a cutoff frequency of .014 Hz was effective, (3) the hemodynamic response function computed from a Gaussian kernel function and its first- and second-derivative terms should be included in the GLM model, and (4) correction of non-autocorrelation and use of effective degrees of freedom were critical. Investigating z-maps computed according to these guidelines revealed that contiguous areas of BA7-BA40-BA21 in the right hemisphere became significantly activated ([Formula: see text], [Formula: see text], and [Formula: see text], respectively) during BS modification while performing the hand-tracing task.

Parametric amplification in quasi-PT symmetric coupled waveguide structures

NASA Astrophysics Data System (ADS)

Zhong, Q.; Ahmed, A.; Dadap, J. I.; Osgood, R. M., Jr.; El-Ganainy, R.

2016-12-01

The concept of non-Hermitian parametric amplification was recently proposed as a means to achieve an efficient energy conversion throughout the process of nonlinear three wave mixing in the absence of phase matching. Here we investigate this effect in a waveguide coupler arrangement whose characteristics are tailored to introduce passive PT symmetry only for the idler component. By means of analytical solutions and numerical analysis, we demonstrate the utility of these novel schemes and obtain the optimal design conditions for these devices.

Reliable estimates of predictive uncertainty for an Alpine catchment using a non-parametric methodology

NASA Astrophysics Data System (ADS)

Matos, José P.; Schaefli, Bettina; Schleiss, Anton J.

2017-04-01

Uncertainty affects hydrological modelling efforts from the very measurements (or forecasts) that serve as inputs to the more or less inaccurate predictions that are produced. Uncertainty is truly inescapable in hydrology and yet, due to the theoretical and technical hurdles associated with its quantification, it is at times still neglected or estimated only qualitatively. In recent years the scientific community has made a significant effort towards quantifying this hydrologic prediction uncertainty. Despite this, most of the developed methodologies can be computationally demanding, are complex from a theoretical point of view, require substantial expertise to be employed, and are constrained by a number of assumptions about the model error distribution. These assumptions limit the reliability of many methods in case of errors that show particular cases of non-normality, heteroscedasticity, or autocorrelation. The present contribution builds on a non-parametric data-driven approach that was developed for uncertainty quantification in operational (real-time) forecasting settings. The approach is based on the concept of Pareto optimality and can be used as a standalone forecasting tool or as a postprocessor. By virtue of its non-parametric nature and a general operating principle, it can be applied directly and with ease to predictions of streamflow, water stage, or even accumulated runoff. Also, it is a methodology capable of coping with high heteroscedasticity and seasonal hydrological regimes (e.g. snowmelt and rainfall driven events in the same catchment). Finally, the training and operation of the model are very fast, making it a tool particularly adapted to operational use. To illustrate its practical use, the uncertainty quantification method is coupled with a process-based hydrological model to produce statistically reliable forecasts for an Alpine catchment located in Switzerland. Results are presented and discussed in terms of their reliability and resolution.

HBCU Efficiency and Endowments: An Exploratory Analysis

ERIC Educational Resources Information Center

Coupet, Jason; Barnum, Darold

2010-01-01

Discussions of efficiency among Historically Black Colleges and Universities (HBCUs) are often missing in academic conversations. This article seeks to assess efficiency of individual HBCUs using Data Envelopment Analysis (DEA), a non-parametric technique that can synthesize multiple inputs and outputs to determine a single efficiency score for…

The Impact of Arts Activity on Nursing Staff Well-Being: An Intervention in the Workplace

PubMed Central

Karpavičiūtė, Simona; Macijauskienė, Jūratė

2016-01-01

Over 59 million workers are employed in the healthcare sector globally, with a daily risk of being exposed to a complex variety of health and safety hazards. The purpose of this study was to investigate the impact of arts activity on the well-being of nursing staff. During October–December 2014, 115 nursing staff working in a hospital, took part in this study, which lasted for 10 weeks. The intervention group (n = 56) took part in silk painting activities once a week. Data was collected using socio-demographic questions, the Warwick-Edinburgh Mental Well-Being Scale, Short Form—36 Health Survey questionnaire, Reeder stress scale, and Multidimensional fatigue inventory (before and after art activities in both groups). Statistical data analysis included descriptive statistics (frequency, percentage, mean, standard deviation), non-parametric statistics analysis (Man Whitney U Test; Wilcoxon signed—ranks test), Fisher’s exact test and reliability analysis (Cronbach’s Alpha). The level of significance was set at p ≤ 0.05. In the intervention group, there was a tendency for participation in arts activity having a positive impact on their general health and mental well-being, reducing stress and fatigue, awaking creativity and increasing a sense of community at work. The control group did not show any improvements. Of the intervention group 93% reported enjoyment, with 75% aspiring to continue arts activity in the future. This research suggests that arts activity, as a workplace intervention, can be used to promote nursing staff well-being at work. PMID:27104550

Nonparametric Residue Analysis of Dynamic PET Data With Application to Cerebral FDG Studies in Normals.

PubMed

O'Sullivan, Finbarr; Muzi, Mark; Spence, Alexander M; Mankoff, David M; O'Sullivan, Janet N; Fitzgerald, Niall; Newman, George C; Krohn, Kenneth A

2009-06-01

Kinetic analysis is used to extract metabolic information from dynamic positron emission tomography (PET) uptake data. The theory of indicator dilutions, developed in the seminal work of Meier and Zierler (1954), provides a probabilistic framework for representation of PET tracer uptake data in terms of a convolution between an arterial input function and a tissue residue. The residue is a scaled survival function associated with tracer residence in the tissue. Nonparametric inference for the residue, a deconvolution problem, provides a novel approach to kinetic analysis-critically one that is not reliant on specific compartmental modeling assumptions. A practical computational technique based on regularized cubic B-spline approximation of the residence time distribution is proposed. Nonparametric residue analysis allows formal statistical evaluation of specific parametric models to be considered. This analysis needs to properly account for the increased flexibility of the nonparametric estimator. The methodology is illustrated using data from a series of cerebral studies with PET and fluorodeoxyglucose (FDG) in normal subjects. Comparisons are made between key functionals of the residue, tracer flux, flow, etc., resulting from a parametric (the standard two-compartment of Phelps et al. 1979) and a nonparametric analysis. Strong statistical evidence against the compartment model is found. Primarily these differences relate to the representation of the early temporal structure of the tracer residence-largely a function of the vascular supply network. There are convincing physiological arguments against the representations implied by the compartmental approach but this is the first time that a rigorous statistical confirmation using PET data has been reported. The compartmental analysis produces suspect values for flow but, notably, the impact on the metabolic flux, though statistically significant, is limited to deviations on the order of 3%-4%. The general advantage of the nonparametric residue analysis is the ability to provide a valid kinetic quantitation in the context of studies where there may be heterogeneity or other uncertainty about the accuracy of a compartmental model approximation of the tissue residue.

Sensitivity of the Halstead and Wechsler Test Batteries to brain damage: Evidence from Reitan's original validation sample.

PubMed

Loring, David W; Larrabee, Glenn J

2006-06-01

The Halstead-Reitan Battery has been instrumental in the development of neuropsychological practice in the United States. Although Reitan administered both the Wechsler-Bellevue Intelligence Scale and Halstead's test battery when evaluating Halstead's theory of biologic intelligence, the relative sensitivity of each test battery to brain damage continues to be an area of controversy. Because Reitan did not perform direct parametric analysis to contrast group performances, we reanalyze Reitan's original validation data from both Halstead (Reitan, 1955) and Wechsler batteries (Reitan, 1959a) and calculate effect sizes and probability levels using traditional parametric approaches. Eight of the 10 tests comprising Halstead's original Impairment Index, as well as the Impairment Index itself, statistically differentiated patients with unequivocal brain damage from controls. In addition, 13 of 14 Wechsler measures including Full-Scale IQ also differed statistically between groups (Brain Damage Full-Scale IQ = 96.2; Control Group Full Scale IQ = 112.6). We suggest that differences in the statistical properties of each battery (e.g., raw scores vs. standardized scores) likely contribute to classification characteristics including test sensitivity and specificity.

The composition of M-type asteroids: Synthesis of spectroscopic and radar observations

NASA Astrophysics Data System (ADS)

Neeley, J. R.; Ockert-Bell, M. E.; Clark, B. E.; Shepard, M. K.; Cloutis, E. A.; Fornasier, S.; Bus, S. J.

2011-10-01

This work updates our and expands our long term radar-driven observational campaign of 27 main-belt asteroids (MBAs) focused on Bus-DeMeo Xc- and Xk-type objects (Tholen X and M class asteroids) using the Arecibo radar and NASA Infrared Telescope Facilities (IRTF). Seventeen of our targets were near-simultaneously observed with radar and those observations are described in companion paper (Shepard et al., 2010). We utilized visible wavelength for a more complete compositional analysis of our targets. Compositional evidence is derived from our target asteroid spectra using three different methods: 1) a χ2 search for spectral matches in the RELAB database, 2) parametric comparisons with meteorites and 3) linear discriminant analysis. This paper synthesizes the results of the RELAB search, parametric comparisons, and linear discriminant analysis with compositional suggestions based on radar observations. We find that for six of seventeen targets with radar data, our spectral results are consistent with their radar analog (16 Psyche, 21 Lutetia, 69 Hesperia, 135 Hertha, 216 Kleopatra, and 497 Iva). For twenty out of twenty-seven objects our statistical comparisons with RELAB meteorites result in consistent analog identification, providing a degree of confidence in our parametric methods.

Discovering genetic variants in Crohn's disease by exploring genomic regions enriched of weak association signals.

PubMed

D'Addabbo, Annarita; Palmieri, Orazio; Maglietta, Rosalia; Latiano, Anna; Mukherjee, Sayan; Annese, Vito; Ancona, Nicola

2011-08-01

A meta-analysis has re-analysed previous genome-wide association scanning definitively confirming eleven genes and further identifying 21 new loci. However, the identified genes/loci still explain only the minority of genetic predisposition of Crohn's disease. To identify genes weakly involved in disease predisposition by analysing chromosomal regions enriched of single nucleotide polymorphisms with modest statistical association. We utilized the WTCCC data set evaluating 1748 CD and 2938 controls. The identification of candidate genes/loci was performed by a two-step procedure: first of all chromosomal regions enriched of weak association signals were localized; subsequently, weak signals clustered in gene regions were identified. The statistical significance was assessed by non parametric permutation tests. The cytoband enrichment analysis highlighted 44 regions (P≤0.05) enriched with single nucleotide polymorphisms significantly associated with the trait including 23 out of 31 previously confirmed and replicated genes. Importantly, we highlight further 20 novel chromosomal regions carrying approximately one hundred genes/loci with modest association. Amongst these we find compelling functional candidate genes such as MAPT, GRB2 and CREM, LCT, and IL12RB2. Our study suggests a different statistical perspective to discover genes weakly associated with a given trait, although further confirmatory functional studies are needed. Copyright © 2011 Editrice Gastroenterologica Italiana S.r.l. All rights reserved.

Tempo-spatial analysis of Fennoscandian intraplate seismicity

NASA Astrophysics Data System (ADS)

Roberts, Roland; Lund, Björn

2017-04-01

Coupled spatial-temporal patterns of the occurrence of earthquakes in Fennoscandia are analysed using non-parametric methods. The occurrence of larger events is unambiguously and very strongly temporally clustered, with major implications for the assessment of seismic hazard in areas such as Fennoscandia. In addition, there is a clear pattern of geographical migration of activity. Data from the Swedish National Seismic Network and a collated international catalogue are analysed. Results show consistent patterns on different spatial and temporal scales. We are currently investigating these patterns in order to assess the statistical significance of the tempo-spatial patterns, and to what extent these may be consistent with stress transfer mechanism such as coulomb stress and pore fluid migration. Indications are that some further mechanism is necessary in order to explain the data, perhaps related to post-glacial uplift, which is up to 1cm/year.

Scaling cosmology with variable dark-energy equation of state

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

Castro, David R.; Velten, Hermano; Zimdahl, Winfried, E-mail: drodriguez-ufes@hotmail.com, E-mail: velten@physik.uni-bielefeld.de, E-mail: winfried.zimdahl@pq.cnpq.br

2012-06-01

Interactions between dark matter and dark energy which result in a power-law behavior (with respect to the cosmic scale factor) of the ratio between the energy densities of the dark components (thus generalizing the ΛCDM model) have been considered as an attempt to alleviate the cosmic coincidence problem phenomenologically. We generalize this approach by allowing for a variable equation of state for the dark energy within the CPL-parametrization. Based on analytic solutions for the Hubble rate and using the Constitution and Union2 SNIa sets, we present a statistical analysis and classify different interacting and non-interacting models according to the Akaikemore » (AIC) and the Bayesian (BIC) information criteria. We do not find noticeable evidence for an alleviation of the coincidence problem with the mentioned type of interaction.« less

Comparison of thawing and freezing dark energy parametrizations

NASA Astrophysics Data System (ADS)

Pantazis, G.; Nesseris, S.; Perivolaropoulos, L.

2016-05-01

Dark energy equation of state w (z ) parametrizations with two parameters and given monotonicity are generically either convex or concave functions. This makes them suitable for fitting either freezing or thawing quintessence models but not both simultaneously. Fitting a data set based on a freezing model with an unsuitable (concave when increasing) w (z ) parametrization [like Chevallier-Polarski-Linder (CPL)] can lead to significant misleading features like crossing of the phantom divide line, incorrect w (z =0 ), incorrect slope, etc., that are not present in the underlying cosmological model. To demonstrate this fact we generate scattered cosmological data at both the level of w (z ) and the luminosity distance DL(z ) based on either thawing or freezing quintessence models and fit them using parametrizations of convex and of concave type. We then compare statistically significant features of the best fit w (z ) with actual features of the underlying model. We thus verify that the use of unsuitable parametrizations can lead to misleading conclusions. In order to avoid these problems it is important to either use both convex and concave parametrizations and select the one with the best χ2 or use principal component analysis thus splitting the redshift range into independent bins. In the latter case, however, significant information about the slope of w (z ) at high redshifts is lost. Finally, we propose a new family of parametrizations w (z )=w0+wa(z/1 +z )n which generalizes the CPL and interpolates between thawing and freezing parametrizations as the parameter n increases to values larger than 1.

Data analytics using canonical correlation analysis and Monte Carlo simulation

NASA Astrophysics Data System (ADS)

Rickman, Jeffrey M.; Wang, Yan; Rollett, Anthony D.; Harmer, Martin P.; Compson, Charles

2017-07-01

A canonical correlation analysis is a generic parametric model used in the statistical analysis of data involving interrelated or interdependent input and output variables. It is especially useful in data analytics as a dimensional reduction strategy that simplifies a complex, multidimensional parameter space by identifying a relatively few combinations of variables that are maximally correlated. One shortcoming of the canonical correlation analysis, however, is that it provides only a linear combination of variables that maximizes these correlations. With this in mind, we describe here a versatile, Monte-Carlo based methodology that is useful in identifying non-linear functions of the variables that lead to strong input/output correlations. We demonstrate that our approach leads to a substantial enhancement of correlations, as illustrated by two experimental applications of substantial interest to the materials science community, namely: (1) determining the interdependence of processing and microstructural variables associated with doped polycrystalline aluminas, and (2) relating microstructural decriptors to the electrical and optoelectronic properties of thin-film solar cells based on CuInSe2 absorbers. Finally, we describe how this approach facilitates experimental planning and process control.

DPARSF: A MATLAB Toolbox for "Pipeline" Data Analysis of Resting-State fMRI.

PubMed

Chao-Gan, Yan; Yu-Feng, Zang

2010-01-01

Resting-state functional magnetic resonance imaging (fMRI) has attracted more and more attention because of its effectiveness, simplicity and non-invasiveness in exploration of the intrinsic functional architecture of the human brain. However, user-friendly toolbox for "pipeline" data analysis of resting-state fMRI is still lacking. Based on some functions in Statistical Parametric Mapping (SPM) and Resting-State fMRI Data Analysis Toolkit (REST), we have developed a MATLAB toolbox called Data Processing Assistant for Resting-State fMRI (DPARSF) for "pipeline" data analysis of resting-state fMRI. After the user arranges the Digital Imaging and Communications in Medicine (DICOM) files and click a few buttons to set parameters, DPARSF will then give all the preprocessed (slice timing, realign, normalize, smooth) data and results for functional connectivity, regional homogeneity, amplitude of low-frequency fluctuation (ALFF), and fractional ALFF. DPARSF can also create a report for excluding subjects with excessive head motion and generate a set of pictures for easily checking the effect of normalization. In addition, users can also use DPARSF to extract time courses from regions of interest.

Parametric Model Based On Imputations Techniques for Partly Interval Censored Data

NASA Astrophysics Data System (ADS)

Zyoud, Abdallah; Elfaki, F. A. M.; Hrairi, Meftah

2017-12-01

The term ‘survival analysis’ has been used in a broad sense to describe collection of statistical procedures for data analysis. In this case, outcome variable of interest is time until an event occurs where the time to failure of a specific experimental unit might be censored which can be right, left, interval, and Partly Interval Censored data (PIC). In this paper, analysis of this model was conducted based on parametric Cox model via PIC data. Moreover, several imputation techniques were used, which are: midpoint, left & right point, random, mean, and median. Maximum likelihood estimate was considered to obtain the estimated survival function. These estimations were then compared with the existing model, such as: Turnbull and Cox model based on clinical trial data (breast cancer data), for which it showed the validity of the proposed model. Result of data set indicated that the parametric of Cox model proved to be more superior in terms of estimation of survival functions, likelihood ratio tests, and their P-values. Moreover, based on imputation techniques; the midpoint, random, mean, and median showed better results with respect to the estimation of survival function.

Experimental study of microwave photon statistics under parametric amplification from a single-mode thermal state in a cavity

NASA Astrophysics Data System (ADS)

Galeazzi, G.; Lombardi, A.; Ruoso, G.; Braggio, C.; Carugno, G.; Della Valle, F.; Zanello, D.; Dodonov, V. V.

2013-11-01

In this paper we present theoretical and experimental studies of the modifications of the thermal spectrum inside a microwave resonator due to a parametric amplification process. Both the degenerate and nondegenerate amplifiers are discussed. Theoretical calculations are compared with measurements performed with a microwave cavity parametric amplifier.

The balance between the serum levels of IL-6 and IL-10 cytokines discriminates mild and severe acute pneumonia.

PubMed

de Brito, Rita de Cássia Coelho Moraes; Lucena-Silva, Norma; Torres, Leuridan Cavalcante; Luna, Carlos Feitosa; Correia, Jaílson de Barros; da Silva, Giselia Alves Pontes

2016-12-01

To identify markers for earlier diagnosis of severe pneumonia, we assess the correlation between serum cytokine profile of children with different pneumonia severity. In 25 hospitalized children, 7 with mild pneumonia and 18 with severe pneumonia, the serum concentration of 11 cytokines in three sampling times were dosed. Statistical analysis included parametric and non-parametric tests, Pearson correlation and ROC curve for cut-off definition of cytokines. At admission, IL-6 serum levels were high in mild or severe pneumonia, and was associated to vomiting (P = 0.019) in both groups; and also to dyspnea (P = 0.012) and white blood cell count (P = 0.045) in patients with severe pneumonia. IL-10 levels were also high in patients with pneumonia and were associated to lymphocytosis (P = 0.025). The ROC curve of the IL-6:IL-10 serum levels ratio discriminated severe pneumonia cases at admission, and persistence of infection in the third day of antibiotic therapy, with positive predictive values of 93% and 89%, respectively. The balance between IL-6 and IL-10 serum levels showed to be a more discriminative marker for severity definition and evaluation of recovery in patients with pneumonia.

Brain responses to facial attractiveness induced by facial proportions: evidence from an fMRI study

PubMed Central

Shen, Hui; Chau, Desmond K. P.; Su, Jianpo; Zeng, Ling-Li; Jiang, Weixiong; He, Jufang; Fan, Jintu; Hu, Dewen

2016-01-01

Brain responses to facial attractiveness induced by facial proportions are investigated by using functional magnetic resonance imaging (fMRI), in 41 young adults (22 males and 19 females). The subjects underwent fMRI while they were presented with computer-generated, yet realistic face images, which had varying facial proportions, but the same neutral facial expression, baldhead and skin tone, as stimuli. Statistical parametric mapping with parametric modulation was used to explore the brain regions with the response modulated by facial attractiveness ratings (ARs). The results showed significant linear effects of the ARs in the caudate nucleus and the orbitofrontal cortex for all of the subjects, and a non-linear response profile in the right amygdala for only the male subjects. Furthermore, canonical correlation analysis was used to learn the most relevant facial ratios that were best correlated with facial attractiveness. A regression model on the fMRI-derived facial ratio components demonstrated a strong linear relationship between the visually assessed mean ARs and the predictive ARs. Overall, this study provided, for the first time, direct neurophysiologic evidence of the effects of facial ratios on facial attractiveness and suggested that there are notable gender differences in perceiving facial attractiveness as induced by facial proportions. PMID:27779211

Brain responses to facial attractiveness induced by facial proportions: evidence from an fMRI study.

PubMed

Shen, Hui; Chau, Desmond K P; Su, Jianpo; Zeng, Ling-Li; Jiang, Weixiong; He, Jufang; Fan, Jintu; Hu, Dewen

2016-10-25

Brain responses to facial attractiveness induced by facial proportions are investigated by using functional magnetic resonance imaging (fMRI), in 41 young adults (22 males and 19 females). The subjects underwent fMRI while they were presented with computer-generated, yet realistic face images, which had varying facial proportions, but the same neutral facial expression, baldhead and skin tone, as stimuli. Statistical parametric mapping with parametric modulation was used to explore the brain regions with the response modulated by facial attractiveness ratings (ARs). The results showed significant linear effects of the ARs in the caudate nucleus and the orbitofrontal cortex for all of the subjects, and a non-linear response profile in the right amygdala for only the male subjects. Furthermore, canonical correlation analysis was used to learn the most relevant facial ratios that were best correlated with facial attractiveness. A regression model on the fMRI-derived facial ratio components demonstrated a strong linear relationship between the visually assessed mean ARs and the predictive ARs. Overall, this study provided, for the first time, direct neurophysiologic evidence of the effects of facial ratios on facial attractiveness and suggested that there are notable gender differences in perceiving facial attractiveness as induced by facial proportions.

JDINAC: joint density-based non-parametric differential interaction network analysis and classification using high-dimensional sparse omics data.

PubMed

Ji, Jiadong; He, Di; Feng, Yang; He, Yong; Xue, Fuzhong; Xie, Lei

2017-10-01

A complex disease is usually driven by a number of genes interwoven into networks, rather than a single gene product. Network comparison or differential network analysis has become an important means of revealing the underlying mechanism of pathogenesis and identifying clinical biomarkers for disease classification. Most studies, however, are limited to network correlations that mainly capture the linear relationship among genes, or rely on the assumption of a parametric probability distribution of gene measurements. They are restrictive in real application. We propose a new Joint density based non-parametric Differential Interaction Network Analysis and Classification (JDINAC) method to identify differential interaction patterns of network activation between two groups. At the same time, JDINAC uses the network biomarkers to build a classification model. The novelty of JDINAC lies in its potential to capture non-linear relations between molecular interactions using high-dimensional sparse data as well as to adjust confounding factors, without the need of the assumption of a parametric probability distribution of gene measurements. Simulation studies demonstrate that JDINAC provides more accurate differential network estimation and lower classification error than that achieved by other state-of-the-art methods. We apply JDINAC to a Breast Invasive Carcinoma dataset, which includes 114 patients who have both tumor and matched normal samples. The hub genes and differential interaction patterns identified were consistent with existing experimental studies. Furthermore, JDINAC discriminated the tumor and normal sample with high accuracy by virtue of the identified biomarkers. JDINAC provides a general framework for feature selection and classification using high-dimensional sparse omics data. R scripts available at https://github.com/jijiadong/JDINAC. lxie@iscb.org. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Dynamic whole body PET parametric imaging: II. Task-oriented statistical estimation

PubMed Central

Karakatsanis, Nicolas A.; Lodge, Martin A.; Zhou, Y.; Wahl, Richard L.; Rahmim, Arman

2013-01-01

In the context of oncology, dynamic PET imaging coupled with standard graphical linear analysis has been previously employed to enable quantitative estimation of tracer kinetic parameters of physiological interest at the voxel level, thus, enabling quantitative PET parametric imaging. However, dynamic PET acquisition protocols have been confined to the limited axial field-of-view (~15–20cm) of a single bed position and have not been translated to the whole-body clinical imaging domain. On the contrary, standardized uptake value (SUV) PET imaging, considered as the routine approach in clinical oncology, commonly involves multi-bed acquisitions, but is performed statically, thus not allowing for dynamic tracking of the tracer distribution. Here, we pursue a transition to dynamic whole body PET parametric imaging, by presenting, within a unified framework, clinically feasible multi-bed dynamic PET acquisition protocols and parametric imaging methods. In a companion study, we presented a novel clinically feasible dynamic (4D) multi-bed PET acquisition protocol as well as the concept of whole body PET parametric imaging employing Patlak ordinary least squares (OLS) regression to estimate the quantitative parameters of tracer uptake rate Ki and total blood distribution volume V. In the present study, we propose an advanced hybrid linear regression framework, driven by Patlak kinetic voxel correlations, to achieve superior trade-off between contrast-to-noise ratio (CNR) and mean squared error (MSE) than provided by OLS for the final Ki parametric images, enabling task-based performance optimization. Overall, whether the observer's task is to detect a tumor or quantitatively assess treatment response, the proposed statistical estimation framework can be adapted to satisfy the specific task performance criteria, by adjusting the Patlak correlation-coefficient (WR) reference value. The multi-bed dynamic acquisition protocol, as optimized in the preceding companion study, was employed along with extensive Monte Carlo simulations and an initial clinical FDG patient dataset to validate and demonstrate the potential of the proposed statistical estimation methods. Both simulated and clinical results suggest that hybrid regression in the context of whole-body Patlak Ki imaging considerably reduces MSE without compromising high CNR. Alternatively, for a given CNR, hybrid regression enables larger reductions than OLS in the number of dynamic frames per bed, allowing for even shorter acquisitions of ~30min, thus further contributing to the clinical adoption of the proposed framework. Compared to the SUV approach, whole body parametric imaging can provide better tumor quantification, and can act as a complement to SUV, for the task of tumor detection. PMID:24080994

Dynamic whole-body PET parametric imaging: II. Task-oriented statistical estimation.

