Estimating contaminant loads in rivers: An application of adjusted maximum likelihood to type 1 censored data

USGS Publications Warehouse

Cohn, Timothy A.

2005-01-01

This paper presents an adjusted maximum likelihood estimator (AMLE) that can be used to estimate fluvial transport of contaminants, like phosphorus, that are subject to censoring because of analytical detection limits. The AMLE is a generalization of the widely accepted minimum variance unbiased estimator (MVUE), and Monte Carlo experiments confirm that it shares essentially all of the MVUE's desirable properties, including high efficiency and negligible bias. In particular, the AMLE exhibits substantially less bias than alternative censored‐data estimators such as the MLE (Tobit) or the MLE followed by a jackknife. As with the MLE and the MVUE the AMLE comes close to achieving the theoretical Frechet‐Cramér‐Rao bounds on its variance. This paper also presents a statistical framework, applicable to both censored and complete data, for understanding and estimating the components of uncertainty associated with load estimates. This can serve to lower the cost and improve the efficiency of both traditional and real‐time water quality monitoring.

REML/BLUP and sequential path analysis in estimating genotypic values and interrelationships among simple maize grain yield-related traits.

PubMed

Olivoto, T; Nardino, M; Carvalho, I R; Follmann, D N; Ferrari, M; Szareski, V J; de Pelegrin, A J; de Souza, V Q

2017-03-22

Methodologies using restricted maximum likelihood/best linear unbiased prediction (REML/BLUP) in combination with sequential path analysis in maize are still limited in the literature. Therefore, the aims of this study were: i) to use REML/BLUP-based procedures in order to estimate variance components, genetic parameters, and genotypic values of simple maize hybrids, and ii) to fit stepwise regressions considering genotypic values to form a path diagram with multi-order predictors and minimum multicollinearity that explains the relationships of cause and effect among grain yield-related traits. Fifteen commercial simple maize hybrids were evaluated in multi-environment trials in a randomized complete block design with four replications. The environmental variance (78.80%) and genotype-vs-environment variance (20.83%) accounted for more than 99% of the phenotypic variance of grain yield, which difficult the direct selection of breeders for this trait. The sequential path analysis model allowed the selection of traits with high explanatory power and minimum multicollinearity, resulting in models with elevated fit (R 2 > 0.9 and ε < 0.3). The number of kernels per ear (NKE) and thousand-kernel weight (TKW) are the traits with the largest direct effects on grain yield (r = 0.66 and 0.73, respectively). The high accuracy of selection (0.86 and 0.89) associated with the high heritability of the average (0.732 and 0.794) for NKE and TKW, respectively, indicated good reliability and prospects of success in the indirect selection of hybrids with high-yield potential through these traits. The negative direct effect of NKE on TKW (r = -0.856), however, must be considered. The joint use of mixed models and sequential path analysis is effective in the evaluation of maize-breeding trials.

Kriging analysis of mean annual precipitation, Powder River Basin, Montana and Wyoming

USGS Publications Warehouse

Karlinger, M.R.; Skrivan, James A.

1981-01-01

Kriging is a statistical estimation technique for regionalized variables which exhibit an autocorrelation structure. Such structure can be described by a semi-variogram of the observed data. The kriging estimate at any point is a weighted average of the data, where the weights are determined using the semi-variogram and an assumed drift, or lack of drift, in the data. Block, or areal, estimates can also be calculated. The kriging algorithm, based on unbiased and minimum-variance estimates, involves a linear system of equations to calculate the weights. Kriging variances can then be used to give confidence intervals of the resulting estimates. Mean annual precipitation in the Powder River basin, Montana and Wyoming, is an important variable when considering restoration of coal-strip-mining lands of the region. Two kriging analyses involving data at 60 stations were made--one assuming no drift in precipitation, and one a partial quadratic drift simulating orographic effects. Contour maps of estimates of mean annual precipitation were similar for both analyses, as were the corresponding contours of kriging variances. Block estimates of mean annual precipitation were made for two subbasins. Runoff estimates were 1-2 percent of the kriged block estimates. (USGS)

Statistics as Unbiased Estimators: Exploring the Teaching of Standard Deviation

ERIC Educational Resources Information Center

Wasserman, Nicholas H.; Casey, Stephanie; Champion, Joe; Huey, Maryann

2017-01-01

This manuscript presents findings from a study about the knowledge for and planned teaching of standard deviation. We investigate how understanding variance as an unbiased (inferential) estimator--not just a descriptive statistic for the variation (spread) in data--is related to teachers' instruction regarding standard deviation, particularly…

Statistical Properties of Maximum Likelihood Estimators of Power Law Spectra Information

NASA Technical Reports Server (NTRS)

Howell, L. W., Jr.

2003-01-01

A simple power law model consisting of a single spectral index, sigma(sub 2), is believed to be an adequate description of the galactic cosmic-ray (GCR) proton flux at energies below 10(exp 13) eV, with a transition at the knee energy, E(sub k), to a steeper spectral index sigma(sub 2) greater than sigma(sub 1) above E(sub k). The maximum likelihood (ML) procedure was developed for estimating the single parameter sigma(sub 1) of a simple power law energy spectrum and generalized to estimate the three spectral parameters of the broken power law energy spectrum from simulated detector responses and real cosmic-ray data. The statistical properties of the ML estimator were investigated and shown to have the three desirable properties: (Pl) consistency (asymptotically unbiased), (P2) efficiency (asymptotically attains the Cramer-Rao minimum variance bound), and (P3) asymptotically normally distributed, under a wide range of potential detector response functions. Attainment of these properties necessarily implies that the ML estimation procedure provides the best unbiased estimator possible. While simulation studies can easily determine if a given estimation procedure provides an unbiased estimate of the spectra information, and whether or not the estimator is approximately normally distributed, attainment of the Cramer-Rao bound (CRB) can only be ascertained by calculating the CRB for an assumed energy spectrum- detector response function combination, which can be quite formidable in practice. However, the effort in calculating the CRB is very worthwhile because it provides the necessary means to compare the efficiency of competing estimation techniques and, furthermore, provides a stopping rule in the search for the best unbiased estimator. Consequently, the CRB for both the simple and broken power law energy spectra are derived herein and the conditions under which they are stained in practice are investigated.

Uncertainty importance analysis using parametric moment ratio functions.