PubMed

Karakatsanis, Nicolas A; Lodge, Martin A; Zhou, Y; Wahl, Richard L; Rahmim, Arman

2013-10-21

In the context of oncology, dynamic PET imaging coupled with standard graphical linear analysis has been previously employed to enable quantitative estimation of tracer kinetic parameters of physiological interest at the voxel level, thus, enabling quantitative PET parametric imaging. However, dynamic PET acquisition protocols have been confined to the limited axial field-of-view (~15-20 cm) of a single-bed position and have not been translated to the whole-body clinical imaging domain. On the contrary, standardized uptake value (SUV) PET imaging, considered as the routine approach in clinical oncology, commonly involves multi-bed acquisitions, but is performed statically, thus not allowing for dynamic tracking of the tracer distribution. Here, we pursue a transition to dynamic whole-body PET parametric imaging, by presenting, within a unified framework, clinically feasible multi-bed dynamic PET acquisition protocols and parametric imaging methods. In a companion study, we presented a novel clinically feasible dynamic (4D) multi-bed PET acquisition protocol as well as the concept of whole-body PET parametric imaging employing Patlak ordinary least squares (OLS) regression to estimate the quantitative parameters of tracer uptake rate Ki and total blood distribution volume V. In the present study, we propose an advanced hybrid linear regression framework, driven by Patlak kinetic voxel correlations, to achieve superior trade-off between contrast-to-noise ratio (CNR) and mean squared error (MSE) than provided by OLS for the final Ki parametric images, enabling task-based performance optimization. Overall, whether the observer's task is to detect a tumor or quantitatively assess treatment response, the proposed statistical estimation framework can be adapted to satisfy the specific task performance criteria, by adjusting the Patlak correlation-coefficient (WR) reference value. The multi-bed dynamic acquisition protocol, as optimized in the preceding companion study, was employed along with extensive Monte Carlo simulations and an initial clinical (18)F-deoxyglucose patient dataset to validate and demonstrate the potential of the proposed statistical estimation methods. Both simulated and clinical results suggest that hybrid regression in the context of whole-body Patlak Ki imaging considerably reduces MSE without compromising high CNR. Alternatively, for a given CNR, hybrid regression enables larger reductions than OLS in the number of dynamic frames per bed, allowing for even shorter acquisitions of ~30 min, thus further contributing to the clinical adoption of the proposed framework. Compared to the SUV approach, whole-body parametric imaging can provide better tumor quantification, and can act as a complement to SUV, for the task of tumor detection.

Maximum Marginal Likelihood Estimation of a Monotonic Polynomial Generalized Partial Credit Model with Applications to Multiple Group Analysis.

PubMed

Falk, Carl F; Cai, Li

2016-06-01

We present a semi-parametric approach to estimating item response functions (IRF) useful when the true IRF does not strictly follow commonly used functions. Our approach replaces the linear predictor of the generalized partial credit model with a monotonic polynomial. The model includes the regular generalized partial credit model at the lowest order polynomial. Our approach extends Liang's (A semi-parametric approach to estimate IRFs, Unpublished doctoral dissertation, 2007) method for dichotomous item responses to the case of polytomous data. Furthermore, item parameter estimation is implemented with maximum marginal likelihood using the Bock-Aitkin EM algorithm, thereby facilitating multiple group analyses useful in operational settings. Our approach is demonstrated on both educational and psychological data. We present simulation results comparing our approach to more standard IRF estimation approaches and other non-parametric and semi-parametric alternatives.

Parametric and experimental analysis using a power flow approach

NASA Technical Reports Server (NTRS)

Cuschieri, J. M.

1988-01-01

Having defined and developed a structural power flow approach for the analysis of structure-borne transmission of structural vibrations, the technique is used to perform an analysis of the influence of structural parameters on the transmitted energy. As a base for comparison, the parametric analysis is first performed using a Statistical Energy Analysis approach and the results compared with those obtained using the power flow approach. The advantages of using structural power flow are thus demonstrated by comparing the type of results obtained by the two methods. Additionally, to demonstrate the advantages of using the power flow method and to show that the power flow results represent a direct physical parameter that can be measured on a typical structure, an experimental investigation of structural power flow is also presented. Results are presented for an L-shaped beam for which an analytical solution has already been obtained. Furthermore, the various methods available to measure vibrational power flow are compared to investigate the advantages and disadvantages of each method.

Model Robust Calibration: Method and Application to Electronically-Scanned Pressure Transducers

NASA Technical Reports Server (NTRS)

Walker, Eric L.; Starnes, B. Alden; Birch, Jeffery B.; Mays, James E.

2010-01-01

This article presents the application of a recently developed statistical regression method to the controlled instrument calibration problem. The statistical method of Model Robust Regression (MRR), developed by Mays, Birch, and Starnes, is shown to improve instrument calibration by reducing the reliance of the calibration on a predetermined parametric (e.g. polynomial, exponential, logarithmic) model. This is accomplished by allowing fits from the predetermined parametric model to be augmented by a certain portion of a fit to the residuals from the initial regression using a nonparametric (locally parametric) regression technique. The method is demonstrated for the absolute scale calibration of silicon-based pressure transducers.

Statistical methods for astronomical data with upper limits. I - Univariate distributions

NASA Technical Reports Server (NTRS)

Feigelson, E. D.; Nelson, P. I.

1985-01-01

The statistical treatment of univariate censored data is discussed. A heuristic derivation of the Kaplan-Meier maximum-likelihood estimator from first principles is presented which results in an expression amenable to analytic error analysis. Methods for comparing two or more censored samples are given along with simple computational examples, stressing the fact that most astronomical problems involve upper limits while the standard mathematical methods require lower limits. The application of univariate survival analysis to six data sets in the recent astrophysical literature is described, and various aspects of the use of survival analysis in astronomy, such as the limitations of various two-sample tests and the role of parametric modelling, are discussed.

Relationship between regional cerebral metabolism and consciousness disturbance in traumatic diffuse brain injury without large focal lesions: an FDG-PET study with statistical parametric mapping analysis.

PubMed

Nakayama, N; Okumura, A; Shinoda, J; Nakashima, T; Iwama, T

2006-07-01

The cerebral metabolism of patients in the chronic stage of traumatic diffuse brain injury (TDBI) has not been fully investigated. To study the relationship between regional cerebral metabolism (rCM) and consciousness disturbance in patients with TDBI. 52 patients with TDBI in the chronic stage without large focal lesions were enrolled, and rCM was evaluated by fluorine-18-fluorodeoxyglucose positron emission tomography (FDG-PET) with statistical parametric mapping (SPM). All the patients were found to have disturbed consciousness or cognitive function and were divided into the following three groups: group A (n = 22), patients in a state with higher brain dysfunction; group B (n = 13), patients in a minimally conscious state; and group C (n = 17), patients in a vegetative state. rCM patterns on FDG-PET among these groups were evaluated and compared with those of normal control subjects on statistical parametric maps. Hypometabolism was consistently indicated bilaterally in the medial prefrontal regions, the medial frontobasal regions, the cingulate gyrus and the thalamus. Hypometabolism in these regions was the most widespread and prominent in group C, and that in group B was more widespread and prominent than that in group A. Bilateral hypometabolism in the medial prefrontal regions, the medial frontobasal regions, the cingulate gyrus and the thalamus may reflect the clinical deterioration of TDBI, which is due to functional and structural disconnections of neural networks rather than due to direct cerebral focal contusion.

Estimation of a partially linear additive model for data from an outcome-dependent sampling design with a continuous outcome

PubMed Central

Tan, Ziwen; Qin, Guoyou; Zhou, Haibo

2016-01-01

Outcome-dependent sampling (ODS) designs have been well recognized as a cost-effective way to enhance study efficiency in both statistical literature and biomedical and epidemiologic studies. A partially linear additive model (PLAM) is widely applied in real problems because it allows for a flexible specification of the dependence of the response on some covariates in a linear fashion and other covariates in a nonlinear non-parametric fashion. Motivated by an epidemiological study investigating the effect of prenatal polychlorinated biphenyls exposure on children's intelligence quotient (IQ) at age 7 years, we propose a PLAM in this article to investigate a more flexible non-parametric inference on the relationships among the response and covariates under the ODS scheme. We propose the estimation method and establish the asymptotic properties of the proposed estimator. Simulation studies are conducted to show the improved efficiency of the proposed ODS estimator for PLAM compared with that from a traditional simple random sampling design with the same sample size. The data of the above-mentioned study is analyzed to illustrate the proposed method. PMID:27006375

Does multi-functionality affect technical efficiency? A non-parametric analysis of the Scottish dairy industry.

PubMed

Barnes, A P

2006-09-01

Recent policy changes within the Common Agricultural Policy have led to a shift from a solely production-led agriculture towards the promotion of multi-functionality. Conversely, the removal of production-led supports would indicate that an increased concentration on production efficiencies would seem a critical strategy for a country's future competitiveness. This paper explores the relationship between the 'multi-functional' farming attitude desired by policy makers and its effect on technical efficiency within Scottish dairy farming. Technical efficiency scores are calculated by applying the non-parametric data envelopment analysis technique and then measured against causes of inefficiency. Amongst these explanatory factors is a constructed score of multi-functionality. This research finds that, amongst other factors, a multi-functional attitude has a significant positive effect on technical efficiency. Consequently, this seems to validate the promotion of a multi-functional approach to farming currently being championed by policy-makers.

Temporal changes and variability in temperature series over Peninsular Malaysia

NASA Astrophysics Data System (ADS)

Suhaila, Jamaludin

2015-02-01

With the current concern over climate change, the descriptions on how temperature series changed over time are very useful. Annual mean temperature has been analyzed for several stations over Peninsular Malaysia. Non-parametric statistical techniques such as Mann-Kendall test and Theil-Sen slope estimation are used primarily for assessing the significance and detection of trends, while a nonparametric Pettitt's test and sequential Mann-Kendall test are adopted to detect any abrupt climate change. Statistically significance increasing trends for annual mean temperature are detected for almost all studied stations with the magnitude of significant trend varied from 0.02°C to 0.05°C per year. The results shows that climate over Peninsular Malaysia is getting warmer than before. In addition, the results of the abrupt changes in temperature using Pettitt's and sequential Mann-Kendall test reveal the beginning of trends which can be related to El Nino episodes that occur in Malaysia. In general, the analysis results can help local stakeholders and water managers to understand the risks and vulnerabilities related to climate change in terms of mean events in the region.

Preliminary evidence for linkage to chromosome 1q31-32, 10q23.3, and 16p13.3 in a South African cohort with bipolar disorder.

PubMed

Savitz, Jonathan; Cupido, Cinda-Lee; Ramesar, Raj Kumar

2007-04-05

Although the genetic variants predisposing to the development of bipolar disorder (BPD) have yet to be conclusively identified, replicated reports of linkage to particular chromosomal regions have been encouraging. Here we carried out a non-parametric linkage analysis of nine of these candidate loci in a unique South African sample of 47 BPD pedigrees (N = 350). Three polymorphic markers per region of interest (3 x 9) were typed in a Caucasian cohort of Afrikaner and British origin. Statistically significant evidence for linkage was obtained at 1q31-32, 10q23.3, and 16p13.3 with maximum NPL scores of 2.52, 2.01, and 1.84, respectively. Our results add to the growing evidence that these chromosomal regions harbor genetic variants that play a role in the development of bipolar spectrum illness. Negative results were obtained for the remaining six candidate loci, possibly due to limited statistical power. (c) 2006 Wiley-Liss, Inc.

Preliminary study on pharmacokinetics of dacarbazine and fotemustine in glioblastoma multiforme patients does not indicate gender-specific differences.

PubMed

Fazeny-Dörner, Barbara; Mader, Robert M; Piribauer, Maria; Rizovski, Blanka; Stögermaier, Barbara; Marosi, Christine

2004-06-01

Twelve patients (six female and six male) with histologically proven glioblastoma multiforme were investigated during the administration of the first cycle of dacarbazine (D; 200 mg/m) and fotemustine (F; 100 mg/m). In total, 18 blood samples were collected for pharmacokinetic analysis (maximum plasma concentration, area under the concentration-time curve and total clearance) of D and F at 14 time points during therapy. D, its metabolite 5-aminoimidazole-4-carboxamide and F were evaluated by reversed-phase HPLC. For statistical calculations, groups were compared by the non-parametric Wilcoxon test. p<0.05 was considered statistically significant. No significant gender-dependent differences were observed in the pharmacokinetics of D and F. An additional response re-evaluation of 100 patients (50 female and 50 male) with glioblastoma multiforme, treated at our institution with D and F, gave no hint of any gender-dependent different response rates. We conclude that there is no evidence, neither from pharmacokinetic nor from our clinical data, to consider different dosages of D and F in female and male patients with glioblastoma multiforme.

A Mokken scale analysis of the peer physical examination questionnaire.

PubMed

Vaughan, Brett; Grace, Sandra

2018-01-01

Peer physical examination (PPE) is a teaching and learning strategy utilised in most health profession education programs. Perceptions of participating in PPE have been described in the literature, focusing on areas of the body students are willing, or unwilling, to examine. A small number of questionnaires exist to evaluate these perceptions, however none have described the measurement properties that may allow them to be used longitudinally. The present study undertook a Mokken scale analysis of the Peer Physical Examination Questionnaire (PPEQ) to evaluate its dimensionality and structure when used with Australian osteopathy students. Students enrolled in Year 1 of the osteopathy programs at Victoria University (Melbourne, Australia) and Southern Cross University (Lismore, Australia) were invited to complete the PPEQ prior to their first practical skills examination class. R, an open-source statistics program, was used to generate the descriptive statistics and perform a Mokken scale analysis. Mokken scale analysis is a non-parametric item response theory approach that is used to cluster items measuring a latent construct. Initial analysis suggested the PPEQ did not form a single scale. Further analysis identified three subscales: 'comfort', 'concern', and 'professionalism and education'. The properties of each subscale suggested they were unidimensional with variable internal structures. The 'comfort' subscale was the strongest of the three identified. All subscales demonstrated acceptable reliability estimation statistics (McDonald's omega > 0.75) supporting the calculation of a sum score for each subscale. The subscales identified are consistent with the literature. The 'comfort' subscale may be useful to longitudinally evaluate student perceptions of PPE. Further research is required to evaluate changes with PPE and the utility of the questionnaire with other health profession education programs.

Equipment Health Monitoring with Non-Parametric Statistics for Online Early Detection and Scoring of Degradation

DTIC Science & Technology

2014-10-02

defined by Eqs. (3)–(4) (Greenwell & Finch , 2004) (Kar & Mohanty, 2006). The p value provides the metric for novelty scoring. p = QKS(z) = 2 ∞∑ j=1 (−1...provides early detection of degradation and ability to score its significance in order to inform maintenance planning and consequently reduce disruption ...actionable information, sig- nals are typically processed from raw measurements into a reduced dimension novelty summary value that may be more easily

Genes and gene networks implicated in aggression related behaviour.

PubMed

Malki, Karim; Pain, Oliver; Du Rietz, Ebba; Tosto, Maria Grazia; Paya-Cano, Jose; Sandnabba, Kenneth N; de Boer, Sietse; Schalkwyk, Leonard C; Sluyter, Frans

2014-10-01

Aggressive behaviour is a major cause of mortality and morbidity. Despite of moderate heritability estimates, progress in identifying the genetic factors underlying aggressive behaviour has been limited. There are currently three genetic mouse models of high and low aggression created using selective breeding. This is the first study to offer a global transcriptomic characterization of the prefrontal cortex across all three genetic mouse models of aggression. A systems biology approach has been applied to transcriptomic data across the three pairs of selected inbred mouse strains (Turku Aggressive (TA) and Turku Non-Aggressive (TNA), Short Attack Latency (SAL) and Long Attack Latency (LAL) mice and North Carolina Aggressive (NC900) and North Carolina Non-Aggressive (NC100)), providing novel insight into the neurobiological mechanisms and genetics underlying aggression. First, weighted gene co-expression network analysis (WGCNA) was performed to identify modules of highly correlated genes associated with aggression. Probe sets belonging to gene modules uncovered by WGCNA were carried forward for network analysis using ingenuity pathway analysis (IPA). The RankProd non-parametric algorithm was then used to statistically evaluate expression differences across the genes belonging to modules significantly associated with aggression. IPA uncovered two pathways, involving NF-kB and MAPKs. The secondary RankProd analysis yielded 14 differentially expressed genes, some of which have previously been implicated in pathways associated with aggressive behaviour, such as Adrbk2. The results highlighted plausible candidate genes and gene networks implicated in aggression-related behaviour.

SHIPS: Spectral Hierarchical Clustering for the Inference of Population Structure in Genetic Studies

PubMed Central

Bouaziz, Matthieu; Paccard, Caroline; Guedj, Mickael; Ambroise, Christophe

2012-01-01

Inferring the structure of populations has many applications for genetic research. In addition to providing information for evolutionary studies, it can be used to account for the bias induced by population stratification in association studies. To this end, many algorithms have been proposed to cluster individuals into genetically homogeneous sub-populations. The parametric algorithms, such as Structure, are very popular but their underlying complexity and their high computational cost led to the development of faster parametric alternatives such as Admixture. Alternatives to these methods are the non-parametric approaches. Among this category, AWclust has proven efficient but fails to properly identify population structure for complex datasets. We present in this article a new clustering algorithm called Spectral Hierarchical clustering for the Inference of Population Structure (SHIPS), based on a divisive hierarchical clustering strategy, allowing a progressive investigation of population structure. This method takes genetic data as input to cluster individuals into homogeneous sub-populations and with the use of the gap statistic estimates the optimal number of such sub-populations. SHIPS was applied to a set of simulated discrete and admixed datasets and to real SNP datasets, that are data from the HapMap and Pan-Asian SNP consortium. The programs Structure, Admixture, AWclust and PCAclust were also investigated in a comparison study. SHIPS and the parametric approach Structure were the most accurate when applied to simulated datasets both in terms of individual assignments and estimation of the correct number of clusters. The analysis of the results on the real datasets highlighted that the clusterings of SHIPS were the more consistent with the population labels or those produced by the Admixture program. The performances of SHIPS when applied to SNP data, along with its relatively low computational cost and its ease of use make this method a promising solution to infer fine-scale genetic patterns. PMID:23077494

Gain statistics of a fiber optical parametric amplifier with a temporally incoherent pump.

PubMed

Xu, Y Q; Murdoch, S G

2010-03-15

We present an investigation of the statistics of the gain fluctuations of a fiber optical parametric amplifier pumped with a temporally incoherent pump. We derive a simple expression for the probability distribution of the gain of the amplified optical signal. The gain statistics are shown to be a strong function of the signal detuning and allow the possibility of generating optical gain distributions with controllable long-tails. Very good agreement is found between this theory and the experimentally measured gain distributions of an incoherently pumped amplifier.

Food expenditure share analysis of household: Case study of food reserved garden area program in Bone Bolango regency of Gorontalo province

NASA Astrophysics Data System (ADS)

Buri, N.; Mantau, Z.

2018-05-01

The share of food expenditure is one of food security indicator in communities. It also can be used as an indicator of the success of rural development. The aim of this research was to find the share of food expenditure of farm households before and after the program of Food Reserved Garden Area (KRPL/FRGA) in Suwawa and Tilongkabila districtat Bone Bolango Regency of Gorontalo Province. Analysis method used share of food expenditure method. The method measure the ratio of food expenditure and total expenditure of household for a month. Statistical test used a non-parametric method, especially The Wilcoxon Test (two paired samples test). The results found that KRPL program in Ulanta Village of Suwawa district did not significantly affect the share of food expenditure of farm household. While in the South Tunggulo village of Tilongkabila district, FRGA program significantly affected the share of food expenditure.

Optoelectronic scanning system upgrade by energy center localization methods

NASA Astrophysics Data System (ADS)

Flores-Fuentes, W.; Sergiyenko, O.; Rodriguez-Quiñonez, J. C.; Rivas-López, M.; Hernández-Balbuena, D.; Básaca-Preciado, L. C.; Lindner, L.; González-Navarro, F. F.

2016-11-01

A problem of upgrading an optoelectronic scanning system with digital post-processing of the signal based on adequate methods of energy center localization is considered. An improved dynamic triangulation analysis technique is proposed by an example of industrial infrastructure damage detection. A modification of our previously published method aimed at searching for the energy center of an optoelectronic signal is described. Application of the artificial intelligence algorithm of compensation for the error of determining the angular coordinate in calculating the spatial coordinate through dynamic triangulation is demonstrated. Five energy center localization methods are developed and tested to select the best method. After implementation of these methods, digital compensation for the measurement error, and statistical data analysis, a non-parametric behavior of the data is identified. The Wilcoxon signed rank test is applied to improve the result further. For optical scanning systems, it is necessary to detect a light emitter mounted on the infrastructure being investigated to calculate its spatial coordinate by the energy center localization method.

Source localization of intermittent rhythmic delta activity in a patient with acute confusional migraine: cross-spectral analysis using standardized low-resolution brain electromagnetic tomography (sLORETA).

PubMed

Kim, Dae-Eun; Shin, Jung-Hyun; Kim, Young-Hoon; Eom, Tae-Hoon; Kim, Sung-Hun; Kim, Jung-Min

2016-01-01

Acute confusional migraine (ACM) shows typical electroencephalography (EEG) patterns of diffuse delta slowing and frontal intermittent rhythmic delta activity (FIRDA). The pathophysiology of ACM is still unclear but these patterns suggest neuronal dysfunction in specific brain areas. We performed source localization analysis of IRDA (in the frequency band of 1-3.5 Hz) to better understand the ACM mechanism. Typical IRDA EEG patterns were recorded in a patient with ACM during the acute stage. A second EEG was obtained after recovery from ACM. To identify source localization of IRDA, statistical non-parametric mapping using standardized low-resolution brain electromagnetic tomography was performed for the delta frequency band comparisons between ACM attack and non-attack periods. A difference in the current density maximum was found in the dorsal anterior cingulated cortex (ACC). The significant differences were widely distributed over the frontal, parietal, temporal and limbic lobe, paracentral lobule and insula and were predominant in the left hemisphere. Dorsal ACC dysfunction was demonstrated for the first time in a patient with ACM in this source localization analysis of IRDA. The ACC plays an important role in the frontal attentional control system and acute confusion. This dysfunction of the dorsal ACC might represent an important ACM pathophysiology.

Minimum Uncertainty Coherent States Attached to Nondegenerate Parametric Amplifiers

NASA Astrophysics Data System (ADS)

Dehghani, A.; Mojaveri, B.

2015-06-01

Exact analytical solutions for the two-mode nondegenerate parametric amplifier have been obtained by using the transformation from the two-dimensional harmonic oscillator Hamiltonian. Some important physical properties such as quantum statistics and quadrature squeezing of the corresponding states are investigated. In addition, these states carry classical features such as Poissonian statistics and minimize the Heisenberg uncertainty relation of a pair of the coordinate and the momentum operators.

Further Empirical Results on Parametric Versus Non-Parametric IRT Modeling of Likert-Type Personality Data

ERIC Educational Resources Information Center

Maydeu-Olivares, Albert

2005-01-01

Chernyshenko, Stark, Chan, Drasgow, and Williams (2001) investigated the fit of Samejima's logistic graded model and Levine's non-parametric MFS model to the scales of two personality questionnaires and found that the graded model did not fit well. We attribute the poor fit of the graded model to small amounts of multidimensionality present in…

Assessment of climate change downscaling and non-stationarity on the spatial pattern of a mangrove ecosystem in an arid coastal region of southern Iran

NASA Astrophysics Data System (ADS)

Etemadi, Halimeh; Samadi, S. Zahra; Sharifikia, Mohammad; Smoak, Joseph M.

2016-10-01

Mangrove wetlands exist in the transition zone between terrestrial and marine environments and have remarkable ecological and socio-economic value. This study uses climate change downscaling to address the question of non-stationarity influences on mangrove variations (expansion and contraction) within an arid coastal region. Our two-step approach includes downscaling models and uncertainty assessment, followed by a non-stationary and trend procedure using the Extreme Value Analysis (extRemes code). The Long Ashton Research Station Weather Generator (LARS-WG) model along with two different general circulation model (GCMs) (MIRH and HadCM3) were used to downscale climatic variables during current (1968-2011) and future (2011-2030, 2045-2065, and 2080-2099) periods. Parametric and non-parametric bootstrapping uncertainty tests demonstrated that the LARS-WGS model skillfully downscaled climatic variables at the 95 % significance level. Downscaling results using MIHR model show that minimum and maximum temperatures will increase in the future (2011-2030, 2045-2065, and 2080-2099) during winter and summer in a range of +4.21 and +4.7 °C, and +3.62 and +3.55 °C, respectively. HadCM3 analysis also revealed an increase in minimum (˜+3.03 °C) and maximum (˜+3.3 °C) temperatures during wet and dry seasons. In addition, we examined how much mangrove area has changed during the past decades and, thus, if climate change non-stationarity impacts mangrove ecosystems. Our results using remote sensing techniques and the non-parametric Mann-Whitney two-sample test indicated a sharp decline in mangrove area during 1972,1987, and 1997 periods ( p value = 0.002). Non-stationary assessment using the generalized extreme value (GEV) distributions by including mangrove area as a covariate further indicated that the null hypothesis of the stationary climate (no trend) should be rejected due to the very low p values for precipitation ( p value = 0.0027), minimum ( p value = 0.000000029) and maximum ( p value = 0.00016) temperatures. Based on non-stationary analysis and an upward trend in downscaled temperature extremes, climate change may control mangrove development in the future.

Integrated modeling environment for systems-level performance analysis of the Next-Generation Space Telescope

NASA Astrophysics Data System (ADS)

Mosier, Gary E.; Femiano, Michael; Ha, Kong; Bely, Pierre Y.; Burg, Richard; Redding, David C.; Kissil, Andrew; Rakoczy, John; Craig, Larry

1998-08-01

All current concepts for the NGST are innovative designs which present unique systems-level challenges. The goals are to outperform existing observatories at a fraction of the current price/performance ratio. Standard practices for developing systems error budgets, such as the 'root-sum-of- squares' error tree, are insufficient for designs of this complexity. Simulation and optimization are the tools needed for this project; in particular tools that integrate controls, optics, thermal and structural analysis, and design optimization. This paper describes such an environment which allows sub-system performance specifications to be analyzed parametrically, and includes optimizing metrics that capture the science requirements. The resulting systems-level design trades are greatly facilitated, and significant cost savings can be realized. This modeling environment, built around a tightly integrated combination of commercial off-the-shelf and in-house- developed codes, provides the foundation for linear and non- linear analysis on both the time and frequency-domains, statistical analysis, and design optimization. It features an interactive user interface and integrated graphics that allow highly-effective, real-time work to be done by multidisciplinary design teams. For the NGST, it has been applied to issues such as pointing control, dynamic isolation of spacecraft disturbances, wavefront sensing and control, on-orbit thermal stability of the optics, and development of systems-level error budgets. In this paper, results are presented from parametric trade studies that assess requirements for pointing control, structural dynamics, reaction wheel dynamic disturbances, and vibration isolation. These studies attempt to define requirements bounds such that the resulting design is optimized at the systems level, without attempting to optimize each subsystem individually. The performance metrics are defined in terms of image quality, specifically centroiding error and RMS wavefront error, which directly links to science requirements.

Sorting of Streptomyces Cell Pellets Using a Complex Object Parametric Analyzer and Sorter

PubMed Central

Petrus, Marloes L. C.; van Veluw, G. Jerre; Wösten, Han A. B.; Claessen, Dennis

2014-01-01

Streptomycetes are filamentous soil bacteria that are used in industry for the production of enzymes and antibiotics. When grown in bioreactors, these organisms form networks of interconnected hyphae, known as pellets, which are heterogeneous in size. Here we describe a method to analyze and sort mycelial pellets using a Complex Object Parametric Analyzer and Sorter (COPAS). Detailed instructions are given for the use of the instrument and the basic statistical analysis of the data. We furthermore describe how pellets can be sorted according to user-defined settings, which enables downstream processing such as the analysis of the RNA or protein content. Using this methodology the mechanism underlying heterogeneous growth can be tackled. This will be instrumental for improving streptomycetes as a cell factory, considering the fact that productivity correlates with pellet size. PMID:24561666

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.

Application of Semiparametric Spline Regression Model in Analyzing Factors that In uence Population Density in Central Java

NASA Astrophysics Data System (ADS)

Sumantari, Y. D.; Slamet, I.; Sugiyanto

2017-06-01

Semiparametric regression is a statistical analysis method that consists of parametric and nonparametric regression. There are various approach techniques in nonparametric regression. One of the approach techniques is spline. Central Java is one of the most densely populated province in Indonesia. Population density in this province can be modeled by semiparametric regression because it consists of parametric and nonparametric component. Therefore, the purpose of this paper is to determine the factors that in uence population density in Central Java using the semiparametric spline regression model. The result shows that the factors which in uence population density in Central Java is Family Planning (FP) active participants and district minimum wage.

Collective feature selection to identify crucial epistatic variants.

PubMed

Verma, Shefali S; Lucas, Anastasia; Zhang, Xinyuan; Veturi, Yogasudha; Dudek, Scott; Li, Binglan; Li, Ruowang; Urbanowicz, Ryan; Moore, Jason H; Kim, Dokyoon; Ritchie, Marylyn D

2018-01-01

Machine learning methods have gained popularity and practicality in identifying linear and non-linear effects of variants associated with complex disease/traits. Detection of epistatic interactions still remains a challenge due to the large number of features and relatively small sample size as input, thus leading to the so-called "short fat data" problem. The efficiency of machine learning methods can be increased by limiting the number of input features. Thus, it is very important to perform variable selection before searching for epistasis. Many methods have been evaluated and proposed to perform feature selection, but no single method works best in all scenarios. We demonstrate this by conducting two separate simulation analyses to evaluate the proposed collective feature selection approach. Through our simulation study we propose a collective feature selection approach to select features that are in the "union" of the best performing methods. We explored various parametric, non-parametric, and data mining approaches to perform feature selection. We choose our top performing methods to select the union of the resulting variables based on a user-defined percentage of variants selected from each method to take to downstream analysis. Our simulation analysis shows that non-parametric data mining approaches, such as MDR, may work best under one simulation criteria for the high effect size (penetrance) datasets, while non-parametric methods designed for feature selection, such as Ranger and Gradient boosting, work best under other simulation criteria. Thus, using a collective approach proves to be more beneficial for selecting variables with epistatic effects also in low effect size datasets and different genetic architectures. Following this, we applied our proposed collective feature selection approach to select the top 1% of variables to identify potential interacting variables associated with Body Mass Index (BMI) in ~ 44,000 samples obtained from Geisinger's MyCode Community Health Initiative (on behalf of DiscovEHR collaboration). In this study, we were able to show that selecting variables using a collective feature selection approach could help in selecting true positive epistatic variables more frequently than applying any single method for feature selection via simulation studies. We were able to demonstrate the effectiveness of collective feature selection along with a comparison of many methods in our simulation analysis. We also applied our method to identify non-linear networks associated with obesity.

New approaches to the analysis of population trends in land birds: Comment

USGS Publications Warehouse

Link, W.A.; Sauer, J.R.

1997-01-01

James et al. (1996, Ecology 77:13-27) used data from the North American Breeding Bird Survey (BBS) to examine geographic variability in patterns of population change for 26 species of wood warblers. They emphasized the importance of evaluating nonlinear patterns of change in bird populations, proposed LOESS-based non-parametric and semi-parametric analyses of BBS data, and contrasted their results with other analyses, including those of Robbins et al. (1989, Proceedings of the National Academy of Sciences 86: 7658-7662) and Peterjohn et al. (1995, Pages 3-39 in T. E. Martin and D. M. Finch, eds. Ecology and management of Neotropical migratory birds: a synthesis and review of critical issues. Oxford University Press, New York.). In this note, we briefly comment on some of the issues that arose from their analysis of BBS data, suggest a few aspects of the survey that should inspire caution in analysts, and review the differences between the LOESS-based procedures and other procedures (e.g., Link and Sauer 1994). We strongly discourage the use of James et al.'s completely non-parametric procedure, which fails to account for observer effects. Our comparisons of estimators adds to the evidence already present in the literature of the bias associated with omitting observer information in analyses of BBS data. Bias resulting from change in observer abilities should be a consideration in any analysis of BBS data.

Linear theory for filtering nonlinear multiscale systems with model error

PubMed Central

Berry, Tyrus; Harlim, John

2014-01-01

In this paper, we study filtering of multiscale dynamical systems with model error arising from limitations in resolving the smaller scale processes. In particular, the analysis assumes the availability of continuous-time noisy observations of all components of the slow variables. Mathematically, this paper presents new results on higher order asymptotic expansion of the first two moments of a conditional measure. In particular, we are interested in the application of filtering multiscale problems in which the conditional distribution is defined over the slow variables, given noisy observation of the slow variables alone. From the mathematical analysis, we learn that for a continuous time linear model with Gaussian noise, there exists a unique choice of parameters in a linear reduced model for the slow variables which gives the optimal filtering when only the slow variables are observed. Moreover, these parameters simultaneously give the optimal equilibrium statistical estimates of the underlying system, and as a consequence they can be estimated offline from the equilibrium statistics of the true signal. By examining a nonlinear test model, we show that the linear theory extends in this non-Gaussian, nonlinear configuration as long as we know the optimal stochastic parametrization and the correct observation model. However, when the stochastic parametrization model is inappropriate, parameters chosen for good filter performance may give poor equilibrium statistical estimates and vice versa; this finding is based on analytical and numerical results on our nonlinear test model and the two-layer Lorenz-96 model. Finally, even when the correct stochastic ansatz is given, it is imperative to estimate the parameters simultaneously and to account for the nonlinear feedback of the stochastic parameters into the reduced filter estimates. In numerical experiments on the two-layer Lorenz-96 model, we find that the parameters estimated online, as part of a filtering procedure, simultaneously produce accurate filtering and equilibrium statistical prediction. In contrast, an offline estimation technique based on a linear regression, which fits the parameters to a training dataset without using the filter, yields filter estimates which are worse than the observations or even divergent when the slow variables are not fully observed. This finding does not imply that all offline methods are inherently inferior to the online method for nonlinear estimation problems, it only suggests that an ideal estimation technique should estimate all parameters simultaneously whether it is online or offline. PMID:25002829

Non-Grey Radiation Modeling using Thermal Desktop/Sindaworks TFAWS06-1009

NASA Technical Reports Server (NTRS)

Anderson, Kevin R.; Paine, Chris

2006-01-01

This paper provides an overview of the non-grey radiation modeling capabilities of Cullimore and Ring's Thermal Desktop(Registered TradeMark) Version 4.8 SindaWorks software. The non-grey radiation analysis theory implemented by Sindaworks and the methodology used by the software are outlined. Representative results from a parametric trade study of a radiation shield comprised of a series of v-grooved shaped deployable panels is used to illustrate the capabilities of the SindaWorks non-grey radiation thermal analysis software using emissivities with temperature and wavelength dependency modeled via a Hagen-Rubens relationship.

Volumetric electromagnetic phase-shift spectroscopy of brain edema and hematoma.

PubMed

Gonzalez, Cesar A; Valencia, Jose A; Mora, Alfredo; Gonzalez, Fernando; Velasco, Beatriz; Porras, Martin A; Salgado, Javier; Polo, Salvador M; Hevia-Montiel, Nidiyare; Cordero, Sergio; Rubinsky, Boris

2013-01-01

Motivated by the need of poor and rural Mexico, where the population has limited access to advanced medical technology and services, we have developed a new paradigm for medical diagnostic based on the technology of "Volumetric Electromagnetic Phase Shift Spectroscopy" (VEPS), as an inexpensive partial substitute to medical imaging. VEPS, can detect changes in tissue properties inside the body through non-contact, multi-frequency electromagnetic measurements from the exterior of the body, and thereby provide rapid and inexpensive diagnostics in a way that is amenable for use in economically disadvantaged parts of the world. We describe the technology and report results from a limited pilot study with 46 healthy volunteers and eight patients with CT radiology confirmed brain edema and brain hematoma. Data analysis with a non-parametric statistical Mann-Whitney U test, shows that in the frequency range of from 26 MHz to 39 MHz, VEPS can distinguish non-invasively and without contact, with a statistical significance of p<0.05, between healthy subjects and those with a medical conditions in the brain. In the frequency range of between 153 MHz to 166 MHz it can distinguish with a statistical significance of p<0.05 between subjects with brain edema and those with a hematoma in the brain. A classifier build from measurements in these two frequency ranges can provide instantaneous diagnostic of the medical condition of the brain of a patient, from a single set of measurements. While this is a small-scale pilot study, it illustrates the potential of VEPS to change the paradigm of medical diagnostic of brain injury through a VEPS classifier-based technology. Obviously substantially larger-scale studies are needed to verify and expand on the findings in this small pilot study.

Volumetric Electromagnetic Phase-Shift Spectroscopy of Brain Edema and Hematoma

PubMed Central

Gonzalez, Cesar A.; Valencia, Jose A.; Mora, Alfredo; Gonzalez, Fernando; Velasco, Beatriz; Porras, Martin A.; Salgado, Javier; Polo, Salvador M.; Hevia-Montiel, Nidiyare; Cordero, Sergio; Rubinsky, Boris

2013-01-01

Motivated by the need of poor and rural Mexico, where the population has limited access to advanced medical technology and services, we have developed a new paradigm for medical diagnostic based on the technology of “Volumetric Electromagnetic Phase Shift Spectroscopy” (VEPS), as an inexpensive partial substitute to medical imaging. VEPS, can detect changes in tissue properties inside the body through non-contact, multi-frequency electromagnetic measurements from the exterior of the body, and thereby provide rapid and inexpensive diagnostics in a way that is amenable for use in economically disadvantaged parts of the world. We describe the technology and report results from a limited pilot study with 46 healthy volunteers and eight patients with CT radiology confirmed brain edema and brain hematoma. Data analysis with a non-parametric statistical Mann-Whitney U test, shows that in the frequency range of from 26 MHz to 39 MHz, VEPS can distinguish non-invasively and without contact, with a statistical significance of p<0.05, between healthy subjects and those with a medical conditions in the brain. In the frequency range of between 153 MHz to 166 MHz it can distinguish with a statistical significance of p<0.05 between subjects with brain edema and those with a hematoma in the brain. A classifier build from measurements in these two frequency ranges can provide instantaneous diagnostic of the medical condition of the brain of a patient, from a single set of measurements. While this is a small-scale pilot study, it illustrates the potential of VEPS to change the paradigm of medical diagnostic of brain injury through a VEPS classifier-based technology. Obviously substantially larger-scale studies are needed to verify and expand on the findings in this small pilot study. PMID:23691001

A concordance index for matched case-control studies with applications in cancer risk.

PubMed

Brentnall, Adam R; Cuzick, Jack; Field, John; Duffy, Stephen W

2015-02-10

In unmatched case-control studies, the area under the receiver operating characteristic (ROC) curve (AUC) may be used to measure how well a variable discriminates between cases and controls. The AUC is sometimes used in matched case-control studies by ignoring matching, but it lacks interpretation because it is not based on an estimate of the ROC for the population of interest. We introduce an alternative measure of discrimination that is the concordance of risk factors conditional on the matching factors. Parametric and non-parametric estimators are given for different matching scenarios, and applied to real data from breast and lung cancer case-control studies. Diagnostic plots to verify the constancy of discrimination over matching factors are demonstrated. The proposed simple measure is easy to use, interpret, more efficient than unmatched AUC statistics and may be applied to compare the conditional discrimination performance of risk factors. Copyright © 2014 John Wiley & Sons, Ltd.

Non-linear behavior of fiber composite laminates

NASA Technical Reports Server (NTRS)

Hashin, Z.; Bagchi, D.; Rosen, B. W.

1974-01-01

The non-linear behavior of fiber composite laminates which results from lamina non-linear characteristics was examined. The analysis uses a Ramberg-Osgood representation of the lamina transverse and shear stress strain curves in conjunction with deformation theory to describe the resultant laminate non-linear behavior. A laminate having an arbitrary number of oriented layers and subjected to a general state of membrane stress was treated. Parametric results and comparison with experimental data and prior theoretical results are presented.

Voxel-based statistical analysis of cerebral glucose metabolism in patients with permanent vegetative state after acquired brain injury.

PubMed

Kim, Yong Wook; Kim, Hyoung Seop; An, Young-Sil; Im, Sang Hee

2010-10-01

Permanent vegetative state is defined as the impaired level of consciousness longer than 12 months after traumatic causes and 3 months after non-traumatic causes of brain injury. Although many studies assessed the cerebral metabolism in patients with acute and persistent vegetative state after brain injury, few studies investigated the cerebral metabolism in patients with permanent vegetative state. In this study, we performed the voxel-based analysis of cerebral glucose metabolism and investigated the relationship between regional cerebral glucose metabolism and the severity of impaired consciousness in patients with permanent vegetative state after acquired brain injury. We compared the regional cerebral glucose metabolism as demonstrated by F-18 fluorodeoxyglucose positron emission tomography from 12 patients with permanent vegetative state after acquired brain injury with those from 12 control subjects. Additionally, covariance analysis was performed to identify regions where decreased changes in regional cerebral glucose metabolism significantly correlated with a decrease of level of consciousness measured by JFK-coma recovery scale. Statistical analysis was performed using statistical parametric mapping. Compared with controls, patients with permanent vegetative state demonstrated decreased cerebral glucose metabolism in the left precuneus, both posterior cingulate cortices, the left superior parietal lobule (P(corrected) < 0.001), and increased cerebral glucose metabolism in the both cerebellum and the right supramarginal cortices (P(corrected) < 0.001). In the covariance analysis, a decrease in the level of consciousness was significantly correlated with decreased cerebral glucose metabolism in the both posterior cingulate cortices (P(uncorrected) < 0.005). Our findings suggest that the posteromedial parietal cortex, which are part of neural network for consciousness, may be relevant structure for pathophysiological mechanism in patients with permanent vegetative state after acquired brain injury.

Pharmacokinetic modelling of intravenous tobramycin in adolescent and adult patients with cystic fibrosis using the nonparametric expectation maximization (NPEM) algorithm.

PubMed

Touw, D J; Vinks, A A; Neef, C

1997-06-01

The availability of personal computer programs for individualizing drug dosage regimens has stimulated the interest in modelling population pharmacokinetics. Data from 82 adolescent and adult patients with cystic fibrosis (CF) who were treated with intravenous tobramycin because of an exacerbation of their pulmonary infection were analysed with a non-parametric expectation maximization (NPEM) algorithm. This algorithm estimates the entire discrete joint probability density of the pharmacokinetic parameters. It also provides traditional parametric statistics such as the means, standard deviation, median, covariances and correlations among the various parameters. It also provides graphic-2- and 3-dimensional representations of the marginal densities of the parameters investigated. Several models for intravenous tobramycin in adolescent and adult patients with CF were compared. Covariates were total body weight (for the volume of distribution) and creatinine clearance (for the total body clearance and elimination rate). Because of lack of data on patients with poor renal function, restricted models with non-renal clearance and the non-renal elimination rate constant fixed at literature values of 0.15 L/h and 0.01 h-1 were also included. In this population, intravenous tobramycin could be best described by median (+/-dispersion factor) volume of distribution per unit of total body weight of 0.28 +/- 0.05 L/kg, elimination rate constant of 0.25 +/- 0.10 h-1 and elimination rate constant per unit of creatinine clearance of 0.0008 +/- 0.0009 h-1/(ml/min/1.73 m2). Analysis of populations of increasing size showed that using a restricted model with a non-renal elimination rate constant fixed at 0.01 h-1, a model based on a population of only 10 to 20 patients, contained parameter values similar to those of the entire population and, using the full model, a larger population (at least 40 patients) was needed.

Direct fluorescence characterisation of a picosecond seeded optical parametric amplifier

NASA Astrophysics Data System (ADS)

Stuart, N. H.; Bigourd, D.; Hill, R. W.; Robinson, T. S.; Mecseki, K.; Patankar, S.; New, G. H. C.; Smith, R. A.

2015-02-01

The temporal intensity contrast of high-power lasers based on optical parametric amplification (OPA) can be limited by parametric fluorescence from the non-linear gain stages. Here we present a spectroscopic method for direct measurement of unwanted parametric fluorescence widely applicable from unseeded to fully seeded and saturated OPA operation. Our technique employs simultaneous spectroscopy of fluorescence photons slightly outside the seed bandwidth and strongly attenuated light at the seed central wavelength. To demonstrate its applicability we have characterised the performance of a two-stage picosecond OPA pre-amplifier with 2.8×105 gain, delivering 335 μJ pulses at 1054 nm. We show that fluorescence from a strongly seeded OPA is reduced by ~500× from the undepleted to full pump depletion regimes. We also determine the vacuum fluctuation driven noise term seeding this OPA fluorescence to be 0.7±0.4 photons ps-1 nm-1 bandwidth. The resulting shot-to-shot statistics highlights a 1.5% probability of a five-fold and 0.3% probability of a ten-fold increase of fluorescence above the average value. Finally, we show that OPA fluorescence can be limited to a few-ps pedestal with 3×10-9 temporal intensity contrast 1.3 ps ahead of an intense laser pulse, a level highly attractive for large scale chirped-pulse OPA laser systems.

Improved estimation of parametric images of cerebral glucose metabolic rate from dynamic FDG-PET using volume-wise principle component analysis

NASA Astrophysics Data System (ADS)

Dai, Xiaoqian; Tian, Jie; Chen, Zhe

2010-03-01

Parametric images can represent both spatial distribution and quantification of the biological and physiological parameters of tracer kinetics. The linear least square (LLS) method is a well-estimated linear regression method for generating parametric images by fitting compartment models with good computational efficiency. However, bias exists in LLS-based parameter estimates, owing to the noise present in tissue time activity curves (TTACs) that propagates as correlated error in the LLS linearized equations. To address this problem, a volume-wise principal component analysis (PCA) based method is proposed. In this method, firstly dynamic PET data are properly pre-transformed to standardize noise variance as PCA is a data driven technique and can not itself separate signals from noise. Secondly, the volume-wise PCA is applied on PET data. The signals can be mostly represented by the first few principle components (PC) and the noise is left in the subsequent PCs. Then the noise-reduced data are obtained using the first few PCs by applying 'inverse PCA'. It should also be transformed back according to the pre-transformation method used in the first step to maintain the scale of the original data set. Finally, the obtained new data set is used to generate parametric images using the linear least squares (LLS) estimation method. Compared with other noise-removal method, the proposed method can achieve high statistical reliability in the generated parametric images. The effectiveness of the method is demonstrated both with computer simulation and with clinical dynamic FDG PET study.

Sample size considerations for clinical research studies in nuclear cardiology.

PubMed

Chiuzan, Cody; West, Erin A; Duong, Jimmy; Cheung, Ken Y K; Einstein, Andrew J

2015-12-01

Sample size calculation is an important element of research design that investigators need to consider in the planning stage of the study. Funding agencies and research review panels request a power analysis, for example, to determine the minimum number of subjects needed for an experiment to be informative. Calculating the right sample size is crucial to gaining accurate information and ensures that research resources are used efficiently and ethically. The simple question "How many subjects do I need?" does not always have a simple answer. Before calculating the sample size requirements, a researcher must address several aspects, such as purpose of the research (descriptive or comparative), type of samples (one or more groups), and data being collected (continuous or categorical). In this article, we describe some of the most frequent methods for calculating the sample size with examples from nuclear cardiology research, including for t tests, analysis of variance (ANOVA), non-parametric tests, correlation, Chi-squared tests, and survival analysis. For the ease of implementation, several examples are also illustrated via user-friendly free statistical software.

Working to eat: Vulnerability, food insecurity, and obesity among migrant and seasonal farmworker families.

PubMed

Borre, Kristen; Ertle, Luke; Graff, Mariaelisa

2010-04-01

Food insecurity and obesity have potential health consequences for migrant and seasonal farm workers (MSFW). Thirty-six Latino MSFW working in eastern North Carolina whose children attended Migrant Head Start completed interviews, focus groups and home visits. Content analysis, nutrient analysis, and non-parametric statistical analysis produced results. MSFW (63.8%) families were food insecure; of those, 34.7% experienced hunger. 32% of pre-school children were food insecure. Food secure families spent more money on food. Obesity was prevalent in adults and children but the relationship to food insecurity remains unclear. Strategies to reduce risk of foods insecurity were employed by MSFW, but employer and community assistance is needed to reduce their risk. Food insecurity is rooted in the cultural lifestyle of farmwork, poverty, and dependency. MSFW obesity and food insecurity require further study to determine the relationship with migration and working conditions. Networking and social support are important for MSFW families to improve food security. Policies and community/workplace interventions could reduce risk of food insecurity and improve the health of workers. (c) 2010 Wiley-Liss, Inc.

Scenario based optimization of a container vessel with respect to its projected operating conditions

NASA Astrophysics Data System (ADS)

Wagner, Jonas; Binkowski, Eva; Bronsart, Robert

2014-06-01

In this paper the scenario based optimization of the bulbous bow of the KRISO Container Ship (KCS) is presented. The optimization of the parametrically modeled vessel is based on a statistically developed operational profile generated from noon-to-noon reports of a comparable 3600 TEU container vessel and specific development functions representing the growth of global economy during the vessels service time. In order to consider uncertainties, statistical fluctuations are added. An analysis of these data lead to a number of most probable upcoming operating conditions (OC) the vessel will stay in the future. According to their respective likeliness an objective function for the evaluation of the optimal design variant of the vessel is derived and implemented within the parametrical optimization workbench FRIENDSHIP Framework. In the following this evaluation is done with respect to vessel's calculated effective power based on the usage of potential flow code. The evaluation shows, that the usage of scenarios within the optimization process has a strong influence on the hull form.

A subdivision-based parametric deformable model for surface extraction and statistical shape modeling of the knee cartilages

NASA Astrophysics Data System (ADS)

Fripp, Jurgen; Crozier, Stuart; Warfield, Simon K.; Ourselin, Sébastien

2006-03-01

Subdivision surfaces and parameterization are desirable for many algorithms that are commonly used in Medical Image Analysis. However, extracting an accurate surface and parameterization can be difficult for many anatomical objects of interest, due to noisy segmentations and the inherent variability of the object. The thin cartilages of the knee are an example of this, especially after damage is incurred from injuries or conditions like osteoarthritis. As a result, the cartilages can have different topologies or exist in multiple pieces. In this paper we present a topology preserving (genus 0) subdivision-based parametric deformable model that is used to extract the surfaces of the patella and tibial cartilages in the knee. These surfaces have minimal thickness in areas without cartilage. The algorithm inherently incorporates several desirable properties, including: shape based interpolation, sub-division remeshing and parameterization. To illustrate the usefulness of this approach, the surfaces and parameterizations of the patella cartilage are used to generate a 3D statistical shape model.