PubMed

Wei, Pengfei; Lu, Zhenzhou; Song, Jingwen

2014-02-01

This article presents a new importance analysis framework, called parametric moment ratio function, for measuring the reduction of model output uncertainty when the distribution parameters of inputs are changed, and the emphasis is put on the mean and variance ratio functions with respect to the variances of model inputs. The proposed concepts efficiently guide the analyst to achieve a targeted reduction on the model output mean and variance by operating on the variances of model inputs. The unbiased and progressive unbiased Monte Carlo estimators are also derived for the parametric mean and variance ratio functions, respectively. Only a set of samples is needed for implementing the proposed importance analysis by the proposed estimators, thus the computational cost is free of input dimensionality. An analytical test example with highly nonlinear behavior is introduced for illustrating the engineering significance of the proposed importance analysis technique and verifying the efficiency and convergence of the derived Monte Carlo estimators. Finally, the moment ratio function is applied to a planar 10-bar structure for achieving a targeted 50% reduction of the model output variance. © 2013 Society for Risk Analysis.

On the robustness of a Bayes estimate. [in reliability theory

NASA Technical Reports Server (NTRS)

Canavos, G. C.

1974-01-01

This paper examines the robustness of a Bayes estimator with respect to the assigned prior distribution. A Bayesian analysis for a stochastic scale parameter of a Weibull failure model is summarized in which the natural conjugate is assigned as the prior distribution of the random parameter. The sensitivity analysis is carried out by the Monte Carlo method in which, although an inverted gamma is the assigned prior, realizations are generated using distribution functions of varying shape. For several distributional forms and even for some fixed values of the parameter, simulated mean squared errors of Bayes and minimum variance unbiased estimators are determined and compared. Results indicate that the Bayes estimator remains squared-error superior and appears to be largely robust to the form of the assigned prior distribution.

A new approach to importance sampling for the simulation of false alarms. [in radar systems

NASA Technical Reports Server (NTRS)

Lu, D.; Yao, K.

1987-01-01

In this paper a modified importance sampling technique for improving the convergence of Importance Sampling is given. By using this approach to estimate low false alarm rates in radar simulations, the number of Monte Carlo runs can be reduced significantly. For one-dimensional exponential, Weibull, and Rayleigh distributions, a uniformly minimum variance unbiased estimator is obtained. For Gaussian distribution the estimator in this approach is uniformly better than that of previously known Importance Sampling approach. For a cell averaging system, by combining this technique and group sampling, the reduction of Monte Carlo runs for a reference cell of 20 and false alarm rate of lE-6 is on the order of 170 as compared to the previously known Importance Sampling approach.

The dependability of medical students' performance ratings as documented on in-training evaluations.

PubMed

van Barneveld, Christina

2005-03-01

To demonstrate an approach to obtain an unbiased estimate of the dependability of students' performance ratings during training, when the data-collection design includes nesting of student in rater, unbalanced nest sizes, and dependent observations. In 2003, two variance components analyses of in-training evaluation (ITE) report data were conducted using urGENOVA software. In the first analysis, the dependability for the nested and unbalanced data-collection design was calculated. In the second analysis, an approach using multiple generalizability studies was used to obtain an unbiased estimate of the student variance component, resulting in an unbiased estimate of dependability. Results suggested that there is bias in estimates of the dependability of students' performance on ITEs that are attributable to the data-collection design. When the bias was corrected, the results indicated that the dependability of ratings of student performance was almost zero. The combination of the multiple generalizability studies method and the use of specialized software provides an unbiased estimate of the dependability of ratings of student performance on ITE scores for data-collection designs that include nesting of student in rater, unbalanced nest sizes, and dependent observations.

Predicting minimum uncertainties in the inversion of ocean color geophysical parameters based on Cramer-Rao bounds.

PubMed

Jay, Sylvain; Guillaume, Mireille; Chami, Malik; Minghelli, Audrey; Deville, Yannick; Lafrance, Bruno; Serfaty, Véronique

2018-01-22

We present an analytical approach based on Cramer-Rao Bounds (CRBs) to investigate the uncertainties in estimated ocean color parameters resulting from the propagation of uncertainties in the bio-optical reflectance modeling through the inversion process. Based on given bio-optical and noise probabilistic models, CRBs can be computed efficiently for any set of ocean color parameters and any sensor configuration, directly providing the minimum estimation variance that can be possibly attained by any unbiased estimator of any targeted parameter. Here, CRBs are explicitly developed using (1) two water reflectance models corresponding to deep and shallow waters, resp., and (2) four probabilistic models describing the environmental noises observed within four Sentinel-2 MSI, HICO, Sentinel-3 OLCI and MODIS images, resp. For both deep and shallow waters, CRBs are shown to be consistent with the experimental estimation variances obtained using two published remote-sensing methods, while not requiring one to perform any inversion. CRBs are also used to investigate to what extent perfect a priori knowledge on one or several geophysical parameters can improve the estimation of remaining unknown parameters. For example, using pre-existing knowledge of bathymetry (e.g., derived from LiDAR) within the inversion is shown to greatly improve the retrieval of bottom cover for shallow waters. Finally, CRBs are shown to provide valuable information on the best estimation performances that may be achieved with the MSI, HICO, OLCI and MODIS configurations for a variety of oceanic, coastal and inland waters. CRBs are thus demonstrated to be an informative and efficient tool to characterize minimum uncertainties in inverted ocean color geophysical parameters.

Elimination of trait blocks from multiple trait mixed model equations with singular (Co)variance parameter matrices

USDA-ARS?s Scientific Manuscript database

Transformations to multiple trait mixed model equations (MME) which are intended to improve computational efficiency in best linear unbiased prediction (BLUP) and restricted maximum likelihood (REML) are described. It is shown that traits that are expected or estimated to have zero residual variance...

Measuring the Power Spectrum with Peculiar Velocities

NASA Astrophysics Data System (ADS)

Macaulay, Edward; Feldman, H. A.; Ferreira, P. G.; Jaffe, A. H.; Agarwal, S.; Hudson, M. J.; Watkins, R.

2012-01-01

The peculiar velocities of galaxies are an inherently valuable cosmological probe, providing an unbiased estimate of the distribution of matter on scales much larger than the depth of the survey. Much research interest has been motivated by the high dipole moment of our local peculiar velocity field, which suggests a large scale excess in the matter power spectrum, and can appear to be in some tension with the LCDM model. We use a composite catalogue of 4,537 peculiar velocity measurements with a characteristic depth of 33 h-1 Mpc to estimate the matter power spectrum. We compare the constraints with this method, directly studying the full peculiar velocity catalogue, to results from Macaulay et al. (2011), studying minimum variance moments of the velocity field, as calculated by Watkins, Feldman & Hudson (2009) and Feldman, Watkins & Hudson (2010). We find good agreement with the LCDM model on scales of k > 0.01 h Mpc-1. We find an excess of power on scales of k < 0.01 h Mpc-1, although with a 1 sigma uncertainty which includes the LCDM model. We find that the uncertainty in the excess at these scales is larger than an alternative result studying only moments of the velocity field, which is due to the minimum variance weights used to calculate the moments. At small scales, we are able to clearly discriminate between linear and nonlinear clustering in simulated peculiar velocity catalogues, and find some evidence (although less clear) for linear clustering in the real peculiar velocity data.