Indirect Reconstruction of Pore Morphology for Parametric Computational Characterization of Unidirectional Porous Iron.

PubMed

Kovačič, Aljaž; Borovinšek, Matej; Vesenjak, Matej; Ren, Zoran

2018-01-26

This paper addresses the problem of reconstructing realistic, irregular pore geometries of lotus-type porous iron for computer models that allow for simple porosity and pore size variation in computational characterization of their mechanical properties. The presented methodology uses image-recognition algorithms for the statistical analysis of pore morphology in real material specimens, from which a unique fingerprint of pore morphology at a certain porosity level is derived. The representative morphology parameter is introduced and used for the indirect reconstruction of realistic and statistically representative pore morphologies, which can be used for the generation of computational models with an arbitrary porosity. Such models were subjected to parametric computer simulations to characterize the dependence of engineering elastic modulus on the porosity of lotus-type porous iron. The computational results are in excellent agreement with experimental observations, which confirms the suitability of the presented methodology of indirect pore geometry reconstruction for computational simulations of similar porous materials.

[Detection of cerebral hypoperfusion using single photon emission computed tomography image analysis and statistical parametric mapping in patients with Parkinson's disease or progressive supranuclear palsy].

PubMed

Harada, Kengo; Saeki, Hiroshi; Matsuya, Eiji; Okita, Izumi

2013-11-01

We carried out differential diagnosis of brain blood flow images using single-photon emission computed tomography (SPECT) for patients with Parkinson's disease (PD) or progressive supranuclear paralysis (PSP) using statistical parametric mapping (SPM) and to whom we had applied anatomical standardization. We studied two groups and compared brain blood flow images using SPECT (N-isopropyl-4-iodoamphetamine [(123)I] hydrochloride injection, 222 MGq dosage i.v.). A total of 27 patients were studied using SPM: 18 with PD and 9 with PSP; humming bird sign on MRI was from moderate to medium. The decline of brain bloodstream in the PSP group was more notable in the midbrain, near the domain where the humming bird sign was observable, than in the PD group. The observable differences in brain bloodstream decline in the midbrain of PSP and PD patients suggest the potential usefulness of this technique's clinical application to distinction diagnosis.

Statistical parametric mapping of stimuli-evoked changes in quantitative blood flow using extended-focus optical coherence microscopy (Conference Presentation)

NASA Astrophysics Data System (ADS)

Marchand, Paul J.; Bouwens, Arno; Shamaei, Vincent; Nguyen, David; Extermann, Jerome; Bolmont, Tristan; Lasser, Theo

2016-03-01

Magnetic Resonance Imaging has revolutionised our understanding of brain function through its ability to image human cerebral structures non-invasively over the entire brain. By exploiting the different magnetic properties of oxygenated and deoxygenated blood, functional MRI can indirectly map areas undergoing neural activation. Alongside the development of fMRI, powerful statistical tools have been developed in an effort to shed light on the neural pathways involved in processing of sensory and cognitive information. In spite of the major improvements made in fMRI technology, the obtained spatial resolution of hundreds of microns prevents MRI in resolving and monitoring processes occurring at the cellular level. In this regard, Optical Coherence Microscopy is an ideal instrumentation as it can image at high spatio-temporal resolution. Moreover, by measuring the mean and the width of the Doppler spectra of light scattered by moving particles, OCM allows extracting the axial and lateral velocity components of red blood cells. The ability to assess quantitatively total blood velocity, as opposed to classical axial velocity Doppler OCM, is of paramount importance in brain imaging as a large proportion of cortical vascular is oriented perpendicularly to the optical axis. We combine here quantitative blood flow imaging with extended-focus Optical Coherence Microscopy and Statistical Parametric Mapping tools to generate maps of stimuli-evoked cortical hemodynamics at the capillary level.

Estimating the expected value of partial perfect information in health economic evaluations using integrated nested Laplace approximation.

PubMed

Heath, Anna; Manolopoulou, Ioanna; Baio, Gianluca

2016-10-15

The Expected Value of Perfect Partial Information (EVPPI) is a decision-theoretic measure of the 'cost' of parametric uncertainty in decision making used principally in health economic decision making. Despite this decision-theoretic grounding, the uptake of EVPPI calculations in practice has been slow. This is in part due to the prohibitive computational time required to estimate the EVPPI via Monte Carlo simulations. However, recent developments have demonstrated that the EVPPI can be estimated by non-parametric regression methods, which have significantly decreased the computation time required to approximate the EVPPI. Under certain circumstances, high-dimensional Gaussian Process (GP) regression is suggested, but this can still be prohibitively expensive. Applying fast computation methods developed in spatial statistics using Integrated Nested Laplace Approximations (INLA) and projecting from a high-dimensional into a low-dimensional input space allows us to decrease the computation time for fitting these high-dimensional GP, often substantially. We demonstrate that the EVPPI calculated using our method for GP regression is in line with the standard GP regression method and that despite the apparent methodological complexity of this new method, R functions are available in the package BCEA to implement it simply and efficiently. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Quantifying discrimination of Framingham risk functions with different survival C statistics.

PubMed

Pencina, Michael J; D'Agostino, Ralph B; Song, Linye

2012-07-10

Cardiovascular risk prediction functions offer an important diagnostic tool for clinicians and patients themselves. They are usually constructed with the use of parametric or semi-parametric survival regression models. It is essential to be able to evaluate the performance of these models, preferably with summaries that offer natural and intuitive interpretations. The concept of discrimination, popular in the logistic regression context, has been extended to survival analysis. However, the extension is not unique. In this paper, we define discrimination in survival analysis as the model's ability to separate those with longer event-free survival from those with shorter event-free survival within some time horizon of interest. This definition remains consistent with that used in logistic regression, in the sense that it assesses how well the model-based predictions match the observed data. Practical and conceptual examples and numerical simulations are employed to examine four C statistics proposed in the literature to evaluate the performance of survival models. We observe that they differ in the numerical values and aspects of discrimination that they capture. We conclude that the index proposed by Harrell is the most appropriate to capture discrimination described by the above definition. We suggest researchers report which C statistic they are using, provide a rationale for their selection, and be aware that comparing different indices across studies may not be meaningful. Copyright © 2012 John Wiley & Sons, Ltd.

Mindfulness, Empathy, and Intercultural Sensitivity amongst Undergraduate Students

ERIC Educational Resources Information Center

Menardo, Dayne Arvin

2017-01-01

This study examined the relationships amongst mindfulness, empathy, and intercultural sensitivity. Non-parametric analysis were conducted through Spearman and Hayes's PROCESS bootstrapping to examine the relationship between mindfulness and intercultural sensitivity, and whether empathy mediates the relationship between mindfulness and…

Effects of alopecia on body image and quality of life of Turkish cancer women with or without headscarf.

PubMed

Erol, Ozgul; Can, Gulbeyaz; Aydıner, Adnan

2012-10-01

The aim of this study was to find out the effects of chemotherapy-related alopecia on body image and quality of life of Turkish women who have cancer with or without headscarves and factors affecting them. This descriptive study was conducted with 204 women who received chemotherapy at the Istanbul University Institute of Oncology, Turkey. The Patient Description Form, Body Image Scale and Nightingale Symptom Assessment Scale were used in data collection. Statistical analyses were performed using descriptive statistics and non-parametric tests. Logistic regression analysis was done to predict the factors affecting body image and quality of life of the patients. No difference was found between women wearing headscarves and those who did not in respect of their body image. However, women who wore headscarves who had no alopecia felt less dissatisfied with their scars, and women not wearing headscarves who had no alopecia have been feeling less self-conscious, less dissatisfied with their appearance. There was difference in terms of quality of life: women wearing headscarves had worse physical, psychological and general well-being than others. Although there were many important factors, multivariate analysis showed that for body image, having alopecia and wearing headscarves; and for quality of life, having alopecia were the variables that had considerable effects.

Statistical detection of geographic clusters of resistant Escherichia coli in a regional network with WHONET and SaTScan.

PubMed

Park, Rachel; O'Brien, Thomas F; Huang, Susan S; Baker, Meghan A; Yokoe, Deborah S; Kulldorff, Martin; Barrett, Craig; Swift, Jamie; Stelling, John

2016-11-01

While antimicrobial resistance threatens the prevention, treatment, and control of infectious diseases, systematic analysis of routine microbiology laboratory test results worldwide can alert new threats and promote timely response. This study explores statistical algorithms for recognizing geographic clustering of multi-resistant microbes within a healthcare network and monitoring the dissemination of new strains over time. Escherichia coli antimicrobial susceptibility data from a three-year period stored in WHONET were analyzed across ten facilities in a healthcare network utilizing SaTScan's spatial multinomial model with two models for defining geographic proximity. We explored geographic clustering of multi-resistance phenotypes within the network and changes in clustering over time. Geographic clustering identified from both latitude/longitude and non-parametric facility groupings geographic models were similar, while the latter was offers greater flexibility and generalizability. Iterative application of the clustering algorithms suggested the possible recognition of the initial appearance of invasive E. coli ST131 in the clinical database of a single hospital and subsequent dissemination to others. Systematic analysis of routine antimicrobial resistance susceptibility test results supports the recognition of geographic clustering of microbial phenotypic subpopulations with WHONET and SaTScan, and iterative application of these algorithms can detect the initial appearance in and dissemination across a region prompting early investigation, response, and containment measures.

Efficient Characterization of Parametric Uncertainty of Complex (Bio)chemical Networks.

PubMed

Schillings, Claudia; Sunnåker, Mikael; Stelling, Jörg; Schwab, Christoph

2015-08-01

Parametric uncertainty is a particularly challenging and relevant aspect of systems analysis in domains such as systems biology where, both for inference and for assessing prediction uncertainties, it is essential to characterize the system behavior globally in the parameter space. However, current methods based on local approximations or on Monte-Carlo sampling cope only insufficiently with high-dimensional parameter spaces associated with complex network models. Here, we propose an alternative deterministic methodology that relies on sparse polynomial approximations. We propose a deterministic computational interpolation scheme which identifies most significant expansion coefficients adaptively. We present its performance in kinetic model equations from computational systems biology with several hundred parameters and state variables, leading to numerical approximations of the parametric solution on the entire parameter space. The scheme is based on adaptive Smolyak interpolation of the parametric solution at judiciously and adaptively chosen points in parameter space. As Monte-Carlo sampling, it is "non-intrusive" and well-suited for massively parallel implementation, but affords higher convergence rates. This opens up new avenues for large-scale dynamic network analysis by enabling scaling for many applications, including parameter estimation, uncertainty quantification, and systems design.

Efficient Characterization of Parametric Uncertainty of Complex (Bio)chemical Networks

PubMed Central

Schillings, Claudia; Sunnåker, Mikael; Stelling, Jörg; Schwab, Christoph

2015-01-01

Parametric uncertainty is a particularly challenging and relevant aspect of systems analysis in domains such as systems biology where, both for inference and for assessing prediction uncertainties, it is essential to characterize the system behavior globally in the parameter space. However, current methods based on local approximations or on Monte-Carlo sampling cope only insufficiently with high-dimensional parameter spaces associated with complex network models. Here, we propose an alternative deterministic methodology that relies on sparse polynomial approximations. We propose a deterministic computational interpolation scheme which identifies most significant expansion coefficients adaptively. We present its performance in kinetic model equations from computational systems biology with several hundred parameters and state variables, leading to numerical approximations of the parametric solution on the entire parameter space. The scheme is based on adaptive Smolyak interpolation of the parametric solution at judiciously and adaptively chosen points in parameter space. As Monte-Carlo sampling, it is “non-intrusive” and well-suited for massively parallel implementation, but affords higher convergence rates. This opens up new avenues for large-scale dynamic network analysis by enabling scaling for many applications, including parameter estimation, uncertainty quantification, and systems design. PMID:26317784

Characterizing Heterogeneity within Head and Neck Lesions Using Cluster Analysis of Multi-Parametric MRI Data.

PubMed

Borri, Marco; Schmidt, Maria A; Powell, Ceri; Koh, Dow-Mu; Riddell, Angela M; Partridge, Mike; Bhide, Shreerang A; Nutting, Christopher M; Harrington, Kevin J; Newbold, Katie L; Leach, Martin O

2015-01-01

To describe a methodology, based on cluster analysis, to partition multi-parametric functional imaging data into groups (or clusters) of similar functional characteristics, with the aim of characterizing functional heterogeneity within head and neck tumour volumes. To evaluate the performance of the proposed approach on a set of longitudinal MRI data, analysing the evolution of the obtained sub-sets with treatment. The cluster analysis workflow was applied to a combination of dynamic contrast-enhanced and diffusion-weighted imaging MRI data from a cohort of squamous cell carcinoma of the head and neck patients. Cumulative distributions of voxels, containing pre and post-treatment data and including both primary tumours and lymph nodes, were partitioned into k clusters (k = 2, 3 or 4). Principal component analysis and cluster validation were employed to investigate data composition and to independently determine the optimal number of clusters. The evolution of the resulting sub-regions with induction chemotherapy treatment was assessed relative to the number of clusters. The clustering algorithm was able to separate clusters which significantly reduced in voxel number following induction chemotherapy from clusters with a non-significant reduction. Partitioning with the optimal number of clusters (k = 4), determined with cluster validation, produced the best separation between reducing and non-reducing clusters. The proposed methodology was able to identify tumour sub-regions with distinct functional properties, independently separating clusters which were affected differently by treatment. This work demonstrates that unsupervised cluster analysis, with no prior knowledge of the data, can be employed to provide a multi-parametric characterization of functional heterogeneity within tumour volumes.

Impact of parametric uncertainty on estimation of the energy deposition into an irradiated brain tumor

NASA Astrophysics Data System (ADS)

Taverniers, Søren; Tartakovsky, Daniel M.

2017-11-01

Predictions of the total energy deposited into a brain tumor through X-ray irradiation are notoriously error-prone. We investigate how this predictive uncertainty is affected by uncertainty in both the location of the region occupied by a dose-enhancing iodinated contrast agent and the agent's concentration. This is done within the probabilistic framework in which these uncertain parameters are modeled as random variables. We employ the stochastic collocation (SC) method to estimate statistical moments of the deposited energy in terms of statistical moments of the random inputs, and the global sensitivity analysis (GSA) to quantify the relative importance of uncertainty in these parameters on the overall predictive uncertainty. A nonlinear radiation-diffusion equation dramatically magnifies the coefficient of variation of the uncertain parameters, yielding a large coefficient of variation for the predicted energy deposition. This demonstrates that accurate prediction of the energy deposition requires a proper treatment of even small parametric uncertainty. Our analysis also reveals that SC outperforms standard Monte Carlo, but its relative efficiency decreases as the number of uncertain parameters increases from one to three. A robust GSA ameliorates this problem by reducing this number.

Inducible nitric oxide expression correlates with the level of inflammation in periapical cysts.

PubMed

Matsumoto, Mariza Akemi; Ribeiro, Daniel Araki

2007-10-01

In an attempt to elucidate if inducible nitric oxide expression (iNOS) is correlated with the level of inflammation in periapical cysts with accuracy, the goal of this study was to evaluate the expression of iNOS in these ones. 30 cases were included in this study being iNOS evaluated by means of immunohistochemistry. Statistical analysis was performed by Kruskal-Wallis non-parametric test followed by the post-hoc Dunn's test. iNOS stain was detected throughout the epithelium, subepithelial fibroblasts and macrophages in all cases, indistinctly. Nevertheless, iNOS immunostaining in periapical cysts was different according to the levels of inflammation, being the strongest effect associated with intense inflammatory infiltrate. Taken together, our results indicate that immunoreactivity of iNOS was expressed in several cellular types present in periapical cyst, being positively correlated with the level of inflammation. Therefore, iNOS expression plays an important role in the pathogenesis of periapical cysts.

Family members' satisfaction with care and decision-making in intensive care units and post-stay follow-up needs-a cross-sectional survey study.

PubMed

Frivold, Gro; Slettebø, Åshild; Heyland, Daren K; Dale, Bjørg

2018-01-01

The aim of this study was to explore family members' satisfaction with care and decision-making during the intensive care units stay and their follow-up needs after the patient's discharge or death. A cross-sectional survey study was conducted. Family members of patients recently treated in an ICU were participating. The questionnaire contented of background variables, the instrument Family Satisfaction in ICU (FS-ICU 24) and questions about follow-up needs. Descriptive and non-parametric statistics and a multiple linear regression were used in the analysis. A total of 123 (47%) relatives returned the questionnaire. Satisfaction with care was higher scored than satisfaction with decision-making. Follow- up needs after the ICU stay was reported by 19 (17%) of the participants. Gender and length of the ICU stay were shown as factors identified to predict follow-up needs.

The Evolution of Your Success Lies at the Centre of Your Co-Authorship Network

PubMed Central

Servia-Rodríguez, Sandra; Noulas, Anastasios; Mascolo, Cecilia; Fernández-Vilas, Ana; Díaz-Redondo, Rebeca P.

2015-01-01

Collaboration among scholars and institutions is progressively becoming essential to the success of research grant procurement and to allow the emergence and evolution of scientific disciplines. Our work focuses on analysing if the volume of collaborations of one author together with the relevance of his collaborators is somewhat related to his research performance over time. In order to prove this relation we collected the temporal distributions of scholars’ publications and citations from the Google Scholar platform and the co-authorship network (of Computer Scientists) underlying the well-known DBLP bibliographic database. By the application of time series clustering, social network analysis and non-parametric statistics, we observe that scholars with similar publications (citations) patterns also tend to have a similar centrality in the co-authorship network. To our knowledge, this is the first work that considers success evolution with respect to co-authorship. PMID:25760732

Evaluating the features of the brain waves to quantify ADHD improvement by neurofeedback.

PubMed

Dehghanpour, Peyman; Einalou, Zahra

2017-10-23

Attention-deficit/hyperactivity disorder (ADHD), as one of the most common neurological disorders in children and adolescents, is characterized by decentralization, slow learning, distraction and hyperactivity. Studies have shown that in addition to medication, neurofeedback training can also be used to partially control the brain activity of these patients. In this study, using the brain signals processing before and after the treatment in 10 children treated by neurofeedback, the changes were evaluated by non-parametric statistical analysis and impact of neurofeedback on brain frequency bands was investigated. Finally, the results were compared with the protocols introduced in this paper and before researches. The results of Kruskal-Wallis test showed an approximately significant increase in the relative power of gamma and an approximately significant reduction in the ratio of relative power of alpha/beta. It represents the emotional response, elicited by the successful learning and diminished ratio of slow learning to active learning respectively.

Cost-effectiveness research in cancer therapy: a systematic review of literature trends, methods and the influence of funding

PubMed Central

Al-Badriyeh, Daoud; Alameri, Marwah; Al-Okka, Randa

2017-01-01

Objective To perform a first-time analysis of the cost-effectiveness (CE) literature on chemotherapies, of all types, in cancer, in terms of trends and change over time, including the influence of industry funding. Design Systematic review. Setting A wide range of cancer-related research settings within healthcare, including health systems, hospitals and medical centres. Participants All literature comparative CE research of drug-based cancer therapies in the period 1986 to 2015. Primary and secondary outcome measures Primary outcomes are the literature trends in relation to journal subject category, authorship, research design, data sources, funds and consultation involvement. An additional outcome measure is the association between industry funding and study outcomes. Analysis Descriptive statistics and the χ2, Fisher exact or Somer's D tests were used to perform non-parametric statistics, with a p value of <0.05 as the statistical significance measure. Results Total 574 publications were analysed. The drug-related CE literature expands over time, with increased publishing in the healthcare sciences and services journal subject category (p<0.001). The retrospective data collection in studies increased over time (p<0.001). The usage of prospective data, however, has been decreasing (p<0.001) in relation to randomised clinical trials (RCTs), but is unchanging for non-RCT studies. The industry-sponsored CE studies have especially been increasing (p<0.001), in contrast to those sponsored by other sources. While paid consultation involvement grew throughout the years, the declaration of funding for this is relatively limited. Importantly, there is evidence that industry funding is associated with favourable result to the sponsor (p<0.001). Conclusions This analysis demonstrates clear trends in how the CE cancer research is presented to the practicing community, including in relation to journals, study designs, authorship and consultation, together with increased financial sponsorship by pharmaceutical industries, which may be more influencing study outcomes than other funding sources. PMID:28131999

Exploring the Link Between Streamflow Trends and Climate Change in Indiana, USA

NASA Astrophysics Data System (ADS)

Kumar, S.; Kam, J.; Thurner, K.; Merwade, V.

2007-12-01

Streamflow trends in Indiana are evaluated for 85 USGS streamflow gaging stations that have continuous unregulated streamflow records varying from 10 to 80 years. The trends are analyzed by using the non-parametric Mann-Kendall test with prior trend-free pre-whitening to remove serial correlation in the data. Bootstrap method is used to establish field significance of the results. Trends are computed for 12 streamflow statistics to include low-, medium- (median and mean flow), and high-flow conditions on annual and seasonal time step. The analysis is done for six study periods, ranging from 10 years to more than 65 years, all ending in 2003. The trends in annual average streamflow, for 50 years study period, are compared with annual average precipitation trends from 14 National Climatic Data Center (NCDC) stations in Indiana, that have 50 years of continuous daily record. The results show field significant positive trends in annual low and medium streamflow statistics at majority of gaging stations for study periods that include 40 or more years of records. In seasonal analysis, all flow statistics in summer and fall (low flow seasons), and only low flow statistics in winter and spring (high flow seasons) are showing positive trends. No field significant trends in annual and seasonal flow statistics are observed for study periods that include 25 or fewer years of records, except for northern Indiana where localized negative trends are observed in 10 and 15 years study periods. Further, stream flow trends are found to be highly correlated with precipitation trends on annual time step. No apparent climate change signal is observed in Indiana stream flow records.

Improved dynamical scaling analysis using the kernel method for nonequilibrium relaxation.

PubMed

Echinaka, Yuki; Ozeki, Yukiyasu

2016-10-01

The dynamical scaling analysis for the Kosterlitz-Thouless transition in the nonequilibrium relaxation method is improved by the use of Bayesian statistics and the kernel method. This allows data to be fitted to a scaling function without using any parametric model function, which makes the results more reliable and reproducible and enables automatic and faster parameter estimation. Applying this method, the bootstrap method is introduced and a numerical discrimination for the transition type is proposed.

Space biology initiative program definition review. Trade study 3: Hardware miniaturization versus cost

NASA Technical Reports Server (NTRS)

Jackson, L. Neal; Crenshaw, John, Sr.; Davidson, William L.; Herbert, Frank J.; Bilodeau, James W.; Stoval, J. Michael; Sutton, Terry

1989-01-01

The optimum hardware miniaturization level with the lowest cost impact for space biology hardware was determined. Space biology hardware and/or components/subassemblies/assemblies which are the most likely candidates for application of miniaturization are to be defined and relative cost impacts of such miniaturization are to be analyzed. A mathematical or statistical analysis method with the capability to support development of parametric cost analysis impacts for levels of production design miniaturization are provided.

ELECTROMYOGRAPHIC EVALUATION OF MASTICATION AND SWALLOWING IN ELDERLY INDIVIDUALS WITH MANDIBULAR FIXED IMPLANTSUPPORTED PROSTHESES

PubMed Central

Berretin-Felix, Giédre; Nary, Hugo; Padovani, Carlos Roberto; Trindade, Alceu Sergio; Machado, Wellington Monteiro

2008-01-01

This study evaluated the effect of implant-supported oral rehabilitation in the mandible on the electromyographic activity during mastication and swallowing in edentulous elderly individuals. Fifteen patients aged more than 60 years were evaluated, being 10 females and 5 males. All patients were edentulous, wore removable complete dentures on both dental arches, and had the mandibular dentures replaced by implant-supported prostheses. All patients were submitted to electromyographic evaluation of the masseter, superior orbicularis oris muscles, and the submental muscles, before surgery and 3, 6 and 18 months postoperatively, using foods of different textures. The results obtained at the different periods were analyzed statistically by Kruskal-Wallis non-parametric test. Statistical analysis showed that only the masseter muscle had a significant loss in electromyographic activity (p<0.001), with a tendency of similar response for the submental muscles. Moreover, there was an increase in the activity of the orbicularis oris muscle during rubber chewing after treatment, yet without statistically significant difference. Mandibular fixed implant-supported prostheses in elderly individuals revealed a decrease in electromyographic amplitude for the masseter muscles during swallowing, which may indicate adaptation to new conditions of stability provided by fixation of the complete denture in the mandibular arch. PMID:19089202

Test of the Chevallier-Polarski-Linder parametrization for rapid dark energy equation of state transitions

NASA Astrophysics Data System (ADS)

Linden, Sebastian; Virey, Jean-Marc

2008-07-01

We test the robustness and flexibility of the Chevallier-Polarski-Linder (CPL) parametrization of the dark energy equation of state w(z)=w0+wa(z)/(1+z) in recovering a four-parameter steplike fiducial model. We constrain the parameter space region of the underlying fiducial model where the CPL parametrization offers a reliable reconstruction. It turns out that non-negligible biases leak into the results for recent (z<2.5) rapid transitions, but that CPL yields a good reconstruction in all other cases. The presented analysis is performed with supernova Ia data as forecasted for a space mission like SNAP/JDEM, combined with future expectations for the cosmic microwave background shift parameter R and the baryonic acoustic oscillation parameter A.

Modeling vertebrate diversity in Oregon using satellite imagery

NASA Astrophysics Data System (ADS)

Cablk, Mary Elizabeth

Vertebrate diversity was modeled for the state of Oregon using a parametric approach to regression tree analysis. This exploratory data analysis effectively modeled the non-linear relationships between vertebrate richness and phenology, terrain, and climate. Phenology was derived from time-series NOAA-AVHRR satellite imagery for the year 1992 using two methods: principal component analysis and derivation of EROS data center greenness metrics. These two measures of spatial and temporal vegetation condition incorporated the critical temporal element in this analysis. The first three principal components were shown to contain spatial and temporal information about the landscape and discriminated phenologically distinct regions in Oregon. Principal components 2 and 3, 6 greenness metrics, elevation, slope, aspect, annual precipitation, and annual seasonal temperature difference were investigated as correlates to amphibians, birds, all vertebrates, reptiles, and mammals. Variation explained for each regression tree by taxa were: amphibians (91%), birds (67%), all vertebrates (66%), reptiles (57%), and mammals (55%). Spatial statistics were used to quantify the pattern of each taxa and assess validity of resulting predictions from regression tree models. Regression tree analysis was relatively robust against spatial autocorrelation in the response data and graphical results indicated models were well fit to the data.

The extension of the parametrization of the radio source coordinates in geodetic VLBI and its impact on the time series analysis

NASA Astrophysics Data System (ADS)

Karbon, Maria; Heinkelmann, Robert; Mora-Diaz, Julian; Xu, Minghui; Nilsson, Tobias; Schuh, Harald

2017-07-01

The radio sources within the most recent celestial reference frame (CRF) catalog ICRF2 are represented by a single, time-invariant coordinate pair. The datum sources were chosen mainly according to certain statistical properties of their position time series. Yet, such statistics are not applicable unconditionally, and also ambiguous. However, ignoring systematics in the source positions of the datum sources inevitably leads to a degradation of the quality of the frame and, therefore, also of the derived quantities such as the Earth orientation parameters. One possible approach to overcome these deficiencies is to extend the parametrization of the source positions, similarly to what is done for the station positions. We decided to use the multivariate adaptive regression splines algorithm to parametrize the source coordinates. It allows a great deal of automation, by combining recursive partitioning and spline fitting in an optimal way. The algorithm finds the ideal knot positions for the splines and, thus, the best number of polynomial pieces to fit the data autonomously. With that we can correct the ICRF2 a priori coordinates for our analysis and eliminate the systematics in the position estimates. This allows us to introduce also special handling sources into the datum definition, leading to on average 30 % more sources in the datum. We find that not only the CPO can be improved by more than 10 % due to the improved geometry, but also the station positions, especially in the early years of VLBI, can benefit greatly.

Effect of Injector Geometry on Atomization of a Liquid-Liquid Double Swirl Coaxial Injector Using Non-invasive Laser, Optical and X-ray Techniques

NASA Technical Reports Server (NTRS)

Radke, C. R.; Meyer, T. R.

2014-01-01

The spray characteristics of a liquid-liquid double swirl coaxial injector were studied using non-invasive optical, laser, and X-ray diagnostics. A parametric study of injector exit geometry demonstrated that spray breakup time, breakup type and sheet stability could be controlled with exit geometry. Phase Doppler interferometry was used to characterize droplet statistics and non-dimensional droplet parameters over a range of inlet conditions and for various fluids allowing for a study on the role of specific fluid properties in atomization. Further, X-ray radiography allowed for investigation of sheet thickness and breakup length to be quantified for different recess exit diameters and inlet pressures. Finally, computed tomography scans revealed that the spray cone was distinctively non-uniform and comprised of several pockets of increased mass flux.

Non-Gaussian Distributions Affect Identification of Expression Patterns, Functional Annotation, and Prospective Classification in Human Cancer Genomes

PubMed Central

Marko, Nicholas F.; Weil, Robert J.

2012-01-01

Introduction Gene expression data is often assumed to be normally-distributed, but this assumption has not been tested rigorously. We investigate the distribution of expression data in human cancer genomes and study the implications of deviations from the normal distribution for translational molecular oncology research. Methods We conducted a central moments analysis of five cancer genomes and performed empiric distribution fitting to examine the true distribution of expression data both on the complete-experiment and on the individual-gene levels. We used a variety of parametric and nonparametric methods to test the effects of deviations from normality on gene calling, functional annotation, and prospective molecular classification using a sixth cancer genome. Results Central moments analyses reveal statistically-significant deviations from normality in all of the analyzed cancer genomes. We observe as much as 37% variability in gene calling, 39% variability in functional annotation, and 30% variability in prospective, molecular tumor subclassification associated with this effect. Conclusions Cancer gene expression profiles are not normally-distributed, either on the complete-experiment or on the individual-gene level. Instead, they exhibit complex, heavy-tailed distributions characterized by statistically-significant skewness and kurtosis. The non-Gaussian distribution of this data affects identification of differentially-expressed genes, functional annotation, and prospective molecular classification. These effects may be reduced in some circumstances, although not completely eliminated, by using nonparametric analytics. This analysis highlights two unreliable assumptions of translational cancer gene expression analysis: that “small” departures from normality in the expression data distributions are analytically-insignificant and that “robust” gene-calling algorithms can fully compensate for these effects. PMID:23118863

Multivariate Statistical Analysis of Water Quality data in Indian River Lagoon, Florida

NASA Astrophysics Data System (ADS)

Sayemuzzaman, M.; Ye, M.

2015-12-01

The Indian River Lagoon, is part of the longest barrier island complex in the United States, is a region of particular concern to the environmental scientist because of the rapid rate of human development throughout the region and the geographical position in between the colder temperate zone and warmer sub-tropical zone. Thus, the surface water quality analysis in this region always brings the newer information. In this present study, multivariate statistical procedures were applied to analyze the spatial and temporal water quality in the Indian River Lagoon over the period 1998-2013. Twelve parameters have been analyzed on twelve key water monitoring stations in and beside the lagoon on monthly datasets (total of 27,648 observations). The dataset was treated using cluster analysis (CA), principle component analysis (PCA) and non-parametric trend analysis. The CA was used to cluster twelve monitoring stations into four groups, with stations on the similar surrounding characteristics being in the same group. The PCA was then applied to the similar groups to find the important water quality parameters. The principal components (PCs), PC1 to PC5 was considered based on the explained cumulative variances 75% to 85% in each cluster groups. Nutrient species (phosphorus and nitrogen), salinity, specific conductivity and erosion factors (TSS, Turbidity) were major variables involved in the construction of the PCs. Statistical significant positive or negative trends and the abrupt trend shift were detected applying Mann-Kendall trend test and Sequential Mann-Kendall (SQMK), for each individual stations for the important water quality parameters. Land use land cover change pattern, local anthropogenic activities and extreme climate such as drought might be associated with these trends. This study presents the multivariate statistical assessment in order to get better information about the quality of surface water. Thus, effective pollution control/management of the surface waters can be undertaken.

Calculating stage duration statistics in multistage diseases.

PubMed

Komarova, Natalia L; Thalhauser, Craig J

2011-01-01

Many human diseases are characterized by multiple stages of progression. While the typical sequence of disease progression can be identified, there may be large individual variations among patients. Identifying mean stage durations and their variations is critical for statistical hypothesis testing needed to determine if treatment is having a significant effect on the progression, or if a new therapy is showing a delay of progression through a multistage disease. In this paper we focus on two methods for extracting stage duration statistics from longitudinal datasets: an extension of the linear regression technique, and a counting algorithm. Both are non-iterative, non-parametric and computationally cheap methods, which makes them invaluable tools for studying the epidemiology of diseases, with a goal of identifying different patterns of progression by using bioinformatics methodologies. Here we show that the regression method performs well for calculating the mean stage durations under a wide variety of assumptions, however, its generalization to variance calculations fails under realistic assumptions about the data collection procedure. On the other hand, the counting method yields reliable estimations for both means and variances of stage durations. Applications to Alzheimer disease progression are discussed.

Scale-Free Nonparametric Factor Analysis: A User-Friendly Introduction with Concrete Heuristic Examples.

ERIC Educational Resources Information Center

Mittag, Kathleen Cage

Most researchers using factor analysis extract factors from a matrix of Pearson product-moment correlation coefficients. A method is presented for extracting factors in a non-parametric way, by extracting factors from a matrix of Spearman rho (rank correlation) coefficients. It is possible to factor analyze a matrix of association such that…

Parametric study of variation in cargo-airplane performance related to progression from current to spanloader designs

NASA Technical Reports Server (NTRS)

Toll, T. A.

1980-01-01

A parametric analysis was made to investigate the relationship between current cargo airplanes and possible future designs that may differ greatly in both size and configuration. The method makes use of empirical scaling laws developed from statistical studies of data from current and advanced airplanes and, in addition, accounts for payload density, effects of span distributed load, and variations in tail area ratio. The method is believed to be particularly useful for exploratory studies of design and technology options for large airplanes. The analysis predicts somewhat more favorable variations of the ratios of payload to gross weight and block fuel to payload as the airplane size is increased than has been generally understood from interpretations of the cube-square law. In terms of these same ratios, large all wing (spanloader) designs show an advantage over wing-fuselage designs.

Identifying Epigenetic Biomarkers using Maximal Relevance and Minimal Redundancy Based Feature Selection for Multi-Omics Data.

PubMed

Mallik, Saurav; Bhadra, Tapas; Maulik, Ujjwal

2017-01-01

Epigenetic Biomarker discovery is an important task in bioinformatics. In this article, we develop a new framework of identifying statistically significant epigenetic biomarkers using maximal-relevance and minimal-redundancy criterion based feature (gene) selection for multi-omics dataset. Firstly, we determine the genes that have both expression as well as methylation values, and follow normal distribution. Similarly, we identify the genes which consist of both expression and methylation values, but do not follow normal distribution. For each case, we utilize a gene-selection method that provides maximal-relevant, but variable-weighted minimum-redundant genes as top ranked genes. For statistical validation, we apply t-test on both the expression and methylation data consisting of only the normally distributed top ranked genes to determine how many of them are both differentially expressed andmethylated. Similarly, we utilize Limma package for performing non-parametric Empirical Bayes test on both expression and methylation data comprising only the non-normally distributed top ranked genes to identify how many of them are both differentially expressed and methylated. We finally report the top-ranking significant gene-markerswith biological validation. Moreover, our framework improves positive predictive rate and reduces false positive rate in marker identification. In addition, we provide a comparative analysis of our gene-selection method as well as othermethods based on classificationperformances obtained using several well-known classifiers.

Physics-based statistical model and simulation method of RF propagation in urban environments

DOEpatents

Pao, Hsueh-Yuan; Dvorak, Steven L.

2010-09-14

A physics-based statistical model and simulation/modeling method and system of electromagnetic wave propagation (wireless communication) in urban environments. In particular, the model is a computationally efficient close-formed parametric model of RF propagation in an urban environment which is extracted from a physics-based statistical wireless channel simulation method and system. The simulation divides the complex urban environment into a network of interconnected urban canyon waveguides which can be analyzed individually; calculates spectral coefficients of modal fields in the waveguides excited by the propagation using a database of statistical impedance boundary conditions which incorporates the complexity of building walls in the propagation model; determines statistical parameters of the calculated modal fields; and determines a parametric propagation model based on the statistical parameters of the calculated modal fields from which predictions of communications capability may be made.

Examination of influential observations in penalized spline regression

NASA Astrophysics Data System (ADS)

Türkan, Semra

2013-10-01

In parametric or nonparametric regression models, the results of regression analysis are affected by some anomalous observations in the data set. Thus, detection of these observations is one of the major steps in regression analysis. These observations are precisely detected by well-known influence measures. Pena's statistic is one of them. In this study, Pena's approach is formulated for penalized spline regression in terms of ordinary residuals and leverages. The real data and artificial data are used to see illustrate the effectiveness of Pena's statistic as to Cook's distance on detecting influential observations. The results of the study clearly reveal that the proposed measure is superior to Cook's Distance to detect these observations in large data set.

Genome-wide scan for genes involved in bipolar affective disorder in 70 European families ascertained through a bipolar type I early-onset proband: supportive evidence for linkage at 3p14

PubMed Central

Etain, Bruno; Mathieu, Flavie; Rietschel, Marcella; Maier, Wolfgang; Albus, Margot; Mckeon, Patrick; Roche, S.; Kealey, Carmel; Blackwood, Douglas; Muir, Walter; Bellivier, Franc; Henry, C.; Dina, Christian; Gallina, Sophie; Gurling, H.; Malafosse, Alain; Preisig, Martin; Ferrero, François; Cichon, Sven; Schumacher, J.; Ohlraun, Stéphanie; Borrmann-Hassenbach, M.; Propping, Peter; Abou Jamra, Rami; Schulze, Thomas G.; Marusic, Andrej; Dernovsek, Mojca Z.; Giros, Bruno; Bourgeron, Thomas; Lemainque, Arnaud; Bacq, Delphine; Betard, Christine; Charon, Céline; Nöthen, Markus M.; Lathrop, Mark; Leboyer, Marion

2006-01-01

Summary Preliminary studies suggested that age at onset (AAO) may help to define homogeneous bipolar affective disorder (BPAD) subtypes. This candidate symptom approach might be useful to identify vulnerability genes. Thus, the probability of detecting major disease-causing genes might be increased by focusing on families with early-onset BPAD type I probands. This study was conducted as part of the European Collaborative Study of Early Onset BPAD (France, Germany, Ireland, Scotland, Switzerland, England, Slovenia). We performed a genome-wide search with 384 microsatellite markers using non parametric linkage analysis in 87 sib-pairs ascertained through an early-onset BPAD type I proband (age at onset of 21 years or below). Non parametric multi-point analysis suggested eight regions of linkage with p-values <0.01 (2p21, 2q14.3, 3p14, 5q33, 7q36, 10q23, 16q23 and 20p12). The 3p14 region showed the most significant linkage (genome-wide p-value estimated over 10.000 simulated replicates of 0.015 [0.01–0.02]). After genome-wide search analysis, we performed additional linkage analyses with increase marker density using markers in four regions suggestive for linkage and having an information contents lower than 75% (3p14, 10q23, 16q23 and 20p12). For these regions, the information content improved by about 10%. In chromosome 3, the non parametric linkage score increased from 3.51 to 3.83. This study is the first to use early onset bipolar type I probands in an attempt to increase sample homogeneity. These preliminary findings require confirmation in independent panels of families. PMID:16534504

Whole-body PET parametric imaging employing direct 4D nested reconstruction and a generalized non-linear Patlak model

NASA Astrophysics Data System (ADS)

Karakatsanis, Nicolas A.; Rahmim, Arman

2014-03-01

Graphical analysis is employed in the research setting to provide quantitative estimation of PET tracer kinetics from dynamic images at a single bed. Recently, we proposed a multi-bed dynamic acquisition framework enabling clinically feasible whole-body parametric PET imaging by employing post-reconstruction parameter estimation. In addition, by incorporating linear Patlak modeling within the system matrix, we enabled direct 4D reconstruction in order to effectively circumvent noise amplification in dynamic whole-body imaging. However, direct 4D Patlak reconstruction exhibits a relatively slow convergence due to the presence of non-sparse spatial correlations in temporal kinetic analysis. In addition, the standard Patlak model does not account for reversible uptake, thus underestimating the influx rate Ki. We have developed a novel whole-body PET parametric reconstruction framework in the STIR platform, a widely employed open-source reconstruction toolkit, a) enabling accelerated convergence of direct 4D multi-bed reconstruction, by employing a nested algorithm to decouple the temporal parameter estimation from the spatial image update process, and b) enhancing the quantitative performance particularly in regions with reversible uptake, by pursuing a non-linear generalized Patlak 4D nested reconstruction algorithm. A set of published kinetic parameters and the XCAT phantom were employed for the simulation of dynamic multi-bed acquisitions. Quantitative analysis on the Ki images demonstrated considerable acceleration in the convergence of the nested 4D whole-body Patlak algorithm. In addition, our simulated and patient whole-body data in the postreconstruction domain indicated the quantitative benefits of our extended generalized Patlak 4D nested reconstruction for tumor diagnosis and treatment response monitoring.

Parametric inference for biological sequence analysis.

PubMed

Pachter, Lior; Sturmfels, Bernd

2004-11-16

One of the major successes in computational biology has been the unification, by using the graphical model formalism, of a multitude of algorithms for annotating and comparing biological sequences. Graphical models that have been applied to these problems include hidden Markov models for annotation, tree models for phylogenetics, and pair hidden Markov models for alignment. A single algorithm, the sum-product algorithm, solves many of the inference problems that are associated with different statistical models. This article introduces the polytope propagation algorithm for computing the Newton polytope of an observation from a graphical model. This algorithm is a geometric version of the sum-product algorithm and is used to analyze the parametric behavior of maximum a posteriori inference calculations for graphical models.

Least Squares Procedures.

ERIC Educational Resources Information Center

Hester, Yvette

Least squares methods are sophisticated mathematical curve fitting procedures used in all classical parametric methods. The linear least squares approximation is most often associated with finding the "line of best fit" or the regression line. Since all statistical analyses are correlational and all classical parametric methods are least…

Numerical investigations of non-collinear optical parametric chirped pulse amplification for Laguerre-Gaussian vortex beam

NASA Astrophysics Data System (ADS)

Xu, Lu; Yu, Lianghong; Liang, Xiaoyan

2016-04-01

We present for the first time a scheme to amplify a Laguerre-Gaussian vortex beam based on non-collinear optical parametric chirped pulse amplification (OPCPA). In addition, a three-dimensional numerical model of non-collinear optical parametric amplification was deduced in the frequency domain, in which the effects of non-collinear configuration, temporal and spatial walk-off, group-velocity dispersion and diffraction were also taken into account, to trace the dynamics of the Laguerre-Gaussian vortex beam and investigate its critical parameters in the non-collinear OPCPA process. Based on the numerical simulation results, the scheme shows promise for implementation in a relativistic twisted laser pulse system, which will diversify the light-matter interaction field.

3D Facial Pattern Analysis for Autism

DTIC Science & Technology

2010-07-01

each individual’s data were scaled by the geometric mean of all possible linear distances between landmarks, following. The first two principal...over traditional template matching in that it can represent geometrical and non- geometrical changes of an object in the parametric template space...set of vertex templates can be generated from the root template by geometric or non- geometric transformation. Let Mtt ,...1 be M normalized vertex

Sleep-wake cycle of the healthy term newborn infant in the immediate postnatal period.

PubMed

Korotchikova, Irina; Stevenson, Nathan J; Livingstone, Vicki; Ryan, C Anthony; Boylan, Geraldine B

2016-04-01

To examine sleep-wake cycle (SWC) composition of healthy term infants in the immediate postnatal period using EEG, and investigate factors that might influence it. Multichannel video-EEG was recorded for a median of 61.9 min (IQR: 60.0-69.3). The absolute and relative scores of sleep states were calculated for each infant's recording. Parametric/non-parametric statistical tests and multiple linear regression analysis were used to investigate the influence of perinatal factors on SWC composition. Eighty healthy term infants aged 1-36 h were studied. A well-developed SWC was evident as early as within the first 6h after birth. The mean (SD) percentage of active sleep (AS) was 52.1% (12.9) and quiet sleep (QS) was 38.6% (12.5). AS was longer and QS shorter in infants delivered by elective caesarean section (CS) compared to infants delivered by vaginal delivery or emergency CS. This is the first large cohort EEG study that has quantified neonatal sleep. SWC is clearly present immediately after birth, it is dominated by AS, and is influenced by mode of delivery. This knowledge of the early neonatal EEG/SWC can be used as reference data for EEG studies of neurologically compromised infants. Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Classification and regression tree analysis vs. multivariable linear and logistic regression methods as statistical tools for studying haemophilia.

PubMed

Henrard, S; Speybroeck, N; Hermans, C

2015-11-01

Haemophilia is a rare genetic haemorrhagic disease characterized by partial or complete deficiency of coagulation factor VIII, for haemophilia A, or IX, for haemophilia B. As in any other medical research domain, the field of haemophilia research is increasingly concerned with finding factors associated with binary or continuous outcomes through multivariable models. Traditional models include multiple logistic regressions, for binary outcomes, and multiple linear regressions for continuous outcomes. Yet these regression models are at times difficult to implement, especially for non-statisticians, and can be difficult to interpret. The present paper sought to didactically explain how, why, and when to use classification and regression tree (CART) analysis for haemophilia research. The CART method is non-parametric and non-linear, based on the repeated partitioning of a sample into subgroups based on a certain criterion. Breiman developed this method in 1984. Classification trees (CTs) are used to analyse categorical outcomes and regression trees (RTs) to analyse continuous ones. The CART methodology has become increasingly popular in the medical field, yet only a few examples of studies using this methodology specifically in haemophilia have to date been published. Two examples using CART analysis and previously published in this field are didactically explained in details. There is increasing interest in using CART analysis in the health domain, primarily due to its ease of implementation, use, and interpretation, thus facilitating medical decision-making. This method should be promoted for analysing continuous or categorical outcomes in haemophilia, when applicable. © 2015 John Wiley & Sons Ltd.

Transfer pricing in hospitals and efficiency of physicians: the case of anesthesia services.

PubMed

Kuntz, Ludwig; Vera, Antonio

2005-01-01

The objective is to investigate theoretically and empirically how the efficiency of the physicians involved in anesthesia and surgery can be optimized by the introduction of transfer pricing for anesthesia services. The anesthesiology data of approximately 57,000 operations carried out at the University Hospital Hamburg-Eppendorf (UKE) in Germany in the period from 2000 to 2002 are analyzed using parametric and non-parametric methods. The principal finding of the empirical analysis is that the efficiency of the physicians involved in anesthesia and surgery at the UKE improved after the introduction of transfer pricing.

Multi-response permutation procedure as an alternative to the analysis of variance: an SPSS implementation.

PubMed

Cai, Li

2006-02-01

A permutation test typically requires fewer assumptions than does a comparable parametric counterpart. The multi-response permutation procedure (MRPP) is a class of multivariate permutation tests of group difference useful for the analysis of experimental data. However, psychologists seldom make use of the MRPP in data analysis, in part because the MRPP is not implemented in popular statistical packages that psychologists use. A set of SPSS macros implementing the MRPP test is provided in this article. The use of the macros is illustrated by analyzing example data sets.

Effects of Inaccurate Identification of Interictal Epileptiform Discharges in Concurrent EEG-fMRI

NASA Astrophysics Data System (ADS)

Gkiatis, K.; Bromis, K.; Kakkos, I.; Karanasiou, I. S.; Matsopoulos, G. K.; Garganis, K.

2017-11-01

Concurrent continuous EEG-fMRI is a novel multimodal technique that is finding its way into clinical practice in epilepsy. EEG timeseries are used to identify the timing of interictal epileptiform discharges (IEDs) which is then included in a GLM analysis in fMRI to localize the epileptic onset zone. Nevertheless, there are still some concerns about its reliability concerning BOLD changes correlated with IEDs. Even though IEDs are identified by an experienced neurologist-epiliptologist, the reliability and concordance of the mark-ups is depending on many factors including the level of fatigue, the amount of time that he spent or, in some cases, even the screen that is being used for the display of timeseries. This investigation is aiming to unravel the effect of misidentification or inaccuracy in the mark-ups of IEDs in the fMRI statistical parametric maps. Concurrent EEG-fMRI was conducted in six subjects with various types of epilepsy. IEDs were identified by an experienced neurologist-epiliptologist. Analysis of EEG was performed with EEGLAB and analysis of fMRI was conducted in FSL. Preliminary results revealed lower statistical significance for missing events or larger period of IEDs than the actual ones and the introduction of false positives and false negatives in statistical parametric maps when random events were included in the GLM on top of the IEDs. Our results suggest that mark-ups in EEG for simultaneous EEG-fMRI should be done with caution from an experienced and restful neurologist as it affects the fMRI results in various and unpredicted ways.

The platelet activating factor acetyl hydrolase, oxidized low-density lipoprotein, paraoxonase 1 and arylesterase levels in treated and untreated patients with polycystic ovary syndrome.

PubMed

Carlioglu, Ayse; Kaygusuz, Ikbal; Karakurt, Feridun; Gumus, Ilknur Inegol; Uysal, Aysel; Kasapoglu, Benan; Armutcu, Ferah; Uysal, Sema; Keskin, Esra Aktepe; Koca, Cemile

2014-11-01

To evaluate the platelet activating factor acetyl hydrolyze (PAF-AH), oxidized low-density lipoprotein (ox-LDL), paraoxonase 1 (PON1), arylesterase (ARE) levels and the effects of metformin and Diane-35 (ethinyl oestradiol + cyproterone acetate) therapies on these parameters and to determine the PON1 polymorphisms among PCOS patients. Ninety patients with PCOS, age 30, and body mass index-matched healthy controls were included in the study. Patients were divided into three groups: metformin treatment, Diane-35 treatment and no medication groups. The treatment with metformin or Diane-35 was continued for 6 months and all subjects were evaluated with clinical and biochemical parameters 6 months later. One-way Anova test, t test and non-parametric Mann-Whitney U tests were used for statistical analysis. PAF-AH and ox-LDL levels were statistically significantly higher in untreated PCOS patients than controls, and they were statistically significantly lower in patients treated with metformin or Diane-35 than untreated PCOS patients. In contrast, there were lower PON1 (not statistically significant) and ARE (statistically significant) levels in untreated PCOS patients than the control group and they significantly increased after metformin and Diane-35 treatments. In PCOS patients serum PON1 levels for QQ, QR and RR phenotypes were statistically significantly lower than the control group. In patients with PCOS, proatherogenic markers increase. The treatment of PCOS with metformin or Diane-35 had positive effects on lipid profile, increased PON1 level, which is a protector from atherosclerosis and decreased the proatherogenic PAF-AH and ox-LDL levels.