Power spectrum estimation from peculiar velocity catalogues

NASA Astrophysics Data System (ADS)

Macaulay, E.; Feldman, H. A.; Ferreira, P. G.; Jaffe, A. H.; Agarwal, S.; Hudson, M. J.; Watkins, R.

2012-09-01

The peculiar velocities of galaxies are an inherently valuable cosmological probe, providing an unbiased estimate of the distribution of matter on scales much larger than the depth of the survey. Much research interest has been motivated by the high dipole moment of our local peculiar velocity field, which suggests a large-scale excess in the matter power spectrum and can appear to be in some tension with the Λ cold dark matter (ΛCDM) model. We use a composite catalogue of 4537 peculiar velocity measurements with a characteristic depth of 33 h-1 Mpc to estimate the matter power spectrum. We compare the constraints with this method, directly studying the full peculiar velocity catalogue, to results by Macaulay et al., studying minimum variance moments of the velocity field, as calculated by Feldman, Watkins & Hudson. We find good agreement with the ΛCDM model on scales of k > 0.01 h Mpc-1. We find an excess of power on scales of k < 0.01 h Mpc-1 with a 1σ uncertainty which includes the ΛCDM model. We find that the uncertainty in excess at these scales is larger than an alternative result studying only moments of the velocity field, which is due to the minimum variance weights used to calculate the moments. At small scales, we are able to clearly discriminate between linear and non-linear clustering in simulated peculiar velocity catalogues and find some evidence (although less clear) for linear clustering in the real peculiar velocity data.

Calibrating SALT: a sampling scheme to improve estimates of suspended sediment yield

Treesearch

Robert B. Thomas

1986-01-01

Abstract - SALT (Selection At List Time) is a variable probability sampling scheme that provides unbiased estimates of suspended sediment yield and its variance. SALT performs better than standard schemes which are estimate variance. Sampling probabilities are based on a sediment rating function which promotes greater sampling intensity during periods of high...

A robust pseudo-inverse spectral filter applied to the Earth Radiation Budget Experiment (ERBE) scanning channels

NASA Technical Reports Server (NTRS)

Avis, L. M.; Green, R. N.; Suttles, J. T.; Gupta, S. K.

1984-01-01

Computer simulations of a least squares estimator operating on the ERBE scanning channels are discussed. The estimator is designed to minimize the errors produced by nonideal spectral response to spectrally varying and uncertain radiant input. The three ERBE scanning channels cover a shortwave band a longwave band and a ""total'' band from which the pseudo inverse spectral filter estimates the radiance components in the shortwave band and a longwave band. The radiance estimator draws on instantaneous field of view (IFOV) scene type information supplied by another algorithm of the ERBE software, and on a priori probabilistic models of the responses of the scanning channels to the IFOV scene types for given Sun scene spacecraft geometry. It is found that the pseudoinverse spectral filter is stable, tolerant of errors in scene identification and in channel response modeling, and, in the absence of such errors, yields minimum variance and essentially unbiased radiance estimates.

Texture and haptic cues in slant discrimination: reliability-based cue weighting without statistically optimal cue combination

NASA Astrophysics Data System (ADS)

Rosas, Pedro; Wagemans, Johan; Ernst, Marc O.; Wichmann, Felix A.

2005-05-01

A number of models of depth-cue combination suggest that the final depth percept results from a weighted average of independent depth estimates based on the different cues available. The weight of each cue in such an average is thought to depend on the reliability of each cue. In principle, such a depth estimation could be statistically optimal in the sense of producing the minimum-variance unbiased estimator that can be constructed from the available information. Here we test such models by using visual and haptic depth information. Different texture types produce differences in slant-discrimination performance, thus providing a means for testing a reliability-sensitive cue-combination model with texture as one of the cues to slant. Our results show that the weights for the cues were generally sensitive to their reliability but fell short of statistically optimal combination - we find reliability-based reweighting but not statistically optimal cue combination.

A comparison of selection at list time and time-stratified sampling for estimating suspended sediment loads

Treesearch

Robert B. Thomas; Jack Lewis

1993-01-01

Time-stratified sampling of sediment for estimating suspended load is introduced and compared to selection at list time (SALT) sampling. Both methods provide unbiased estimates of load and variance. The magnitude of the variance of the two methods is compared using five storm populations of suspended sediment flux derived from turbidity data. Under like conditions,...

Extreme Mean and Its Applications

NASA Technical Reports Server (NTRS)

Swaroop, R.; Brownlow, J. D.

1979-01-01

Extreme value statistics obtained from normally distributed data are considered. An extreme mean is defined as the mean of p-th probability truncated normal distribution. An unbiased estimate of this extreme mean and its large sample distribution are derived. The distribution of this estimate even for very large samples is found to be nonnormal. Further, as the sample size increases, the variance of the unbiased estimate converges to the Cramer-Rao lower bound. The computer program used to obtain the density and distribution functions of the standardized unbiased estimate, and the confidence intervals of the extreme mean for any data are included for ready application. An example is included to demonstrate the usefulness of extreme mean application.

One-shot estimate of MRMC variance: AUC.

PubMed

Gallas, Brandon D

2006-03-01

One popular study design for estimating the area under the receiver operating characteristic curve (AUC) is the one in which a set of readers reads a set of cases: a fully crossed design in which every reader reads every case. The variability of the subsequent reader-averaged AUC has two sources: the multiple readers and the multiple cases (MRMC). In this article, we present a nonparametric estimate for the variance of the reader-averaged AUC that is unbiased and does not use resampling tools. The one-shot estimate is based on the MRMC variance derived by the mechanistic approach of Barrett et al. (2005), as well as the nonparametric variance of a single-reader AUC derived in the literature on U statistics. We investigate the bias and variance properties of the one-shot estimate through a set of Monte Carlo simulations with simulated model observers and images. The different simulation configurations vary numbers of readers and cases, amounts of image noise and internal noise, as well as how the readers are constructed. We compare the one-shot estimate to a method that uses the jackknife resampling technique with an analysis of variance model at its foundation (Dorfman et al. 1992). The name one-shot highlights that resampling is not used. The one-shot and jackknife estimators behave similarly, with the one-shot being marginally more efficient when the number of cases is small. We have derived a one-shot estimate of the MRMC variance of AUC that is based on a probabilistic foundation with limited assumptions, is unbiased, and compares favorably to an established estimate.