Hyperbolic and semi-parametric models in finance

NASA Astrophysics Data System (ADS)

Bingham, N. H.; Kiesel, Rüdiger

2001-02-01

The benchmark Black-Scholes-Merton model of mathematical finance is parametric, based on the normal/Gaussian distribution. Its principal parametric competitor, the hyperbolic model of Barndorff-Nielsen, Eberlein and others, is briefly discussed. Our main theme is the use of semi-parametric models, incorporating the mean vector and covariance matrix as in the Markowitz approach, plus a non-parametric part, a scalar function incorporating features such as tail-decay. Implementation is also briefly discussed.

Temperature modifies the health effects of particulate matter in Brisbane, Australia

NASA Astrophysics Data System (ADS)

Ren, Cizao; Tong, Shilu

2006-11-01

A few epidemiological studies have examined whether there was an interactive effect between temperature and ambient particulate matter on cardiorespiratory morbidity and mortality, but the results were inconsistent. The present study used three time-series approaches to explore whether maximum temperature modified the impact of ambient particulate matter less than 10 μm in diameter (PM10) on daily respiratory hospital admissions, cardiovascular hospital admissions, respiratory emergency visits, cardiovascular emergency visits, non-external cause mortality and cardiovascular mortality in Brisbane between 1996 and 2001. The analytical approaches included a bivariate response surface model, a non-stratification parametric model and a stratification parametric model. Results show that there existed a statistically significant interaction between PM10 and temperature on most health outcomes at various lags. PM10 exhibited more adverse health effects on warm days than cold days. The choice of the degree of freedom for smoothers to adjust for confounders and the selection of arbitrary cut-offs for temperature affected the interaction estimates to a certain extent, but did not change the overall conclusion. The results imply that it is important to control and reduce the emission of air particles in Brisbane, particularly when temperature increases.

Observed changes in relative humidity and dew point temperature in coastal regions of Iran

NASA Astrophysics Data System (ADS)

Hosseinzadeh Talaee, P.; Sabziparvar, A. A.; Tabari, Hossein

2012-12-01

The analysis of trends in hydroclimatic parameters and assessment of their statistical significance have recently received a great concern to clarify whether or not there is an obvious climate change. In the current study, parametric linear regression and nonparametric Mann-Kendall tests were applied for detecting annual and seasonal trends in the relative humidity (RH) and dew point temperature ( T dew) time series at ten coastal weather stations in Iran during 1966-2005. The serial structure of the data was considered, and the significant serial correlations were eliminated using the trend-free pre-whitening method. The results showed that annual RH increased by 1.03 and 0.28 %/decade at the northern and southern coastal regions of the country, respectively, while annual T dew increased by 0.29 and 0.15°C per decade at the northern and southern regions, respectively. The significant trends were frequent in the T dew series, but they were observed only at 2 out of the 50 RH series. The results showed that the difference between the results of the parametric and nonparametric tests was small, although the parametric test detected larger significant trends in the RH and T dew time series. Furthermore, the differences between the results of the trend tests were not related to the normality of the statistical distribution.

SU-F-I-10: Spatially Local Statistics for Adaptive Image Filtering

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

Iliopoulos, AS; Sun, X; Floros, D

Purpose: To facilitate adaptive image filtering operations, addressing spatial variations in both noise and signal. Such issues are prevalent in cone-beam projections, where physical effects such as X-ray scattering result in spatially variant noise, violating common assumptions of homogeneous noise and challenging conventional filtering approaches to signal extraction and noise suppression. Methods: We present a computational mechanism for probing into and quantifying the spatial variance of noise throughout an image. The mechanism builds a pyramid of local statistics at multiple spatial scales; local statistical information at each scale includes (weighted) mean, median, standard deviation, median absolute deviation, as well asmore » histogram or dynamic range after local mean/median shifting. Based on inter-scale differences of local statistics, the spatial scope of distinguishable noise variation is detected in a semi- or un-supervised manner. Additionally, we propose and demonstrate the incorporation of such information in globally parametrized (i.e., non-adaptive) filters, effectively transforming the latter into spatially adaptive filters. The multi-scale mechanism is materialized by efficient algorithms and implemented in parallel CPU/GPU architectures. Results: We demonstrate the impact of local statistics for adaptive image processing and analysis using cone-beam projections of a Catphan phantom, fitted within an annulus to increase X-ray scattering. The effective spatial scope of local statistics calculations is shown to vary throughout the image domain, necessitating multi-scale noise and signal structure analysis. Filtering results with and without spatial filter adaptation are compared visually, illustrating improvements in imaging signal extraction and noise suppression, and in preserving information in low-contrast regions. Conclusion: Local image statistics can be incorporated in filtering operations to equip them with spatial adaptivity to spatial signal/noise variations. An efficient multi-scale computational mechanism is developed to curtail processing latency. Spatially adaptive filtering may impact subsequent processing tasks such as reconstruction and numerical gradient computations for deformable registration. NIH Grant No. R01-184173.« less

The Galker test of speech reception in noise; associations with background variables, middle ear status, hearing, and language in Danish preschool children.

PubMed

Lauritsen, Maj-Britt Glenn; Söderström, Margareta; Kreiner, Svend; Dørup, Jens; Lous, Jørgen

2016-01-01

We tested "the Galker test", a speech reception in noise test developed for primary care for Danish preschool children, to explore if the children's ability to hear and understand speech was associated with gender, age, middle ear status, and the level of background noise. The Galker test is a 35-item audio-visual, computerized word discrimination test in background noise. Included were 370 normally developed children attending day care center. The children were examined with the Galker test, tympanometry, audiometry, and the Reynell test of verbal comprehension. Parents and daycare teachers completed questionnaires on the children's ability to hear and understand speech. As most of the variables were not assessed using interval scales, non-parametric statistics (Goodman-Kruskal's gamma) were used for analyzing associations with the Galker test score. For comparisons, analysis of variance (ANOVA) was used. Interrelations were adjusted for using a non-parametric graphic model. In unadjusted analyses, the Galker test was associated with gender, age group, language development (Reynell revised scale), audiometry, and tympanometry. The Galker score was also associated with the parents' and day care teachers' reports on the children's vocabulary, sentence construction, and pronunciation. Type B tympanograms were associated with a mean hearing 5-6dB below that of than type A, C1, or C2. In the graphic analysis, Galker scores were closely and significantly related to Reynell test scores (Gamma (G)=0.35), the children's age group (G=0.33), and the day care teachers' assessment of the children's vocabulary (G=0.26). The Galker test of speech reception in noise appears promising as an easy and quick tool for evaluating preschool children's understanding of spoken words in noise, and it correlated well with the day care teachers' reports and less with the parents' reports. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Is the maturity of hospitals' quality improvement systems associated with measures of quality and patient safety?

PubMed Central

2011-01-01

Background Previous research addressed the development of a classification scheme for quality improvement systems in European hospitals. In this study we explore associations between the 'maturity' of the hospitals' quality improvement system and clinical outcomes. Methods The maturity classification scheme was developed based on survey results from 389 hospitals in eight European countries. We matched the hospitals from the Spanish sample (113 hospitals) with those hospitals participating in a nation-wide, voluntary hospital performance initiative. We then compared sample distributions and explored associations between the 'maturity' of the hospitals' quality improvement system and a range of composite outcomes measures, such as adjusted hospital-wide mortality, -readmission, -complication and -length of stay indices. Statistical analysis includes bivariate correlations for parametrically and non-parametrically distributed data, multiple robust regression models and bootstrapping techniques to obtain confidence-intervals for the correlation and regression estimates. Results Overall, 43 hospitals were included. Compared to the original sample of 113, this sample was characterized by a higher representation of university hospitals. Maturity of the quality improvement system was similar, although the matched sample showed less variability. Analysis of associations between the quality improvement system and hospital-wide outcomes suggests significant correlations for the indicator adjusted hospital complications, borderline significance for adjusted hospital readmissions and non-significance for the adjusted hospital mortality and length of stay indicators. These results are confirmed by the bootstrap estimates of the robust regression model after adjusting for hospital characteristics. Conclusions We assessed associations between hospitals' quality improvement systems and clinical outcomes. From this data it seems that having a more developed quality improvement system is associated with lower rates of adjusted hospital complications. A number of methodological and logistic hurdles remain to link hospital quality improvement systems to outcomes. Further research should aim at identifying the latent dimensions of quality improvement systems that predict quality and safety outcomes. Such research would add pertinent knowledge regarding the implementation of organizational strategies related with quality of care outcomes. PMID:22185479

Parametric excitation of tire-wheel assemblies by a stiffness non-uniformity

NASA Astrophysics Data System (ADS)

Stutts, D. S.; Krousgrill, C. M.; Soedel, W.

1995-01-01

A simple model of the effect of a concentrated radial stiffness non-uniformity in a passenger car tire is presented. The model treats the tread band of the tire as a rigid ring supported on a viscoelastic foundation. The distributed radial stiffness is lumped into equivalent horizontal (fore-and-aft) and vertical stiffnesses. The concentrated radial stiffness non-uniformity is modeled by treating the tread band as fixed, and the stiffness non-uniformity as rotating around it at the nominal angular velocity of the wheel. Due to loading, the center of mass of the tread band ring model is displaced upward with respect to the wheel spindle and, therefore, the rotating stiffness non-uniformity is alternately compressed and stretched through one complete rotation. This stretching and compressing of the stiffness non-uniformity results in force transmission to the wheel spindle at twice the nominal angular velocity in frequency, and therefore, would excite a given resonance at one-half the nominal angular wheel velocity that a mass unbalance would. The forcing produced by the stiffness non-uniformity is parametric in nature, thus creating the possibility of parametric resonance. The basic theory of the parametric resonance is explained, and a parameter study using derived lumped parameters based on a typical passenger car tire is performed. This study revealed that parametric resonance in passenger car tires, although possible, is unlikely at normal highway speeds as predicted by this model unless the tire is partially deflated.

Variability of rainfall over Lake Kariba catchment area in the Zambezi river basin, Zimbabwe

NASA Astrophysics Data System (ADS)

Muchuru, Shepherd; Botai, Joel O.; Botai, Christina M.; Landman, Willem A.; Adeola, Abiodun M.

2016-04-01

In this study, average monthly and annual rainfall totals recorded for the period 1970 to 2010 from a network of 13 stations across the Lake Kariba catchment area of the Zambezi river basin were analyzed in order to characterize the spatial-temporal variability of rainfall across the catchment area. In the analysis, the data were subjected to intervention and homogeneity analysis using the Cumulative Summation (CUSUM) technique and step change analysis using rank-sum test. Furthermore, rainfall variability was characterized by trend analysis using the non-parametric Mann-Kendall statistic. Additionally, the rainfall series were decomposed and the spectral characteristics derived using Cross Wavelet Transform (CWT) and Wavelet Coherence (WC) analysis. The advantage of using the wavelet-based parameters is that they vary in time and can therefore be used to quantitatively detect time-scale-dependent correlations and phase shifts between rainfall time series at various localized time-frequency scales. The annual and seasonal rainfall series were homogeneous and demonstrated no apparent significant shifts. According to the inhomogeneity classification, the rainfall series recorded across the Lake Kariba catchment area belonged to category A (useful) and B (doubtful), i.e., there were zero to one and two absolute tests rejecting the null hypothesis (at 5 % significance level), respectively. Lastly, the long-term variability of the rainfall series across the Lake Kariba catchment area exhibited non-significant positive and negative trends with coherent oscillatory modes that are constantly locked in phase in the Morlet wavelet space.

Full immersion simulation: validation of a distributed simulation environment for technical and non-technical skills training in Urology.

PubMed

Brewin, James; Tang, Jessica; Dasgupta, Prokar; Khan, Muhammad S; Ahmed, Kamran; Bello, Fernando; Kneebone, Roger; Jaye, Peter

2015-07-01

To evaluate the face, content and construct validity of the distributed simulation (DS) environment for technical and non-technical skills training in endourology. To evaluate the educational impact of DS for urology training. DS offers a portable, low-cost simulated operating room environment that can be set up in any open space. A prospective mixed methods design using established validation methodology was conducted in this simulated environment with 10 experienced and 10 trainee urologists. All participants performed a simulated prostate resection in the DS environment. Outcome measures included surveys to evaluate the DS, as well as comparative analyses of experienced and trainee urologist's performance using real-time and 'blinded' video analysis and validated performance metrics. Non-parametric statistical methods were used to compare differences between groups. The DS environment demonstrated face, content and construct validity for both non-technical and technical skills. Kirkpatrick level 1 evidence for the educational impact of the DS environment was shown. Further studies are needed to evaluate the effect of simulated operating room training on real operating room performance. This study has shown the validity of the DS environment for non-technical, as well as technical skills training. DS-based simulation appears to be a valuable addition to traditional classroom-based simulation training. © 2014 The Authors BJU International © 2014 BJU International Published by John Wiley & Sons Ltd.

Keeping nurses at work: a duration analysis.

PubMed

Holmås, Tor Helge

2002-09-01

A shortage of nurses is currently a problem in several countries, and an important question is therefore how one can increase the supply of nursing labour. In this paper, we focus on the issue of nurses leaving the public health sector by utilising a unique data set containing information on both the supply and demand side of the market. To describe the exit rate from the health sector we apply a semi-parametric hazard rate model. In the estimations, we correct for unobserved heterogeneity by both a parametric (Gamma) and a non-parametric approach. We find that both wages and working conditions have an impact on nurses' decision to quit. Furthermore, failing to correct for the fact that nurses' income partly consists of compensation for inconvenient working hours results in a considerable downward bias of the wage effect. Copyright 2002 John Wiley & Sons, Ltd.

Biomarker analysis of American toad (Anaxyrus americanus) ...

EPA Pesticide Factsheets

The objective of the current study was to use a biomarker-based approach to investigate the influence of atrazine exposure on American toad (Anaxyrus americanus) and grey tree frog (Hyla versicolor) tadpoles. Atrazine is one of the most frequently detected herbicides in environmental matrices throughout the United States. In surface waters, it has been found at concentrations from 0.04–2859 μg/L and thus presents a likely exposure scenario for non-target species such as amphibians. Studies have examined the effect of atrazine on the metamorphic parameters of amphibians, however, the data are often contradictory. Gosner stage 22–24 tadpoles were exposed to 0 (control), 10, 50, 250 or 1250 μg/L of atrazine for 48 h. Endogenous polar metabolites were extracted and analyzed using gas chromatography coupled with mass spectrometry. Statistical analyses of the acquired spectra with machine learning classification models demonstrated identifiable changes in the metabolomic profiles between exposed and control tadpoles. Support vector machine models with recursive feature elimination created a more efficient, non-parametric data analysis and increased interpretability of metabolomic profiles. Biochemical fluxes observed in the exposed groups of both A. americanus and H. versicolor displayed perturbations in a number of classes of biological macromolecules including fatty acids, amino acids, purine nucleosides, pyrimidines, and mono- and di-saccharides. Metabolomic

Levels of heavy metals and essential minerals in hair samples of children with autism in Oman: a case-control study.

PubMed

Al-Farsi, Yahya M; Waly, Mostafa I; Al-Sharbati, Marwan M; Al-Shafaee, Mohammed A; Al-Farsi, Omar A; Al-Khaduri, Maha M; Gupta, Ishita; Ouhtit, Allal; Al-Adawi, Samir; Al-Said, Mona F; Deth, Richard C

2013-02-01

Toxic levels of heavy metals and low levels of essential minerals have been suggested to play a critical role in the pathogenesis of autism spectrum disorders (ASD). This study documents the levels of heavy metals and essential minerals in hair samples of children with ASD in Muscat, the urbanized capital of Oman, Muscat. The study included 27 children with ASD and 27 matched non-ASD controls. Parental interviews were held and dietary intake questionnaires completed in conjunction with the collection of hair samples. Analysis of heavy metals and essential minerals was carried out by inductively coupled plasma mass spectrometry. Chi-square analysis and non-parametric Fisher's exact tests were used to assess statistical significance. Children with ASD had significantly higher levels of all 11 analyzed heavy metals in their hair samples (P < 0.05), ranging from 150 to 365 % of control levels. ASD children also had significantly higher levels of essential minerals sulfur, sodium, magnesium, potassium, zinc, and iron, but lower levels of calcium and copper in their hair samples. This study corroborates data from previous studies in different parts of the world indicating the presence of elevated levels of heavy metals and selective depletion of essential minerals in the hair of children with ASD.

Combining Shapley value and statistics to the analysis of gene expression data in children exposed to air pollution

PubMed Central

Moretti, Stefano; van Leeuwen, Danitsja; Gmuender, Hans; Bonassi, Stefano; van Delft, Joost; Kleinjans, Jos; Patrone, Fioravante; Merlo, Domenico Franco

2008-01-01

Background In gene expression analysis, statistical tests for differential gene expression provide lists of candidate genes having, individually, a sufficiently low p-value. However, the interpretation of each single p-value within complex systems involving several interacting genes is problematic. In parallel, in the last sixty years, game theory has been applied to political and social problems to assess the power of interacting agents in forcing a decision and, more recently, to represent the relevance of genes in response to certain conditions. Results In this paper we introduce a Bootstrap procedure to test the null hypothesis that each gene has the same relevance between two conditions, where the relevance is represented by the Shapley value of a particular coalitional game defined on a microarray data-set. This method, which is called Comparative Analysis of Shapley value (shortly, CASh), is applied to data concerning the gene expression in children differentially exposed to air pollution. The results provided by CASh are compared with the results from a parametric statistical test for testing differential gene expression. Both lists of genes provided by CASh and t-test are informative enough to discriminate exposed subjects on the basis of their gene expression profiles. While many genes are selected in common by CASh and the parametric test, it turns out that the biological interpretation of the differences between these two selections is more interesting, suggesting a different interpretation of the main biological pathways in gene expression regulation for exposed individuals. A simulation study suggests that CASh offers more power than t-test for the detection of differential gene expression variability. Conclusion CASh is successfully applied to gene expression analysis of a data-set where the joint expression behavior of genes may be critical to characterize the expression response to air pollution. We demonstrate a synergistic effect between coalitional games and statistics that resulted in a selection of genes with a potential impact in the regulation of complex pathways. PMID:18764936

Characterizing Heterogeneity within Head and Neck Lesions Using Cluster Analysis of Multi-Parametric MRI Data

PubMed Central

Borri, Marco; Schmidt, Maria A.; Powell, Ceri; Koh, Dow-Mu; Riddell, Angela M.; Partridge, Mike; Bhide, Shreerang A.; Nutting, Christopher M.; Harrington, Kevin J.; Newbold, Katie L.; Leach, Martin O.

2015-01-01

Purpose To describe a methodology, based on cluster analysis, to partition multi-parametric functional imaging data into groups (or clusters) of similar functional characteristics, with the aim of characterizing functional heterogeneity within head and neck tumour volumes. To evaluate the performance of the proposed approach on a set of longitudinal MRI data, analysing the evolution of the obtained sub-sets with treatment. Material and Methods The cluster analysis workflow was applied to a combination of dynamic contrast-enhanced and diffusion-weighted imaging MRI data from a cohort of squamous cell carcinoma of the head and neck patients. Cumulative distributions of voxels, containing pre and post-treatment data and including both primary tumours and lymph nodes, were partitioned into k clusters (k = 2, 3 or 4). Principal component analysis and cluster validation were employed to investigate data composition and to independently determine the optimal number of clusters. The evolution of the resulting sub-regions with induction chemotherapy treatment was assessed relative to the number of clusters. Results The clustering algorithm was able to separate clusters which significantly reduced in voxel number following induction chemotherapy from clusters with a non-significant reduction. Partitioning with the optimal number of clusters (k = 4), determined with cluster validation, produced the best separation between reducing and non-reducing clusters. Conclusion The proposed methodology was able to identify tumour sub-regions with distinct functional properties, independently separating clusters which were affected differently by treatment. This work demonstrates that unsupervised cluster analysis, with no prior knowledge of the data, can be employed to provide a multi-parametric characterization of functional heterogeneity within tumour volumes. PMID:26398888

The Efficiency Change of Italian Public Universities in the New Millennium: A Non-Parametric Analysis

ERIC Educational Resources Information Center

Guccio, Calogero; Martorana, Marco Ferdinando; Mazza, Isidoro

2017-01-01

The paper assesses the evolution of efficiency of Italian public universities for the period 2000-2010. It aims at investigating whether their levels of efficiency showed signs of convergence, and if the well-known disparity between northern and southern regions decreased. For this purpose, we use a refinement of data envelopment analysis, namely…

Genome-wide analysis of genetic susceptibility to language impairment in an isolated Chilean population

PubMed Central

Villanueva, Pia; Newbury, Dianne F; Jara, Lilian; De Barbieri, Zulema; Mirza, Ghazala; Palomino, Hernán M; Fernández, María Angélica; Cazier, Jean-Baptiste; Monaco, Anthony P; Palomino, Hernán

2011-01-01

Specific language impairment (SLI) is an unexpected deficit in the acquisition of language skills and affects between 5 and 8% of pre-school children. Despite its prevalence and high heritability, our understanding of the aetiology of this disorder is only emerging. In this paper, we apply genome-wide techniques to investigate an isolated Chilean population who exhibit an increased frequency of SLI. Loss of heterozygosity (LOH) mapping and parametric and non-parametric linkage analyses indicate that complex genetic factors are likely to underlie susceptibility to SLI in this population. Across all analyses performed, the most consistently implicated locus was on chromosome 7q. This locus achieved highly significant linkage under all three non-parametric models (max NPL=6.73, P=4.0 × 10−11). In addition, it yielded a HLOD of 1.24 in the recessive parametric linkage analyses and contained a segment that was homozygous in two affected individuals. Further, investigation of this region identified a two-SNP haplotype that occurs at an increased frequency in language-impaired individuals (P=0.008). We hypothesise that the linkage regions identified here, in particular that on chromosome 7, may contain variants that underlie the high prevalence of SLI observed in this isolated population and may be of relevance to other populations affected by language impairments. PMID:21248734

Parametric and Non-Parametric Vibration-Based Structural Identification Under Earthquake Excitation

NASA Astrophysics Data System (ADS)

Pentaris, Fragkiskos P.; Fouskitakis, George N.

2014-05-01

The problem of modal identification in civil structures is of crucial importance, and thus has been receiving increasing attention in recent years. Vibration-based methods are quite promising as they are capable of identifying the structure's global characteristics, they are relatively easy to implement and they tend to be time effective and less expensive than most alternatives [1]. This paper focuses on the off-line structural/modal identification of civil (concrete) structures subjected to low-level earthquake excitations, under which, they remain within their linear operating regime. Earthquakes and their details are recorded and provided by the seismological network of Crete [2], which 'monitors' the broad region of south Hellenic arc, an active seismic region which functions as a natural laboratory for earthquake engineering of this kind. A sufficient number of seismic events are analyzed in order to reveal the modal characteristics of the structures under study, that consist of the two concrete buildings of the School of Applied Sciences, Technological Education Institute of Crete, located in Chania, Crete, Hellas. Both buildings are equipped with high-sensitivity and accuracy seismographs - providing acceleration measurements - established at the basement (structure's foundation) presently considered as the ground's acceleration (excitation) and at all levels (ground floor, 1st floor, 2nd floor and terrace). Further details regarding the instrumentation setup and data acquisition may be found in [3]. The present study invokes stochastic, both non-parametric (frequency-based) and parametric methods for structural/modal identification (natural frequencies and/or damping ratios). Non-parametric methods include Welch-based spectrum and Frequency response Function (FrF) estimation, while parametric methods, include AutoRegressive (AR), AutoRegressive with eXogeneous input (ARX) and Autoregressive Moving-Average with eXogeneous input (ARMAX) models[4, 5]. Preliminary results indicate that parametric methods are capable of sufficiently providing the structural/modal characteristics such as natural frequencies and damping ratios. The study also aims - at a further level of investigation - to provide a reliable statistically-based methodology for structural health monitoring after major seismic events which potentially cause harming consequences in structures. Acknowledgments This work was supported by the State Scholarships Foundation of Hellas. References [1] J. S. Sakellariou and S. D. Fassois, "Stochastic output error vibration-based damage detection and assessment in structures under earthquake excitation," Journal of Sound and Vibration, vol. 297, pp. 1048-1067, 2006. [2] G. Hloupis, I. Papadopoulos, J. P. Makris, and F. Vallianatos, "The South Aegean seismological network - HSNC," Adv. Geosci., vol. 34, pp. 15-21, 2013. [3] F. P. Pentaris, J. Stonham, and J. P. Makris, "A review of the state-of-the-art of wireless SHM systems and an experimental set-up towards an improved design," presented at the EUROCON, 2013 IEEE, Zagreb, 2013. [4] S. D. Fassois, "Parametric Identification of Vibrating Structures," in Encyclopedia of Vibration, S. G. Braun, D. J. Ewins, and S. S. Rao, Eds., ed London: Academic Press, London, 2001. [5] S. D. Fassois and J. S. Sakellariou, "Time-series methods for fault detection and identification in vibrating structures," Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 365, pp. 411-448, February 15 2007.

K2 and K2*: efficient alignment-free sequence similarity measurement based on Kendall statistics.