Statistical Properties of Maximum Likelihood Estimators of Power Law Spectra Information

NASA Technical Reports Server (NTRS)

Howell, L. W.

2002-01-01

A simple power law model consisting of a single spectral index, a is believed to be an adequate description of the galactic cosmic-ray (GCR) proton flux at energies below 10(exp 13) eV, with a transition at the knee energy, E(sub k), to a steeper spectral index alpha(sub 2) greater than alpha(sub 1) above E(sub k). The Maximum likelihood (ML) procedure was developed for estimating the single parameter alpha(sub 1) of a simple power law energy spectrum and generalized to estimate the three spectral parameters of the broken power law energy spectrum from simulated detector responses and real cosmic-ray data. The statistical properties of the ML estimator were investigated and shown to have the three desirable properties: (P1) consistency (asymptotically unbiased). (P2) efficiency asymptotically attains the Cramer-Rao minimum variance bound), and (P3) asymptotically normally distributed, under a wide range of potential detector response functions. Attainment of these properties necessarily implies that the ML estimation procedure provides the best unbiased estimator possible. While simulation studies can easily determine if a given estimation procedure provides an unbiased estimate of the spectra information, and whether or not the estimator is approximately normally distributed, attainment of the Cramer-Rao bound (CRB) can only he ascertained by calculating the CRB for an assumed energy spectrum-detector response function combination, which can be quite formidable in practice. However. the effort in calculating the CRB is very worthwhile because it provides the necessary means to compare the efficiency of competing estimation techniques and, furthermore, provides a stopping rule in the search for the best unbiased estimator. Consequently, the CRB for both the simple and broken power law energy spectra are derived herein and the conditions under which they are attained in practice are investigated. The ML technique is then extended to estimate spectra information from an arbitrary number of astrophysics data sets produced by vastly different science instruments. This theory and its successful implementation will facilitate the interpretation of spectral information from multiple astrophysics missions and thereby permit the derivation of superior spectral parameter estimates based on the combination of data sets.

Systematic effects of foreground removal in 21-cm surveys of reionization

NASA Astrophysics Data System (ADS)

Petrovic, Nada; Oh, S. Peng

2011-05-01

21-cm observations have the potential to revolutionize our understanding of the high-redshift Universe. Whilst extremely bright radio continuum foregrounds exist at these frequencies, their spectral smoothness can be exploited to allow efficient foreground subtraction. It is well known that - regardless of other instrumental effects - this removes power on scales comparable to the survey bandwidth. We investigate associated systematic biases. We show that removing line-of-sight fluctuations on large scales aliases into suppression of the 3D power spectrum across a broad range of scales. This bias can be dealt with by correctly marginalizing over small wavenumbers in the 1D power spectrum; however, the unbiased estimator will have unavoidably larger variance. We also show that Gaussian realizations of the power spectrum permit accurate and extremely rapid Monte Carlo simulations for error analysis; repeated realizations of the fully non-Gaussian field are unnecessary. We perform Monte Carlo maximum likelihood simulations of foreground removal which yield unbiased, minimum variance estimates of the power spectrum in agreement with Fisher matrix estimates. Foreground removal also distorts the 21-cm probability distribution function (PDF), reducing the contrast between neutral and ionized regions, with potentially serious consequences for efforts to extract information from the PDF. We show that it is the subtraction of large-scale modes which is responsible for this distortion, and that it is less severe in the earlier stages of reionization. It can be reduced by using larger bandwidths. In the late stages of reionization, identification of the largest ionized regions (which consist of foreground emission only) provides calibration points which potentially allow recovery of large-scale modes. Finally, we also show that (i) the broad frequency response of synchrotron and free-free emission will smear out any features in the electron momentum distribution and ensure spectrally smooth foregrounds and (ii) extragalactic radio recombination lines should be negligible foregrounds.

Optimal reconstruction of the states in qutrit systems

NASA Astrophysics Data System (ADS)

Yan, Fei; Yang, Ming; Cao, Zhuo-Liang

2010-10-01

Based on mutually unbiased measurements, an optimal tomographic scheme for the multiqutrit states is presented explicitly. Because the reconstruction process of states based on mutually unbiased states is free of information waste, we refer to our scheme as the optimal scheme. By optimal we mean that the number of the required conditional operations reaches the minimum in this tomographic scheme for the states of qutrit systems. Special attention will be paid to how those different mutually unbiased measurements are realized; that is, how to decompose each transformation that connects each mutually unbiased basis with the standard computational basis. It is found that all those transformations can be decomposed into several basic implementable single- and two-qutrit unitary operations. For the three-qutrit system, there exist five different mutually unbiased-bases structures with different entanglement properties, so we introduce the concept of physical complexity to minimize the number of nonlocal operations needed over the five different structures. This scheme is helpful for experimental scientists to realize the most economical reconstruction of quantum states in qutrit systems.

Reliability Stress-Strength Models for Dependent Observations with Applications in Clinical Trials

NASA Technical Reports Server (NTRS)

Kushary, Debashis; Kulkarni, Pandurang M.

1995-01-01

We consider the applications of stress-strength models in studies involving clinical trials. When studying the effects and side effects of certain procedures (treatments), it is often the case that observations are correlated due to subject effect, repeated measurements and observing many characteristics simultaneously. We develop maximum likelihood estimator (MLE) and uniform minimum variance unbiased estimator (UMVUE) of the reliability which in clinical trial studies could be considered as the chances of increased side effects due to a particular procedure compared to another. The results developed apply to both univariate and multivariate situations. Also, for the univariate situations we develop simple to use lower confidence bounds for the reliability. Further, we consider the cases when both stress and strength constitute time dependent processes. We define the future reliability and obtain methods of constructing lower confidence bounds for this reliability. Finally, we conduct simulation studies to evaluate all the procedures developed and also to compare the MLE and the UMVUE.

Cosmic Microwave Background Mapmaking with a Messenger Field

NASA Astrophysics Data System (ADS)

Huffenberger, Kevin M.; Næss, Sigurd K.

2018-01-01

We apply a messenger field method to solve the linear minimum-variance mapmaking equation in the context of Cosmic Microwave Background (CMB) observations. In simulations, the method produces sky maps that converge significantly faster than those from a conjugate gradient descent algorithm with a diagonal preconditioner, even though the computational cost per iteration is similar. The messenger method recovers large scales in the map better than conjugate gradient descent, and yields a lower overall χ2. In the single, pencil beam approximation, each iteration of the messenger mapmaking procedure produces an unbiased map, and the iterations become more optimal as they proceed. A variant of the method can handle differential data or perform deconvolution mapmaking. The messenger method requires no preconditioner, but a high-quality solution needs a cooling parameter to control the convergence. We study the convergence properties of this new method and discuss how the algorithm is feasible for the large data sets of current and future CMB experiments.

The applicability of ordinary least squares to consistently short distances between taxa in phylogenetic tree construction and the normal distribution test consequences.

PubMed

Roux, C Z

2009-05-01

Short phylogenetic distances between taxa occur, for example, in studies on ribosomal RNA-genes with slow substitution rates. For consistently short distances, it is proved that in the completely singular limit of the covariance matrix ordinary least squares (OLS) estimates are minimum variance or best linear unbiased (BLU) estimates of phylogenetic tree branch lengths. Although OLS estimates are in this situation equal to generalized least squares (GLS) estimates, the GLS chi-square likelihood ratio test will be inapplicable as it is associated with zero degrees of freedom. Consequently, an OLS normal distribution test or an analogous bootstrap approach will provide optimal branch length tests of significance for consistently short phylogenetic distances. As the asymptotic covariances between branch lengths will be equal to zero, it follows that the product rule can be used in tree evaluation to calculate an approximate simultaneous confidence probability that all interior branches are positive.

Fast State-Space Methods for Inferring Dendritic Synaptic Connectivity

DTIC Science & Technology

2013-08-08

the results of 100 simulations with the same parameters as in Figures 4 and 5. As expected, the LARS/LARS+ results are (downward) biased and have low...with a strength slightly biased toward lower values. To measure the variability of the results across the 20 simulations , we computed for each...are downward biased and have low variance, and the OLS results are unbiased but have high variance. Note that for LARS+ the values above the median are

Some New Results on Grubbs’ Estimators.

DTIC Science & Technology

1983-06-01

8217 ESTIMATORS DENNIS A. BRINDLEY AND RALPH A. BRADLEY* Consider a two-way classification with n rows and r columns and the usual model of analysis of variance...except that the error components of the model may have heterogeneous variances, by columns. -Grubbs provided unbiased estimators Q. of a . that depend...of observations yij, i = 1, ... , n, j 1, ... , r, and the model , Yij = Ili + ij + Ej, (1) when Vi represents the mean response of row i, . represents

A weighted least squares estimation of the polynomial regression model on paddy production in the area of Kedah and Perlis

NASA Astrophysics Data System (ADS)

Musa, Rosliza; Ali, Zalila; Baharum, Adam; Nor, Norlida Mohd

2017-08-01

The linear regression model assumes that all random error components are identically and independently distributed with constant variance. Hence, each data point provides equally precise information about the deterministic part of the total variation. In other words, the standard deviations of the error terms are constant over all values of the predictor variables. When the assumption of constant variance is violated, the ordinary least squares estimator of regression coefficient lost its property of minimum variance in the class of linear and unbiased estimators. Weighted least squares estimation are often used to maximize the efficiency of parameter estimation. A procedure that treats all of the data equally would give less precisely measured points more influence than they should have and would give highly precise points too little influence. Optimizing the weighted fitting criterion to find the parameter estimates allows the weights to determine the contribution of each observation to the final parameter estimates. This study used polynomial model with weighted least squares estimation to investigate paddy production of different paddy lots based on paddy cultivation characteristics and environmental characteristics in the area of Kedah and Perlis. The results indicated that factors affecting paddy production are mixture fertilizer application cycle, average temperature, the squared effect of average rainfall, the squared effect of pest and disease, the interaction between acreage with amount of mixture fertilizer, the interaction between paddy variety and NPK fertilizer application cycle and the interaction between pest and disease and NPK fertilizer application cycle.

Estimating fluvial wood discharge from timelapse photography with varying sampling intervals

NASA Astrophysics Data System (ADS)

Anderson, N. K.

2013-12-01

There is recent focus on calculating wood budgets for streams and rivers to help inform management decisions, ecological studies and carbon/nutrient cycling models. Most work has measured in situ wood in temporary storage along stream banks or estimated wood inputs from banks. Little effort has been employed monitoring and quantifying wood in transport during high flows. This paper outlines a procedure for estimating total seasonal wood loads using non-continuous coarse interval sampling and examines differences in estimation between sampling at 1, 5, 10 and 15 minutes. Analysis is performed on wood transport for the Slave River in Northwest Territories, Canada. Relative to the 1 minute dataset, precision decreased by 23%, 46% and 60% for the 5, 10 and 15 minute datasets, respectively. Five and 10 minute sampling intervals provided unbiased equal variance estimates of 1 minute sampling, whereas 15 minute intervals were biased towards underestimation by 6%. Stratifying estimates by day and by discharge increased precision over non-stratification by 4% and 3%, respectively. Not including wood transported during ice break-up, the total minimum wood load estimated at this site is 3300 × 800$ m3 for the 2012 runoff season. The vast majority of the imprecision in total wood volumes came from variance in estimating average volume per log. Comparison of proportions and variance across sample intervals using bootstrap sampling to achieve equal n. Each trial was sampled for n=100, 10,000 times and averaged. All trials were then averaged to obtain an estimate for each sample interval. Dashed lines represent values from the one minute dataset.

A sampling strategy to estimate the area and perimeter of irregularly shaped planar regions

Treesearch

Timothy G. Gregoire; Harry T. Valentine

1995-01-01

The length of a randomly oriented ray emanating from an interior point of a planar region can be used to unbiasedly estimate the region's area and perimeter. Estimators and corresponding variance estimators under various selection strategies are presented.

Estimation of distribution overlap of urn models.

PubMed

Hampton, Jerrad; Lladser, Manuel E

2012-01-01

A classical problem in statistics is estimating the expected coverage of a sample, which has had applications in gene expression, microbial ecology, optimization, and even numismatics. Here we consider a related extension of this problem to random samples of two discrete distributions. Specifically, we estimate what we call the dissimilarity probability of a sample, i.e., the probability of a draw from one distribution not being observed in [Formula: see text] draws from another distribution. We show our estimator of dissimilarity to be a [Formula: see text]-statistic and a uniformly minimum variance unbiased estimator of dissimilarity over the largest appropriate range of [Formula: see text]. Furthermore, despite the non-Markovian nature of our estimator when applied sequentially over [Formula: see text], we show it converges uniformly in probability to the dissimilarity parameter, and we present criteria when it is approximately normally distributed and admits a consistent jackknife estimator of its variance. As proof of concept, we analyze V35 16S rRNA data to discern between various microbial environments. Other potential applications concern any situation where dissimilarity of two discrete distributions may be of interest. For instance, in SELEX experiments, each urn could represent a random RNA pool and each draw a possible solution to a particular binding site problem over that pool. The dissimilarity of these pools is then related to the probability of finding binding site solutions in one pool that are absent in the other.