PubMed

Lin, Jie; Adjeroh, Donald A; Jiang, Bing-Hua; Jiang, Yue

2018-05-15

Alignment-free sequence comparison methods can compute the pairwise similarity between a huge number of sequences much faster than sequence-alignment based methods. We propose a new non-parametric alignment-free sequence comparison method, called K2, based on the Kendall statistics. Comparing to the other state-of-the-art alignment-free comparison methods, K2 demonstrates competitive performance in generating the phylogenetic tree, in evaluating functionally related regulatory sequences, and in computing the edit distance (similarity/dissimilarity) between sequences. Furthermore, the K2 approach is much faster than the other methods. An improved method, K2*, is also proposed, which is able to determine the appropriate algorithmic parameter (length) automatically, without first considering different values. Comparative analysis with the state-of-the-art alignment-free sequence similarity methods demonstrates the superiority of the proposed approaches, especially with increasing sequence length, or increasing dataset sizes. The K2 and K2* approaches are implemented in the R language as a package and is freely available for open access (http://community.wvu.edu/daadjeroh/projects/K2/K2_1.0.tar.gz). yueljiang@163.com. Supplementary data are available at Bioinformatics online.

Analysis of data on large explosive eruptions of stratovolcanoes to constrain under-recording and eruption rates

NASA Astrophysics Data System (ADS)

Rougier, Jonty; Cashman, Kathy; Sparks, Stephen

2016-04-01

We have analysed the Large Magnitude Explosive Volcanic Eruptions database (LaMEVE) for volcanoes that classify as stratovolcanoes. A non-parametric statistical approach is used to assess the global recording rate for large (M4+). The approach imposes minimal structure on the shape of the recording rate through time. We find that the recording rates have declined rapidly, going backwards in time. Prior to 1600 they are below 50%, and prior to 1100 they are below 20%. Even in the recent past, e.g. the 1800s, they are likely to be appreciably less than 100%.The assessment for very large (M5+) eruptions is more uncertain, due to the scarcity of events. Having taken under-recording into account the large-eruption rates of stratovolcanoes are modelled exchangeably, in order to derive an informative prior distribution as an input into a subsequent volcano-by-volcano hazard assessment. The statistical model implies that volcano-by-volcano predictions can be grouped by the number of recorded large eruptions. Further, it is possible to combine all volcanoes together into a global large eruption prediction, with an M4+ rate computed from the LaMEVE database of 0.57/yr.

Microbial contamination and disinfection methods of pacifiers.

PubMed

Nelson-Filho, Paulo; Louvain, Márcia Costa; Macari, Soraia; Lucisano, Marília Pacífico; Silva, Raquel Assed Bezerra da; Queiroz, Alexandra Mussolino de; Gaton-Hernández, Patrícia; Silva, Léa Assed Bezerra da

2015-10-01

To evaluate the microbial contamination of pacifiers by Mutans Streptococci(MS) and the efficacy of different methods for their disinfection. Twenty-eight children were assigned to a 4-stage changeover system with a 1-week interval. In each stage, children received a new pacifier and the parents were instructed to maintain their normal habits for 1 week. After this time, the pacifiers were subjected to the following 4 disinfection methods: spraying with 0.12% chlorhexidine solution, Brushtox or sterile tap water, and immersion in boiling tap water for 15 minutes. Microbiological culture for MS and Scanning Electron Microscopy (SEM) were performed. The results were analyzed statistically by Friedman's non-parametric test (a=0.05). The 0.12% chlorhexidine spray was statistically similar to the boiling water (p>0.05) and more effective than the Brushtox spray and control (p<0.05). The analysis of SEM showed the formation of a cariogenic biofilm in all groups with positive culture. Pacifiers become contaminated by MS after their use by children and should be disinfected routinely. Spraying with a 0.12% chlorhexidine solution and immersion in boiling water promoted better disinfection of the pacifiers compared with a commercial antiseptic toothbrush cleanser (Brushtox).

Influence of concentration, time and method of application of citric acid and sodium citrate in root conditioning

PubMed Central

CAVASSIM, Rodrigo; LEITE, Fábio Renato Manzolli; ZANDIM, Daniela Leal; DANTAS, Andrea Abi Rached; RACHED, Ricardo Samih Georges Abi; SAMPAIO, José Eduardo Cezar

2012-01-01

Objective The aim of this study was to establish the parameters of concentration, time and mode of application of citric acid and sodium citrate in relation to root conditioning. Material and Methods A total of 495 samples were obtained and equally distributed among 11 groups (5 for testing different concentrations of citric acid, 5 for testing different concentrations of sodium citrate and 1 control group). After laboratorial processing, the samples were analyzed under scanning electron microscopy. A previously calibrated and blind examiner evaluated micrographs of the samples. Non-parametric statistical analysis was performed to analyze the data obtained. Results Brushing 25% citric acid for 3 min, promoted greater exposure of collagen fibers in comparison with the brushing of 1% citric acid for 1 minute and its topical application at 1% for 3 min. Sodium citrate exposed collagen fibers in a few number of samples. Conclusion Despite the lack of statistical significance, better results for collagen exposure were obtained with brushing application of 25% citric acid for 3 min than with other application parameter. Sodium citrate produced a few number of samples with collagen exposure, so it is not indicated for root conditioning. PMID:22858707

Quantitative analysis of diffusion tensor imaging (DTI) using statistical parametric mapping (SPM) for brain disorders

NASA Astrophysics Data System (ADS)

Lee, Jae-Seung; Im, In-Chul; Kang, Su-Man; Goo, Eun-Hoe; Kwak, Byung-Joon

2013-07-01

This study aimed to quantitatively analyze data from diffusion tensor imaging (DTI) using statistical parametric mapping (SPM) in patients with brain disorders and to assess its potential utility for analyzing brain function. DTI was obtained by performing 3.0-T magnetic resonance imaging for patients with Alzheimer's disease (AD) and vascular dementia (VD), and the data were analyzed using Matlab-based SPM software. The two-sample t-test was used for error analysis of the location of the activated pixels. We compared regions of white matter where the fractional anisotropy (FA) values were low and the apparent diffusion coefficients (ADCs) were increased. In the AD group, the FA values were low in the right superior temporal gyrus, right inferior temporal gyrus, right sub-lobar insula, and right occipital lingual gyrus whereas the ADCs were significantly increased in the right inferior frontal gyrus and right middle frontal gyrus. In the VD group, the FA values were low in the right superior temporal gyrus, right inferior temporal gyrus, right limbic cingulate gyrus, and right sub-lobar caudate tail whereas the ADCs were significantly increased in the left lateral globus pallidus and left medial globus pallidus. In conclusion by using DTI and SPM analysis, we were able to not only determine the structural state of the regions affected by brain disorders but also quantitatively analyze and assess brain function.

The analysis of incontinence episodes and other count data in patients with overactive bladder by Poisson and negative binomial regression.

PubMed

Martina, R; Kay, R; van Maanen, R; Ridder, A

2015-01-01

Clinical studies in overactive bladder have traditionally used analysis of covariance or nonparametric methods to analyse the number of incontinence episodes and other count data. It is known that if the underlying distributional assumptions of a particular parametric method do not hold, an alternative parametric method may be more efficient than a nonparametric one, which makes no assumptions regarding the underlying distribution of the data. Therefore, there are advantages in using methods based on the Poisson distribution or extensions of that method, which incorporate specific features that provide a modelling framework for count data. One challenge with count data is overdispersion, but methods are available that can account for this through the introduction of random effect terms in the modelling, and it is this modelling framework that leads to the negative binomial distribution. These models can also provide clinicians with a clearer and more appropriate interpretation of treatment effects in terms of rate ratios. In this paper, the previously used parametric and non-parametric approaches are contrasted with those based on Poisson regression and various extensions in trials evaluating solifenacin and mirabegron in patients with overactive bladder. In these applications, negative binomial models are seen to fit the data well. Copyright © 2014 John Wiley & Sons, Ltd.

A nonparametric mean-variance smoothing method to assess Arabidopsis cold stress transcriptional regulator CBF2 overexpression microarray data.

PubMed

Hu, Pingsha; Maiti, Tapabrata

2011-01-01

Microarray is a powerful tool for genome-wide gene expression analysis. In microarray expression data, often mean and variance have certain relationships. We present a non-parametric mean-variance smoothing method (NPMVS) to analyze differentially expressed genes. In this method, a nonlinear smoothing curve is fitted to estimate the relationship between mean and variance. Inference is then made upon shrinkage estimation of posterior means assuming variances are known. Different methods have been applied to simulated datasets, in which a variety of mean and variance relationships were imposed. The simulation study showed that NPMVS outperformed the other two popular shrinkage estimation methods in some mean-variance relationships; and NPMVS was competitive with the two methods in other relationships. A real biological dataset, in which a cold stress transcription factor gene, CBF2, was overexpressed, has also been analyzed with the three methods. Gene ontology and cis-element analysis showed that NPMVS identified more cold and stress responsive genes than the other two methods did. The good performance of NPMVS is mainly due to its shrinkage estimation for both means and variances. In addition, NPMVS exploits a non-parametric regression between mean and variance, instead of assuming a specific parametric relationship between mean and variance. The source code written in R is available from the authors on request.

A Nonparametric Mean-Variance Smoothing Method to Assess Arabidopsis Cold Stress Transcriptional Regulator CBF2 Overexpression Microarray Data

PubMed Central

Hu, Pingsha; Maiti, Tapabrata

2011-01-01

Microarray is a powerful tool for genome-wide gene expression analysis. In microarray expression data, often mean and variance have certain relationships. We present a non-parametric mean-variance smoothing method (NPMVS) to analyze differentially expressed genes. In this method, a nonlinear smoothing curve is fitted to estimate the relationship between mean and variance. Inference is then made upon shrinkage estimation of posterior means assuming variances are known. Different methods have been applied to simulated datasets, in which a variety of mean and variance relationships were imposed. The simulation study showed that NPMVS outperformed the other two popular shrinkage estimation methods in some mean-variance relationships; and NPMVS was competitive with the two methods in other relationships. A real biological dataset, in which a cold stress transcription factor gene, CBF2, was overexpressed, has also been analyzed with the three methods. Gene ontology and cis-element analysis showed that NPMVS identified more cold and stress responsive genes than the other two methods did. The good performance of NPMVS is mainly due to its shrinkage estimation for both means and variances. In addition, NPMVS exploits a non-parametric regression between mean and variance, instead of assuming a specific parametric relationship between mean and variance. The source code written in R is available from the authors on request. PMID:21611181

Enhanced multi-protocol analysis via intelligent supervised embedding (EMPrAvISE): detecting prostate cancer on multi-parametric MRI

NASA Astrophysics Data System (ADS)

Viswanath, Satish; Bloch, B. Nicholas; Chappelow, Jonathan; Patel, Pratik; Rofsky, Neil; Lenkinski, Robert; Genega, Elizabeth; Madabhushi, Anant

2011-03-01

Currently, there is significant interest in developing methods for quantitative integration of multi-parametric (structural, functional) imaging data with the objective of building automated meta-classifiers to improve disease detection, diagnosis, and prognosis. Such techniques are required to address the differences in dimensionalities and scales of individual protocols, while deriving an integrated multi-parametric data representation which best captures all disease-pertinent information available. In this paper, we present a scheme called Enhanced Multi-Protocol Analysis via Intelligent Supervised Embedding (EMPrAvISE); a powerful, generalizable framework applicable to a variety of domains for multi-parametric data representation and fusion. Our scheme utilizes an ensemble of embeddings (via dimensionality reduction, DR); thereby exploiting the variance amongst multiple uncorrelated embeddings in a manner similar to ensemble classifier schemes (e.g. Bagging, Boosting). We apply this framework to the problem of prostate cancer (CaP) detection on 12 3 Tesla pre-operative in vivo multi-parametric (T2-weighted, Dynamic Contrast Enhanced, and Diffusion-weighted) magnetic resonance imaging (MRI) studies, in turn comprising a total of 39 2D planar MR images. We first align the different imaging protocols via automated image registration, followed by quantification of image attributes from individual protocols. Multiple embeddings are generated from the resultant high-dimensional feature space which are then combined intelligently to yield a single stable solution. Our scheme is employed in conjunction with graph embedding (for DR) and probabilistic boosting trees (PBTs) to detect CaP on multi-parametric MRI. Finally, a probabilistic pairwise Markov Random Field algorithm is used to apply spatial constraints to the result of the PBT classifier, yielding a per-voxel classification of CaP presence. Per-voxel evaluation of detection results against ground truth for CaP extent on MRI (obtained by spatially registering pre-operative MRI with available whole-mount histological specimens) reveals that EMPrAvISE yields a statistically significant improvement (AUC=0.77) over classifiers constructed from individual protocols (AUC=0.62, 0.62, 0.65, for T2w, DCE, DWI respectively) as well as one trained using multi-parametric feature concatenation (AUC=0.67).

Intraocular pressure and pulsatile ocular blood flow after retrobulbar and peribulbar anaesthesia

PubMed Central

Watkins, R.; Beigi, B.; Yates, M.; Chang, B.; Linardos, E.

2001-01-01

AIMS—This study investigated the effect of peribulbar and retrobulbar local anaesthesia on intraocular pressure (IOP) and pulsatile ocular blood flow (POBF), as such anaesthetic techniques may adversely affect these parameters.
METHODS—20 eyes of 20 patients who were to undergo phacoemulsification cataract surgery were prospectively randomised to receive peribulbar or retrobulbar anaesthesia. The OBF tonometer (OBF Labs, Wiltshire, UK) was used to simultaneously measure IOP and POBF before anaesthesia and 1 minute and 10 minutes after anaesthesia. Between group comparisons of age, baseline IOP, and baseline POBF were performed using the non-parametric Mann-Whitney test. Within group comparisons of IOP and POBF measured preanaesthesia and post-anaesthesia were performed using the non-parametric Wilcoxon signed ranks test for both groups.
RESULTS—There was no statistically significant IOP increase post-anaesthesia in either group. In the group receiving peribulbar anaesthesia, there was a significant reduction in POBF initially post-anaesthesia which recovered after 10 minutes. In the group receiving retrobulbar anaesthesia, there was a persistent statistically significant reduction in POBF.
CONCLUSIONS—Retrobulbar and peribulbar injections have little effect on IOP. Ocular compression is not needed for IOP reduction when using local anaesthesia for cataract surgery. Conversely, POBF falls, at least for a short time, when anaesthesia for ophthalmic surgery is administered via a retrobulbar route or a peribulbar route. This reduction may be mediated by pharmacologically altered orbital vascular tone. It may be safer to use other anaesthetic techniques in patients with ocular vascular compromise.

 PMID:11423451

Cross-validation and Peeling Strategies for Survival Bump Hunting using Recursive Peeling Methods

PubMed Central

Dazard, Jean-Eudes; Choe, Michael; LeBlanc, Michael; Rao, J. Sunil

2015-01-01

We introduce a framework to build a survival/risk bump hunting model with a censored time-to-event response. Our Survival Bump Hunting (SBH) method is based on a recursive peeling procedure that uses a specific survival peeling criterion derived from non/semi-parametric statistics such as the hazards-ratio, the log-rank test or the Nelson--Aalen estimator. To optimize the tuning parameter of the model and validate it, we introduce an objective function based on survival or prediction-error statistics, such as the log-rank test and the concordance error rate. We also describe two alternative cross-validation techniques adapted to the joint task of decision-rule making by recursive peeling and survival estimation. Numerical analyses show the importance of replicated cross-validation and the differences between criteria and techniques in both low and high-dimensional settings. Although several non-parametric survival models exist, none addresses the problem of directly identifying local extrema. We show how SBH efficiently estimates extreme survival/risk subgroups unlike other models. This provides an insight into the behavior of commonly used models and suggests alternatives to be adopted in practice. Finally, our SBH framework was applied to a clinical dataset. In it, we identified subsets of patients characterized by clinical and demographic covariates with a distinct extreme survival outcome, for which tailored medical interventions could be made. An R package PRIMsrc (Patient Rule Induction Method in Survival, Regression and Classification settings) is available on CRAN (Comprehensive R Archive Network) and GitHub. PMID:27034730

An assessment on the trustworthiness of engineers in higher tertiary institutions

NASA Astrophysics Data System (ADS)

Ooi Kuan, Tan; Lloyd, Ling; Mou Chuan, Cheng

2017-10-01

In Malaysian higher education history, the evolution from public funded to private funded and now to private non profit oriented model has been taking place since 80s. The evolution also demarcated higher learning institution into academic or research based university. As such, postgraduate studies became increasingly competitive in students intake. The evolution also created doubt to the public in term of the quality of postgraduate education offered by different classifications. This study investigates the gender specific perception and trustworthiness of engineering postgraduate students in private non profit oriented higher tertiary institution. An equally divided gender groups of 118 respondents were chosen for the study. Non-parametric statistics were used and the result showed that there was no difference and no correlation of genders in perception on teaching role and trustworthiness among the future engineers in private non profit oriented higher tertiary institution.

Pooling sexes when assessing ground reaction forces during walking: Statistical Parametric Mapping versus traditional approach.

PubMed

Castro, Marcelo P; Pataky, Todd C; Sole, Gisela; Vilas-Boas, Joao Paulo

2015-07-16

Ground reaction force (GRF) data from men and women are commonly pooled for analyses. However, it may not be justifiable to pool sexes on the basis of discrete parameters extracted from continuous GRF gait waveforms because this can miss continuous effects. Forty healthy participants (20 men and 20 women) walked at a cadence of 100 steps per minute across two force plates, recording GRFs. Two statistical methods were used to test the null hypothesis of no mean GRF differences between sexes: (i) Statistical Parametric Mapping-using the entire three-component GRF waveform; and (ii) traditional approach-using the first and second vertical GRF peaks. Statistical Parametric Mapping results suggested large sex differences, which post-hoc analyses suggested were due predominantly to higher anterior-posterior and vertical GRFs in early stance in women compared to men. Statistically significant differences were observed for the first GRF peak and similar values for the second GRF peak. These contrasting results emphasise that different parts of the waveform have different signal strengths and thus that one may use the traditional approach to choose arbitrary metrics and make arbitrary conclusions. We suggest that researchers and clinicians consider both the entire gait waveforms and sex-specificity when analysing GRF data. Copyright © 2015 Elsevier Ltd. All rights reserved.

Parametrization study of the land multiparameter VTI elastic waveform inversion

NASA Astrophysics Data System (ADS)

He, W.; Plessix, R.-É.; Singh, S.

2018-06-01

Multiparameter inversion of seismic data remains challenging due to the trade-off between the different elastic parameters and the non-uniqueness of the solution. The sensitivity of the seismic data to a given subsurface elastic parameter depends on the source and receiver ray/wave path orientations at the subsurface point. In a high-frequency approximation, this is commonly analysed through the study of the radiation patterns that indicate the sensitivity of each parameter versus the incoming (from the source) and outgoing (to the receiver) angles. In practice, this means that the inversion result becomes sensitive to the choice of parametrization, notably because the null-space of the inversion depends on this choice. We can use a least-overlapping parametrization that minimizes the overlaps between the radiation patterns, in this case each parameter is only sensitive in a restricted angle domain, or an overlapping parametrization that contains a parameter sensitive to all angles, in this case overlaps between the radiation parameters occur. Considering a multiparameter inversion in an elastic vertically transverse isotropic medium and a complex land geological setting, we show that the inversion with the least-overlapping parametrization gives less satisfactory results than with the overlapping parametrization. The difficulties come from the complex wave paths that make difficult to predict the areas of sensitivity of each parameter. This shows that the parametrization choice should not only be based on the radiation pattern analysis but also on the angular coverage at each subsurface point that depends on geology and the acquisition layout.

Crash Lethality Model

DTIC Science & Technology

2012-06-06

Statistical Data ........................................................................................... 45 31 Parametric Model for Rotor Wing Debris...Area .............................................................. 46 32 Skid Distance Statistical Data...results. The curve that related the BC value to the probability of skull fracture resulted in a tight confidence interval and a two tailed statistical p

The influence of linear elements on plant species diversity of Mediterranean rural landscapes: assessment of different indices and statistical approaches.

PubMed

García del Barrio, J M; Ortega, M; Vázquez De la Cueva, A; Elena-Rosselló, R

2006-08-01

This paper mainly aims to study the linear element influence on the estimation of vascular plant species diversity in five Mediterranean landscapes modeled as land cover patch mosaics. These landscapes have several core habitats and a different set of linear elements--habitat edges or ecotones, roads or railways, rivers, streams and hedgerows on farm land--whose plant composition were examined. Secondly, it aims to check plant diversity estimation in Mediterranean landscapes using parametric and non-parametric procedures, with two indices: Species richness and Shannon index. Land cover types and landscape linear elements were identified from aerial photographs. Their spatial information was processed using GIS techniques. Field plots were selected using a stratified sampling design according to relieve and tree density of each habitat type. A 50x20 m2 multi-scale sampling plot was designed for the core habitats and across the main landscape linear elements. Richness and diversity of plant species were estimated by comparing the observed field data to ICE (Incidence-based Coverage Estimator) and ACE (Abundance-based Coverage Estimator) non-parametric estimators. The species density, percentage of unique species, and alpha diversity per plot were significantly higher (p < 0.05) in linear elements than in core habitats. ICE estimate of number of species was 32% higher than of ACE estimate, which did not differ significantly from the observed values. Accumulated species richness in core habitats together with linear elements, were significantly higher than those recorded only in the core habitats in all the landscapes. Conversely, Shannon diversity index did not show significant differences.

Model Comparison of Bayesian Semiparametric and Parametric Structural Equation Models

ERIC Educational Resources Information Center

Song, Xin-Yuan; Xia, Ye-Mao; Pan, Jun-Hao; Lee, Sik-Yum

2011-01-01

Structural equation models have wide applications. One of the most important issues in analyzing structural equation models is model comparison. This article proposes a Bayesian model comparison statistic, namely the "L[subscript nu]"-measure for both semiparametric and parametric structural equation models. For illustration purposes, we consider…

Non-parametric characterization of long-term rainfall time series

NASA Astrophysics Data System (ADS)

Tiwari, Harinarayan; Pandey, Brij Kishor

2018-03-01

The statistical study of rainfall time series is one of the approaches for efficient hydrological system design. Identifying, and characterizing long-term rainfall time series could aid in improving hydrological systems forecasting. In the present study, eventual statistics was applied for the long-term (1851-2006) rainfall time series under seven meteorological regions of India. Linear trend analysis was carried out using Mann-Kendall test for the observed rainfall series. The observed trend using the above-mentioned approach has been ascertained using the innovative trend analysis method. Innovative trend analysis has been found to be a strong tool to detect the general trend of rainfall time series. Sequential Mann-Kendall test has also been carried out to examine nonlinear trends of the series. The partial sum of cumulative deviation test is also found to be suitable to detect the nonlinear trend. Innovative trend analysis, sequential Mann-Kendall test and partial cumulative deviation test have potential to detect the general as well as nonlinear trend for the rainfall time series. Annual rainfall analysis suggests that the maximum changes in mean rainfall is 11.53% for West Peninsular India, whereas the maximum fall in mean rainfall is 7.8% for the North Mountainous Indian region. The innovative trend analysis method is also capable of finding the number of change point available in the time series. Additionally, we have performed von Neumann ratio test and cumulative deviation test to estimate the departure from homogeneity. Singular spectrum analysis has been applied in this study to evaluate the order of departure from homogeneity in the rainfall time series. Monsoon season (JS) of North Mountainous India and West Peninsular India zones has higher departure from homogeneity and singular spectrum analysis shows the results to be in coherence with the same.

International comparisons of the technical efficiency of the hospital sector: panel data analysis of OECD countries using parametric and non-parametric approaches.

PubMed

Varabyova, Yauheniya; Schreyögg, Jonas

2013-09-01

There is a growing interest in the cross-country comparisons of the performance of national health care systems. The present work provides a comparison of the technical efficiency of the hospital sector using unbalanced panel data from OECD countries over the period 2000-2009. The estimation of the technical efficiency of the hospital sector is performed using nonparametric data envelopment analysis (DEA) and parametric stochastic frontier analysis (SFA). Internal and external validity of findings is assessed by estimating the Spearman rank correlations between the results obtained in different model specifications. The panel-data analyses using two-step DEA and one-stage SFA show that countries, which have higher health care expenditure per capita, tend to have a more technically efficient hospital sector. Whether the expenditure is financed through private or public sources is not related to the technical efficiency of the hospital sector. On the other hand, the hospital sector in countries with higher income inequality and longer average hospital length of stay is less technically efficient. Copyright © 2013 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

Quantum noise in SIS mixers

NASA Astrophysics Data System (ADS)

Zorin, A. B.

1985-03-01

In the present, quantum-statistical analysis of SIS heterodyne mixer performance, the conventional three-port model of the mixer circuit and the microscopic theory of superconducting tunnel junctions are used to derive a general expression for a noise parameter previously used for the case of parametric amplifiers. This expression is numerically evaluated for various quasiparticle current step widths, dc bias voltages, local oscillator powers, signal frequencies, signal source admittances, and operation temperatures.

A Genomewide Linkage Scan of Cocaine Dependence and Major Depressive Episode in Two Populations

PubMed Central

Yang, Bao-Zhu; Han, Shizhong; Kranzler, Henry R; Farrer, Lindsay A; Gelernter, Joel

2011-01-01

Cocaine dependence (CD) and major depressive episode (MDE) frequently co-occur with poorer treatment outcome and higher relapse risk. Shared genetic risk was affirmed; to date, there have been no reports of genomewide linkage scans (GWLSs) surveying the susceptibility regions for comorbid CD and MDE (CD–MDE). We aimed to identify chromosomal regions and candidate genes susceptible to CD, MDE, and CD–MDE in African Americans (AAs) and European Americans (EAs). A total of 1896 individuals were recruited from 384 AA and 355 EA families, each with at least a sibling-pair with CD and/or opioid dependence. Array-based genotyping of about 6000 single-nucleotide polymorphisms was completed for all individuals. Parametric and non-parametric genomewide linkage analyses were performed. We found a genomewide-significant linkage peak on chromosome 7 at 183.4 cM for non-parametric analysis of CD–MDE in AAs (lod=3.8, genomewide empirical p=0.016; point-wise p=0.00001). A nearly genomewide significant linkage was identified for CD–MDE in EAs on chromosome 5 at 14.3 cM (logarithm of odds (lod)=2.95, genomewide empirical p=0.055; point-wise p=0.00012). Parametric analysis corroborated the findings in these two regions and improved the support for the peak on chromosome 5 so that it reached genomewide significance (heterogeneity lod=3.28, genomewide empirical p=0.046; point-wise p=0.00053). This is the first GWLS for CD–MDE. The genomewide significant linkage regions on chromosomes 5 and 7 harbor four particularly promising candidate genes: SRD5A1, UBE3C, PTPRN2, and VIPR2. Replication of the linkage findings in other populations is warranted, as is a focused analysis of the genes located in the linkage regions implicated here. PMID:21849985

Hybrid modeling as a QbD/PAT tool in process development: an industrial E. coli case study.