Estimating total suspended sediment yield with probability sampling

Treesearch

Robert B. Thomas

1985-01-01

The ""Selection At List Time"" (SALT) scheme controls sampling of concentration for estimating total suspended sediment yield. The probability of taking a sample is proportional to its estimated contribution to total suspended sediment discharge. This procedure gives unbiased estimates of total suspended sediment yield and the variance of the...

Distribution of kriging errors, the implications and how to communicate them

NASA Astrophysics Data System (ADS)

Li, Hong Yi; Milne, Alice; Webster, Richard

2016-04-01

Kriging in one form or another has become perhaps the most popular method for spatial prediction in environmental science. Each prediction is unbiased and of minimum variance, which itself is estimated. The kriging variances depend on the mathematical model chosen to describe the spatial variation; different models, however plausible, give rise to different minimized variances. Practitioners often compare models by so-called cross-validation before finally choosing the most appropriate for their kriging. One proceeds as follows. One removes a unit (a sampling point) from the whole set, kriges the value there and compares the kriged value with the value observed to obtain the deviation or error. One repeats the process for each and every point in turn and for all plausible models. One then computes the mean errors (MEs) and the mean of the squared errors (MSEs). Ideally a squared error should equal the corresponding kriging variance (σK2), and so one is advised to choose the model for which on average the squared errors most nearly equal the kriging variances, i.e. the ratio MSDR = MSE/σK2 ≈ 1. Maximum likelihood estimation of models almost guarantees that the MSDR equals 1, and so the kriging variances are unbiased predictors of the squared error across the region. The method is based on the assumption that the errors have a normal distribution. The squared deviation ratio (SDR) should therefore be distributed as χ2 with one degree of freedom with a median of 0.455. We have found that often the median of the SDR (MedSDR) is less, in some instances much less, than 0.455 even though the mean of the SDR is close to 1. It seems that in these cases the distributions of the errors are leptokurtic, i.e. they have an excess of predictions close to the true values, excesses near the extremes and a dearth of predictions in between. In these cases the kriging variances are poor measures of the uncertainty at individual sites. The uncertainty is typically under-estimated for the extreme observations and compensated for by over estimating for other observations. Statisticians must tell users when they present maps of predictions. We illustrate the situation with results from mapping salinity in land reclaimed from the Yangtze delta in the Gulf of Hangzhou, China. There the apparent electrical conductivity (ECa) of the topsoil was measured at 525 points in a field of 2.3 ha. The marginal distribution of the observations was strongly positively skewed, and so the observed ECas were transformed to their logarithms to give an approximately symmetric distribution. That distribution was strongly platykurtic with short tails and no evident outliers. The logarithms were analysed as a mixed model of quadratic drift plus correlated random residuals with a spherical variogram. The kriged predictions that deviated from their true values with an MSDR of 0.993, but with a medSDR=0.324. The coefficient of kurtosis of the deviations was 1.45, i.e. substantially larger than 0 for a normal distribution. The reasons for this behaviour are being sought. The most likely explanation is that there are spatial outliers, i.e. points at which the observed values that differ markedly from those at their their closest neighbours.

Distribution of kriging errors, the implications and how to communicate them

NASA Astrophysics Data System (ADS)

Li, HongYi; Milne, Alice; Webster, Richard

2015-04-01

Kriging in one form or another has become perhaps the most popular method for spatial prediction in environmental science. Each prediction is unbiased and of minimum variance, which itself is estimated. The kriging variances depend on the mathematical model chosen to describe the spatial variation; different models, however plausible, give rise to different minimized variances. Practitioners often compare models by so-called cross-validation before finally choosing the most appropriate for their kriging. One proceeds as follows. One removes a unit (a sampling point) from the whole set, kriges the value there and compares the kriged value with the value observed to obtain the deviation or error. One repeats the process for each and every point in turn and for all plausible models. One then computes the mean errors (MEs) and the mean of the squared errors (MSEs). Ideally a squared error should equal the corresponding kriging variance (σ_K^2), and so one is advised to choose the model for which on average the squared errors most nearly equal the kriging variances, i.e. the ratio MSDR=MSE/ σ_K2 ≈1. Maximum likelihood estimation of models almost guarantees that the MSDR equals 1, and so the kriging variances are unbiased predictors of the squared error across the region. The method is based on the assumption that the errors have a normal distribution. The squared deviation ratio (SDR) should therefore be distributed as χ2 with one degree of freedom with a median of 0.455. We have found that often the median of the SDR (MedSDR) is less, in some instances much less, than 0.455 even though the mean of the SDR is close to 1. It seems that in these cases the distributions of the errors are leptokurtic, i.e. they have an excess of predictions close to the true values, excesses near the extremes and a dearth of predictions in between. In these cases the kriging variances are poor measures of the uncertainty at individual sites. The uncertainty is typically under-estimated for the extreme observations and compensated for by over estimating for other observations. Statisticians must tell users when they present maps of predictions. We illustrate the situation with results from mapping salinity in land reclaimed from the Yangtze delta in the Gulf of Hangzhou, China. There the apparent electrical conductivity (EC_a) of the topsoil was measured at 525 points in a field of 2.3~ha. The marginal distribution of the observations was strongly positively skewed, and so the observed EC_as were transformed to their logarithms to give an approximately symmetric distribution. That distribution was strongly platykurtic with short tails and no evident outliers. The logarithms were analysed as a mixed model of quadratic drift plus correlated random residuals with a spherical variogram. The kriged predictions that deviated from their true values with an MSDR of 0.993, but with a medSDR=0.324. The coefficient of kurtosis of the deviations was 1.45, i.e. substantially larger than 0 for a normal distribution. The reasons for this behaviour are being sought. The most likely explanation is that there are spatial outliers, i.e. points at which the observed values that differ markedly from those at their their closest neighbours.

Variances and uncertainties of the sample laboratory-to-laboratory variance (S(L)2) and standard deviation (S(L)) associated with an interlaboratory study.