PubMed

von Stosch, Moritz; Hamelink, Jan-Martijn; Oliveira, Rui

2016-05-01

Process understanding is emphasized in the process analytical technology initiative and the quality by design paradigm to be essential for manufacturing of biopharmaceutical products with consistent high quality. A typical approach to developing a process understanding is applying a combination of design of experiments with statistical data analysis. Hybrid semi-parametric modeling is investigated as an alternative method to pure statistical data analysis. The hybrid model framework provides flexibility to select model complexity based on available data and knowledge. Here, a parametric dynamic bioreactor model is integrated with a nonparametric artificial neural network that describes biomass and product formation rates as function of varied fed-batch fermentation conditions for high cell density heterologous protein production with E. coli. Our model can accurately describe biomass growth and product formation across variations in induction temperature, pH and feed rates. The model indicates that while product expression rate is a function of early induction phase conditions, it is negatively impacted as productivity increases. This could correspond with physiological changes due to cytoplasmic product accumulation. Due to the dynamic nature of the model, rational process timing decisions can be made and the impact of temporal variations in process parameters on product formation and process performance can be assessed, which is central for process understanding.

Measured, modeled, and causal conceptions of fitness

PubMed Central

Abrams, Marshall

2012-01-01

This paper proposes partial answers to the following questions: in what senses can fitness differences plausibly be considered causes of evolution?What relationships are there between fitness concepts used in empirical research, modeling, and abstract theoretical proposals? How does the relevance of different fitness concepts depend on research questions and methodological constraints? The paper develops a novel taxonomy of fitness concepts, beginning with type fitness (a property of a genotype or phenotype), token fitness (a property of a particular individual), and purely mathematical fitness. Type fitness includes statistical type fitness, which can be measured from population data, and parametric type fitness, which is an underlying property estimated by statistical type fitnesses. Token fitness includes measurable token fitness, which can be measured on an individual, and tendential token fitness, which is assumed to be an underlying property of the individual in its environmental circumstances. Some of the paper's conclusions can be outlined as follows: claims that fitness differences do not cause evolution are reasonable when fitness is treated as statistical type fitness, measurable token fitness, or purely mathematical fitness. Some of the ways in which statistical methods are used in population genetics suggest that what natural selection involves are differences in parametric type fitnesses. Further, it's reasonable to think that differences in parametric type fitness can cause evolution. Tendential token fitnesses, however, are not themselves sufficient for natural selection. Though parametric type fitnesses are typically not directly measurable, they can be modeled with purely mathematical fitnesses and estimated by statistical type fitnesses, which in turn are defined in terms of measurable token fitnesses. The paper clarifies the ways in which fitnesses depend on pragmatic choices made by researchers. PMID:23112804

Genome-wide regression and prediction with the BGLR statistical package.

PubMed

Pérez, Paulino; de los Campos, Gustavo

2014-10-01

Many modern genomic data analyses require implementing regressions where the number of parameters (p, e.g., the number of marker effects) exceeds sample size (n). Implementing these large-p-with-small-n regressions poses several statistical and computational challenges, some of which can be confronted using Bayesian methods. This approach allows integrating various parametric and nonparametric shrinkage and variable selection procedures in a unified and consistent manner. The BGLR R-package implements a large collection of Bayesian regression models, including parametric variable selection and shrinkage methods and semiparametric procedures (Bayesian reproducing kernel Hilbert spaces regressions, RKHS). The software was originally developed for genomic applications; however, the methods implemented are useful for many nongenomic applications as well. The response can be continuous (censored or not) or categorical (either binary or ordinal). The algorithm is based on a Gibbs sampler with scalar updates and the implementation takes advantage of efficient compiled C and Fortran routines. In this article we describe the methods implemented in BGLR, present examples of the use of the package, and discuss practical issues emerging in real-data analysis. Copyright © 2014 by the Genetics Society of America.

Estimation and confidence intervals for empirical mixing distributions

USGS Publications Warehouse

Link, W.A.; Sauer, J.R.

1995-01-01

Questions regarding collections of parameter estimates can frequently be expressed in terms of an empirical mixing distribution (EMD). This report discusses empirical Bayes estimation of an EMD, with emphasis on the construction of interval estimates. Estimation of the EMD is accomplished by substitution of estimates of prior parameters in the posterior mean of the EMD. This procedure is examined in a parametric model (the normal-normal mixture) and in a semi-parametric model. In both cases, the empirical Bayes bootstrap of Laird and Louis (1987, Journal of the American Statistical Association 82, 739-757) is used to assess the variability of the estimated EMD arising from the estimation of prior parameters. The proposed methods are applied to a meta-analysis of population trend estimates for groups of birds.

A meta-analysis of inferior thyroid artery variations in different human ethnic groups and their clinical implications.

PubMed

Toni, Roberto; Casa, Claudia Della; Castorina, Sergio; Roti, Elio; Ceda, Gianpaolo; Valenti, Giorgio

2005-09-01

We have recently found ethnic differences in superior thyroid artery (STA) variational anatomy. Therefore, we now focus on the inferior thyroid artery (ITA). In particular, we analyze whether presence, numerical variations and site of origin of ITA are influenced by ethnic group and gender, whether and which neck side has the largest arterial caliber, whether differences occur between the presence of ITA and STA, to which extent a non-selective thyroid angiography is effective in visualizing ITA, also in comparison to STA, and which clinical value this information may have in selected pathologies of the thyroid, parathyroid and larynx. A meta-analysis has been performed, including 33 library- and Medline-selected publications on Caucasoids (European and non-European) and East Asians, and a set of original data on European Caucasoids. A total of 6285 Caucasoid and 847 East Asian items, comprising half bodies and arteries, were analyzed. After testing the homogeneity of the available data sources in relation to the anatomical variables under study we calculated a cumulative value for each selected anatomical parameter and evaluated differences using non-parametric statistics. The effectiveness of non-selective thyroid angiography was determined using sensitivity, specificity, positive and negative predictive values. The ITA was more frequently absent in East Asians than in Caucasoids, and respectively either more or less frequently arising from thyrocervical and subclavian arteries, in East Asians versus Caucasoids. In contrast, the ITA was less frequently present both in Caucasoids and East Asians than the STA. In addition, the ITA was more frequently present on the right than on the left side in both ethnic groups, but no neck side predominated in size of arterial caliber in European Caucasoids. Finally, the ITA was more frequently present in East Asian males than females, and the effectiveness of a non-selective thyroid angiography showed higher numbers for ITA than STA in Caucasoids. Statistically significant variations occur in some ITA parameters between Caucasoids and East Asians, and in its presence with respect to STA, within each ethnic group. These differences, together with a sexual dimorphic presence of ITA in East Asians and high effectiveness of its visualization by non-selective angiography in European Caucasoids, may represent an evidence-based supply of anatomical information for analysis in selected pathologies of the thyroid, parathyroid and larynx.

Parametric Transformation Analysis

NASA Technical Reports Server (NTRS)

Gary, G. Allan

2003-01-01

Because twisted coronal features are important proxies for predicting solar eruptive events, and, yet not clearly understood, we present new results to resolve the complex, non-potential magnetic field configurations of active regions. This research uses free-form deformation mathematics to generate the associated coronal magnetic field. We use a parametric representation of the magnetic field lines such that the field lines can be manipulated to match the structure of EUV and SXR coronal loops. The objective is to derive sigmoidal magnetic field solutions which allows the beta greater than 1 regions to be included, aligned and non-aligned electric currents to be calculated, and the Lorentz force to be determined. The advantage of our technique is that the solution is independent of the unknown upper and side boundary conditions, allows non-vanishing magnetic forces, and provides a global magnetic field solution, which contains high- and low-beta regimes and is consistent with all the coronal images of the region. We show that the mathematical description is unique and physical.

Accurate analysis and visualization of cardiac (11)C-PIB uptake in amyloidosis with semiautomatic software.

PubMed

Kero, Tanja; Lindsjö, Lars; Sörensen, Jens; Lubberink, Mark

2016-08-01

(11)C-PIB PET is a promising non-invasive diagnostic tool for cardiac amyloidosis. Semiautomatic analysis of PET data is now available but it is not known how accurate these methods are for amyloid imaging. The aim of this study was to evaluate the feasibility of one semiautomatic software tool for analysis and visualization of (11)C-PIB left ventricular retention index (RI) in cardiac amyloidosis. Patients with systemic amyloidosis and cardiac involvement (n = 10) and healthy controls (n = 5) were investigated with dynamic (11)C-PIB PET. Two observers analyzed the PET studies with semiautomatic software to calculate the left ventricular RI of (11)C-PIB and to create parametric images. The mean RI at 15-25 min from the semiautomatic analysis was compared with RI based on manual analysis and showed comparable values (0.056 vs 0.054 min(-1) for amyloidosis patients and 0.024 vs 0.025 min(-1) in healthy controls; P = .78) and the correlation was excellent (r = 0.98). Inter-reader reproducibility also was excellent (intraclass correlation coefficient, ICC > 0.98). Parametric polarmaps and histograms made visual separation of amyloidosis patients and healthy controls fast and simple. Accurate semiautomatic analysis of cardiac (11)C-PIB RI in amyloidosis patients is feasible. Parametric polarmaps and histograms make visual interpretation fast and simple.

Consistency among distance measurements: transparency, BAO scale and accelerated expansion

NASA Astrophysics Data System (ADS)

Avgoustidis, Anastasios; Verde, Licia; Jimenez, Raul

2009-06-01

We explore consistency among different distance measures, including Supernovae Type Ia data, measurements of the Hubble parameter, and determination of the Baryon acoustic oscillation scale. We present new constraints on the cosmic transparency combining H(z) data together with the latest Supernovae Type Ia data compilation. This combination, in the context of a flat ΛCDM model, improves current constraints by nearly an order of magnitude although the constraints presented here are parametric rather than non-parametric. We re-examine the recently reported tension between the Baryon acoustic oscillation scale and Supernovae data in light of possible deviations from transparency, concluding that the source of the discrepancy may most likely be found among systematic effects of the modelling of the low redshift data or a simple ~ 2-σ statistical fluke, rather than in exotic physics. Finally, we attempt to draw model-independent conclusions about the recent accelerated expansion, determining the acceleration redshift to be zacc = 0.35+0.20-0.13 (1-σ).

The dose-response of salvage radiotherapy following radical prostatectomy: A systematic review and meta-analysis.

PubMed

King, Christopher R

2016-11-01

To date neither the optimal radiotherapy dose nor the existence of a dose-response has been established for salvage RT (SRT). A systematic review from 1996 to 2015 and meta-analysis was performed to identify the pathologic, clinical and treatment factors associated with relapse-free survival (RFS) after SRT (uniformly defined as a PSA>0.2ng/mL or rising above post-SRT nadir). A sigmoidal dose-response curve was objectively fitted and a non-parametric statistical test used to determine significance. 71 studies (10,034 patients) satisfied the meta-analysis criteria. SRT dose (p=0.0001), PSA prior to SRT (p=0.0009), ECE+ (p=0.039) and SV+ (p=0.046) had significant associations with RFS. Statistical analyses confirmed the independence of SRT dose-response. Omission of series with ADT did not alter results. Dose-response is well fit by a sigmoidal curve (p=0.0001) with a TCD 50 of 65.8Gy, with a dose of 70Gy achieving 58.4% RFS vs. 38.5% for 60Gy. A 2.0% [95% CI 1.1-3.2] improvement in RFS is achieved for each Gy. The SRT dose-response remarkably parallels that for definitive RT of localized disease. This study provides level 2a evidence for dose-escalated SRT>70Gy. The presence of an SRT dose-response for microscopic disease supports the hypothesis that prostate cancer is inherently radio-resistant. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

An Interactive Software for Conceptual Wing Flutter Analysis and Parametric Study

NASA Technical Reports Server (NTRS)

Mukhopadhyay, Vivek

1996-01-01

An interactive computer program was developed for wing flutter analysis in the conceptual design stage. The objective was to estimate the flutter instability boundary of a flexible cantilever wing, when well-defined structural and aerodynamic data are not available, and then study the effect of change in Mach number, dynamic pressure, torsional frequency, sweep, mass ratio, aspect ratio, taper ratio, center of gravity, and pitch inertia, to guide the development of the concept. The software was developed for Macintosh or IBM compatible personal computers, on MathCad application software with integrated documentation, graphics, data base and symbolic mathematics. The analysis method was based on non-dimensional parametric plots of two primary flutter parameters, namely Regier number and Flutter number, with normalization factors based on torsional stiffness, sweep, mass ratio, taper ratio, aspect ratio, center of gravity location and pitch inertia radius of gyration. The parametric plots were compiled in a Vought Corporation report from a vast data base of past experiments and wind-tunnel tests. The computer program was utilized for flutter analysis of the outer wing of a Blended-Wing-Body concept, proposed by McDonnell Douglas Corp. Using a set of assumed data, preliminary flutter boundary and flutter dynamic pressure variation with altitude, Mach number and torsional stiffness were determined.

Thermofluid Analysis of Magnetocaloric Refrigeration

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

Abdelaziz, Omar; Gluesenkamp, Kyle R; Vineyard, Edward Allan

While there have been extensive studies on thermofluid characteristics of different magnetocaloric refrigeration systems, a conclusive optimization study using non-dimensional parameters which can be applied to a generic system has not been reported yet. In this study, a numerical model has been developed for optimization of active magnetic refrigerator (AMR). This model is computationally efficient and robust, making it appropriate for running the thousands of simulations required for parametric study and optimization. The governing equations have been non-dimensionalized and numerically solved using finite difference method. A parametric study on a wide range of non-dimensional numbers has been performed. While themore » goal of AMR systems is to improve the performance of competitive parameters including COP, cooling capacity and temperature span, new parameters called AMR performance index-1 have been introduced in order to perform multi objective optimization and simultaneously exploit all these parameters. The multi-objective optimization is carried out for a wide range of the non-dimensional parameters. The results of this study will provide general guidelines for designing high performance AMR systems.« less

Non-stationarity and cross-correlation effects in the MHD solar activity

NASA Astrophysics Data System (ADS)

Demin, S. A.; Nefedyev, Y. A.; Andreev, A. O.; Demina, N. Y.; Timashev, S. F.

2018-01-01

The analysis of turbulent processes in sunspots and pores which are self-organizing long-lived magnetic structures is a complicated and not yet solved problem. The present work focuses on studying such magneto-hydrodynamic (MHD) formations on the basis of flicker-noise spectroscopy using a new method of multi-parametric analysis. The non-stationarity and cross-correlation effects taking place in solar activity dynamics are considered. The calculated maximum values of non-stationarity factor may become precursors of significant restructuring in solar magnetic activity. The introduced cross-correlation functions enable us to judge synchronization effects between the signals of various solar activity indicators registered simultaneously.

Topographic optic disc analysis by Heidelberg retinal tomography in ocular Behcet's disease.

PubMed

Berker, Nilufer; Elgin, Ufuk; Ozdal, Pinar; Batman, Aygen; Soykan, Emel; Ozkan, Seyhan S

2007-09-01

To compare the topographic characteristics of the optic discs in patients with severe and mild ocular Behçet's disease by using Heidelberg retinal tomographaphy (HRT). This prospective study included 47 eyes of 47 patients with ocular BD who were being followed-up at the Uveitis Clinic of the Ankara Ulucanlar Eye Research Hospital, Ankara, Turkey. The patients were divided into two groups. Group 1 consisted of 21 eyes with mild uveitis, and group 2 consisted of 26 eyes with severe uveitis. All patients underwent topographic optic disc analysis by HRT II, and the quantitative optic disc parameters of both groups were compared by non-parametric Mann-Whitney U test. The mean cup volume, rim volume, cup area, disc area and cup depth in group 1 were found to be statistically significantly greater than those in group 2 (p<0.0001, p = 0.03, p = 0.021, p = 0.01 and p = 0.017, respectively), while the difference between the mean cup-to-disc ratios in group 1 and group 2 were found to be statistically insignificant (p = 0.148). A relationship was found between the severity of ocular BD and optic disc topography determined by HRT. In eyes with smaller optic discs, uveitis was observed to have a more severe course with more frequent relapses than those with larger discs.

Comparison of somatotype values of football players in two professional league football teams according to the positions.

PubMed

Orhan, Ozlem; Sagir, Mehmet; Zorba, Erdal

2013-06-01

This study compared the somatotype values of football players according to their playing positions. The study aimed to determine the physical profiles of players and to analyze the relationships between somatotypes and playing positions. Study participants were members of two teams in the Turkey Professional Football League, Gençlerbirligi Sports Team (GB) (N = 24) and Gençlerbirligi Oftas Sports Team (GBO) (N = 24). Anthropometric measurements of the players were performed according to techniques suggested by the Anthropometric Standardization Reference Manual (ASRM) and International Biological Program (IBP). In somatotype calculations, triceps, subscapular, supraspinale and calf skinfold thickness, humerus bicondylar, femur bicondylar, biceps circumference, calf circumference and body weight and height were used. Statistical analysis of the data was performed using the Graph Pad prism Version 5.00 for Windows (Graph Pad Software, San Diego California USA); somatotype calculations and analyses used the Somatotype 1.1 program and graphical representations of the results were produced. Analysis of non-parametric (two independent samples) Mann-Whitney U Test of the player data showed that there were no statistically significant differences between the two teams. The measurements indicated that, when all of the GB and GBO players were evaluated collectively, their average somatotypes were balanced mesomorph. The somatotypes of GBO goalkeepers were generally ectomorphic mesomorph; GB goalkeepers were balanced mesomorphic, although they were slightly endomorphic.

Statistical detection of geographic clusters of resistant Escherichia coli in a regional network with WHONET and SaTScan

PubMed Central

Park, Rachel; O'Brien, Thomas F.; Huang, Susan S.; Baker, Meghan A.; Yokoe, Deborah S.; Kulldorff, Martin; Barrett, Craig; Swift, Jamie; Stelling, John

2016-01-01

Objectives While antimicrobial resistance threatens the prevention, treatment, and control of infectious diseases, systematic analysis of routine microbiology laboratory test results worldwide can alert new threats and promote timely response. This study explores statistical algorithms for recognizing geographic clustering of multi-resistant microbes within a healthcare network and monitoring the dissemination of new strains over time. Methods Escherichia coli antimicrobial susceptibility data from a three-year period stored in WHONET were analyzed across ten facilities in a healthcare network utilizing SaTScan's spatial multinomial model with two models for defining geographic proximity. We explored geographic clustering of multi-resistance phenotypes within the network and changes in clustering over time. Results Geographic clustering identified from both latitude/longitude and non-parametric facility groupings geographic models were similar, while the latter was offers greater flexibility and generalizability. Iterative application of the clustering algorithms suggested the possible recognition of the initial appearance of invasive E. coli ST131 in the clinical database of a single hospital and subsequent dissemination to others. Conclusion Systematic analysis of routine antimicrobial resistance susceptibility test results supports the recognition of geographic clustering of microbial phenotypic subpopulations with WHONET and SaTScan, and iterative application of these algorithms can detect the initial appearance in and dissemination across a region prompting early investigation, response, and containment measures. PMID:27530311

Statistical analysis of trends in monthly precipitation at the Limbang River Basin, Sarawak (NW Borneo), Malaysia

NASA Astrophysics Data System (ADS)

Krishnan, M. V. Ninu; Prasanna, M. V.; Vijith, H.

2018-05-01

Effect of climate change in a region can be characterised by the analysis of rainfall trends. In the present research, monthly rainfall trends at Limbang River Basin (LRB) in Sarawak, Malaysia for a period of 45 years (1970-2015) were characterised through the non-parametric Mann-Kendall and Spearman's Rho tests and relative seasonality index. Statistically processed monthly rainfall of 12 well distributed rain gauging stations in LRB shows almost equal amount of rainfall in all months. Mann-Kendall and Spearman's Rho tests revealed a specific pattern of rainfall trend with a definite boundary marked in the months of January and August with positive trends in all stations. Among the stations, Limbang DID, Long Napir and Ukong showed positive (increasing) trends in all months with a maximum increase of 4.06 mm/year (p = 0.01) in November. All other stations showed varying trends (both increasing and decreasing). Significant (p = 0.05) decreasing trend was noticed in Ulu Medalam and Setuan during September (- 1.67 and - 1.79 mm/year) and October (- 1.59 and - 1.68 mm/year) in Mann-Kendall and Spearman's Rho tests. Spatial pattern of monthly rainfall trends showed two clusters of increasing rainfalls (maximas) in upper and lower part of the river basin separated with a dominant decreasing rainfall corridor. The results indicate a generally increasing trend of rainfall in Sarawak, Borneo.

Statistical plant set estimation using Schroeder-phased multisinusoidal input design

NASA Technical Reports Server (NTRS)

Bayard, D. S.

1992-01-01

A frequency domain method is developed for plant set estimation. The estimation of a plant 'set' rather than a point estimate is required to support many methods of modern robust control design. The approach here is based on using a Schroeder-phased multisinusoid input design which has the special property of placing input energy only at the discrete frequency points used in the computation. A detailed analysis of the statistical properties of the frequency domain estimator is given, leading to exact expressions for the probability distribution of the estimation error, and many important properties. It is shown that, for any nominal parametric plant estimate, one can use these results to construct an overbound on the additive uncertainty to any prescribed statistical confidence. The 'soft' bound thus obtained can be used to replace 'hard' bounds presently used in many robust control analysis and synthesis methods.

Statistical analysis for validating ACO-KNN algorithm as feature selection in sentiment analysis

NASA Astrophysics Data System (ADS)

Ahmad, Siti Rohaidah; Yusop, Nurhafizah Moziyana Mohd; Bakar, Azuraliza Abu; Yaakub, Mohd Ridzwan

2017-10-01

This research paper aims to propose a hybrid of ant colony optimization (ACO) and k-nearest neighbor (KNN) algorithms as feature selections for selecting and choosing relevant features from customer review datasets. Information gain (IG), genetic algorithm (GA), and rough set attribute reduction (RSAR) were used as baseline algorithms in a performance comparison with the proposed algorithm. This paper will also discuss the significance test, which was used to evaluate the performance differences between the ACO-KNN, IG-GA, and IG-RSAR algorithms. This study evaluated the performance of the ACO-KNN algorithm using precision, recall, and F-score, which were validated using the parametric statistical significance tests. The evaluation process has statistically proven that this ACO-KNN algorithm has been significantly improved compared to the baseline algorithms. The evaluation process has statistically proven that this ACO-KNN algorithm has been significantly improved compared to the baseline algorithms. In addition, the experimental results have proven that the ACO-KNN can be used as a feature selection technique in sentiment analysis to obtain quality, optimal feature subset that can represent the actual data in customer review data.

Parametric estimates for the receiver operating characteristic curve generalization for non-monotone relationships.

PubMed

Martínez-Camblor, Pablo; Pardo-Fernández, Juan C

2017-01-01

Diagnostic procedures are based on establishing certain conditions and then checking if those conditions are satisfied by a given individual. When the diagnostic procedure is based on a continuous marker, this is equivalent to fix a region or classification subset and then check if the observed value of the marker belongs to that region. Receiver operating characteristic curve is a valuable and popular tool to study and compare the diagnostic ability of a given marker. Besides, the area under the receiver operating characteristic curve is frequently used as an index of the global discrimination ability. This paper revises and widens the scope of the receiver operating characteristic curve definition by setting the classification subsets in which the final decision is based in the spotlight of the analysis. We revise the definition of the receiver operating characteristic curve in terms of particular classes of classification subsets and then focus on a receiver operating characteristic curve generalization for situations in which both low and high values of the marker are associated with more probability of having the studied characteristic. Parametric and non-parametric estimators of the receiver operating characteristic curve generalization are investigated. Monte Carlo studies and real data examples illustrate their practical performance.

Power transfer systems for future navy helicopters. Final report 25 Jun 70--28 Jun 72

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

Bossler, R.B. Jr.

1972-11-01

The purpose of this program was to conduct an analysis of helicopter power transfer systems (pts), both conventional and advanced concept type, with the objective of reducing specific weights and improving reliability beyond present values. The analysis satisfied requirements specified for a 200,000 pound cargo transport helicopter (CTH), a 70,000 pound heavy assault helicopter, and a 15,000 pound non-combat search and rescue helicopter. Four selected gearing systems (out of seven studied), optimized for lightest weight and equal reliability for the CTH, using component proportioning via stress and stiffness equations, had no significant difference between their aircraft payloads. All optimized ptsmore » were approximately 70% of statistically predicted weight. Reliability increase is predicted via gearbox derating using Weibull relationships. Among advanced concepts, the Turbine Integrated Geared Rotor was competitive for weight, technology availability and reliability increase but handicapped by a special engine requirement. The warm cycle system was found not competitive. Helicopter parametric weight analysis is shown. Advanced development Plans are presented for the pts for the CTH, including total pts system, selected pts components, and scale model flight testing in a Kaman HH2 helicopter.« less