PubMed

McClure, Foster D; Lee, Jung K

2012-01-01

The validation process for an analytical method usually employs an interlaboratory study conducted as a balanced completely randomized model involving a specified number of randomly chosen laboratories, each analyzing a specified number of randomly allocated replicates. For such studies, formulas to obtain approximate unbiased estimates of the variance and uncertainty of the sample laboratory-to-laboratory (lab-to-lab) STD (S(L)) have been developed primarily to account for the uncertainty of S(L) when there is a need to develop an uncertainty budget that includes the uncertainty of S(L). For the sake of completeness on this topic, formulas to estimate the variance and uncertainty of the sample lab-to-lab variance (S(L)2) were also developed. In some cases, it was necessary to derive the formulas based on an approximate distribution for S(L)2.

Current and efficiency of Brownian particles under oscillating forces in entropic barriers

NASA Astrophysics Data System (ADS)

Nutku, Ferhat; Aydιner, Ekrem

2015-04-01

In this study, considering the temporarily unbiased force and different forms of oscillating forces, we investigate the current and efficiency of Brownian particles in an entropic tube structure and present the numerically obtained results. We show that different force forms give rise to different current and efficiency profiles in different optimized parameter intervals. We find that an unbiased oscillating force and an unbiased temporal force lead to the current and efficiency, which are dependent on these parameters. We also observe that the current and efficiency caused by temporal and different oscillating forces have maximum and minimum values in different parameter intervals. We conclude that the current or efficiency can be controlled dynamically by adjusting the parameters of entropic barriers and applied force. Project supported by the Funds from Istanbul University (Grant No. 45662).

Regression sampling: some results for resource managers and researchers

Treesearch

William G. O' Regan; Robert W. Boyd

1974-01-01

Regression sampling is widely used in natural resources management and research to estimate quantities of resources per unit area. This note brings together results found in the statistical literature in the application of this sampling technique. Conditional and unconditional estimators are listed and for each estimator, exact variances and unbiased estimators for the...

75 FR 40797 - Upper Peninsula Power Company; Notice of Application for Temporary Amendment of License and...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-07-14

... for drought-based temporary variance of the reservoir elevations and minimum flow releases at the Dead... temporary variance to the reservoir elevation and minimum flow requirements at the Hoist Development. The...: (1) Releasing a minimum flow of 75 cubic feet per second (cfs) from the Hoist Reservoir, instead of...

Estimation of genetic parameters and response to selection for a continuous trait subject to culling before testing.

PubMed

Arnason, T; Albertsdóttir, E; Fikse, W F; Eriksson, S; Sigurdsson, A

2012-02-01

The consequences of assuming a zero environmental covariance between a binary trait 'test-status' and a continuous trait on the estimates of genetic parameters by restricted maximum likelihood and Gibbs sampling and on response from genetic selection when the true environmental covariance deviates from zero were studied. Data were simulated for two traits (one that culling was based on and a continuous trait) using the following true parameters, on the underlying scale: h² = 0.4; r(A) = 0.5; r(E) = 0.5, 0.0 or -0.5. The selection on the continuous trait was applied to five subsequent generations where 25 sires and 500 dams produced 1500 offspring per generation. Mass selection was applied in the analysis of the effect on estimation of genetic parameters. Estimated breeding values were used in the study of the effect of genetic selection on response and accuracy. The culling frequency was either 0.5 or 0.8 within each generation. Each of 10 replicates included 7500 records on 'test-status' and 9600 animals in the pedigree file. Results from bivariate analysis showed unbiased estimates of variance components and genetic parameters when true r(E) = 0.0. For r(E) = 0.5, variance components (13-19% bias) and especially (50-80%) were underestimated for the continuous trait, while heritability estimates were unbiased. For r(E) = -0.5, heritability estimates of test-status were unbiased, while genetic variance and heritability of the continuous trait together with were overestimated (25-50%). The bias was larger for the higher culling frequency. Culling always reduced genetic progress from selection, but the genetic progress was found to be robust to the use of wrong parameter values of the true environmental correlation between test-status and the continuous trait. Use of a bivariate linear-linear model reduced bias in genetic evaluations, when data were subject to culling. © 2011 Blackwell Verlag GmbH.

A UNIFIED FRAMEWORK FOR VARIANCE COMPONENT ESTIMATION WITH SUMMARY STATISTICS IN GENOME-WIDE ASSOCIATION STUDIES.

PubMed

Zhou, Xiang

2017-12-01

Linear mixed models (LMMs) are among the most commonly used tools for genetic association studies. However, the standard method for estimating variance components in LMMs-the restricted maximum likelihood estimation method (REML)-suffers from several important drawbacks: REML requires individual-level genotypes and phenotypes from all samples in the study, is computationally slow, and produces downward-biased estimates in case control studies. To remedy these drawbacks, we present an alternative framework for variance component estimation, which we refer to as MQS. MQS is based on the method of moments (MoM) and the minimal norm quadratic unbiased estimation (MINQUE) criterion, and brings two seemingly unrelated methods-the renowned Haseman-Elston (HE) regression and the recent LD score regression (LDSC)-into the same unified statistical framework. With this new framework, we provide an alternative but mathematically equivalent form of HE that allows for the use of summary statistics. We provide an exact estimation form of LDSC to yield unbiased and statistically more efficient estimates. A key feature of our method is its ability to pair marginal z -scores computed using all samples with SNP correlation information computed using a small random subset of individuals (or individuals from a proper reference panel), while capable of producing estimates that can be almost as accurate as if both quantities are computed using the full data. As a result, our method produces unbiased and statistically efficient estimates, and makes use of summary statistics, while it is computationally efficient for large data sets. Using simulations and applications to 37 phenotypes from 8 real data sets, we illustrate the benefits of our method for estimating and partitioning SNP heritability in population studies as well as for heritability estimation in family studies. Our method is implemented in the GEMMA software package, freely available at www.xzlab.org/software.html.

MRMC analysis of agreement studies

NASA Astrophysics Data System (ADS)

Gallas, Brandon D.; Anam, Amrita; Chen, Weijie; Wunderlich, Adam; Zhang, Zhiwei

2016-03-01

The purpose of this work is to present and evaluate methods based on U-statistics to compare intra- or inter-reader agreement across different imaging modalities. We apply these methods to multi-reader multi-case (MRMC) studies. We measure reader-averaged agreement and estimate its variance accounting for the variability from readers and cases (an MRMC analysis). In our application, pathologists (readers) evaluate patient tissue mounted on glass slides (cases) in two ways. They evaluate the slides on a microscope (reference modality) and they evaluate digital scans of the slides on a computer display (new modality). In the current work, we consider concordance as the agreement measure, but many of the concepts outlined here apply to other agreement measures. Concordance is the probability that two readers rank two cases in the same order. Concordance can be estimated with a U-statistic and thus it has some nice properties: it is unbiased, asymptotically normal, and its variance is given by an explicit formula. Another property of a U-statistic is that it is symmetric in its inputs; it doesn't matter which reader is listed first or which case is listed first, the result is the same. Using this property and a few tricks while building the U-statistic kernel for concordance, we get a mathematically tractable problem and efficient software. Simulations show that our variance and covariance estimates are unbiased.

Uncertainty relation based on unbiased parameter estimations

NASA Astrophysics Data System (ADS)

Sun, Liang-Liang; Song, Yong-Shun; Qiao, Cong-Feng; Yu, Sixia; Chen, Zeng-Bing

2017-02-01

Heisenberg's uncertainty relation has been extensively studied in spirit of its well-known original form, in which the inaccuracy measures used exhibit some controversial properties and don't conform with quantum metrology, where the measurement precision is well defined in terms of estimation theory. In this paper, we treat the joint measurement of incompatible observables as a parameter estimation problem, i.e., estimating the parameters characterizing the statistics of the incompatible observables. Our crucial observation is that, in a sequential measurement scenario, the bias induced by the first unbiased measurement in the subsequent measurement can be eradicated by the information acquired, allowing one to extract unbiased information of the second measurement of an incompatible observable. In terms of Fisher information we propose a kind of information comparison measure and explore various types of trade-offs between the information gains and measurement precisions, which interpret the uncertainty relation as surplus variance trade-off over individual perfect measurements instead of a constraint on extracting complete information of incompatible observables.

An evaluation of flow-stratified sampling for estimating suspended sediment loads

Treesearch

Robert B. Thomas; Jack Lewis

1995-01-01

Abstract - Flow-stratified sampling is a new method for sampling water quality constituents such as suspended sediment to estimate loads. As with selection-at-list-time (SALT) and time-stratified sampling, flow-stratified sampling is a statistical method requiring random sampling, and yielding unbiased estimates of load and variance. It can be used to estimate event...

Roofing: Don't Let What's Over Head Kill Your Bottom Line.

ERIC Educational Resources Information Center

Shannon, James W., Jr.

1983-01-01

A Colorado school district employs a professional consulting firm to give an unbiased opinion on the district's roofing needs. Built-up, single-ply, and modified asphalt roofing systems have all been utilized. Preventive maintenance keeps roofing bills to a minimum. (MLF)

The Variance of Solar Wind Magnetic Fluctuations: Solutions and Further Puzzles

NASA Technical Reports Server (NTRS)

Roberts, D. A.; Goldstein, M. L.

2006-01-01

We study the dependence of the variance directions of the magnetic field in the solar wind as a function of scale, radial distance, and Alfvenicity. The study resolves the question of why different studies have arrived at widely differing values for the maximum to minimum power (approximately equal to 3:1 up to approximately equal to 20:1). This is due to the decreasing anisotropy with increasing time interval chosen for the variance, and is a direct result of the "spherical polarization" of the waves which follows from the near constancy of |B|. The reason for the magnitude preserving evolution is still unresolved. Moreover, while the long-known tendency for the minimum variance to lie along the mean field also follows from this view (as shown by Barnes many years ago), there is no theory for why the minimum variance follows the field direction as the Parker angle changes. We show that this turning is quite generally true in Alfvenic regions over a wide range of heliocentric distances. The fact that nonAlfvenic regions, while still showing strong power anisotropies, tend to have a much broader range of angles between the minimum variance and the mean field makes it unlikely that the cause of the variance turning is to be found in a turbulence mechanism. There are no obvious alternative mechanisms, leaving us with another intriguing puzzle.

Reconsidering Cluster Bias in Multilevel Data: A Monte Carlo Comparison of Free and Constrained Baseline Approaches.

PubMed

Guenole, Nigel

2018-01-01

The test for item level cluster bias examines the improvement in model fit that results from freeing an item's between level residual variance from a baseline model with equal within and between level factor loadings and between level residual variances fixed at zero. A potential problem is that this approach may include a misspecified unrestricted model if any non-invariance is present, but the log-likelihood difference test requires that the unrestricted model is correctly specified. A free baseline approach where the unrestricted model includes only the restrictions needed for model identification should lead to better decision accuracy, but no studies have examined this yet. We ran a Monte Carlo study to investigate this issue. When the referent item is unbiased, compared to the free baseline approach, the constrained baseline approach led to similar true positive (power) rates but much higher false positive (Type I error) rates. The free baseline approach should be preferred when the referent indicator is unbiased. When the referent assumption is violated, the false positive rate was unacceptably high for both free and constrained baseline approaches, and the true positive rate was poor regardless of whether the free or constrained baseline approach was used. Neither the free or constrained baseline approach can be recommended when the referent indicator is biased. We recommend paying close attention to ensuring the referent indicator is unbiased in tests of cluster bias. All Mplus input and output files, R, and short Python scripts used to execute this simulation study are uploaded to an open access repository.

Reconsidering Cluster Bias in Multilevel Data: A Monte Carlo Comparison of Free and Constrained Baseline Approaches

PubMed Central

Guenole, Nigel

2018-01-01

The test for item level cluster bias examines the improvement in model fit that results from freeing an item's between level residual variance from a baseline model with equal within and between level factor loadings and between level residual variances fixed at zero. A potential problem is that this approach may include a misspecified unrestricted model if any non-invariance is present, but the log-likelihood difference test requires that the unrestricted model is correctly specified. A free baseline approach where the unrestricted model includes only the restrictions needed for model identification should lead to better decision accuracy, but no studies have examined this yet. We ran a Monte Carlo study to investigate this issue. When the referent item is unbiased, compared to the free baseline approach, the constrained baseline approach led to similar true positive (power) rates but much higher false positive (Type I error) rates. The free baseline approach should be preferred when the referent indicator is unbiased. When the referent assumption is violated, the false positive rate was unacceptably high for both free and constrained baseline approaches, and the true positive rate was poor regardless of whether the free or constrained baseline approach was used. Neither the free or constrained baseline approach can be recommended when the referent indicator is biased. We recommend paying close attention to ensuring the referent indicator is unbiased in tests of cluster bias. All Mplus input and output files, R, and short Python scripts used to execute this simulation study are uploaded to an open access repository. PMID:29551985

The Misspecification of the Covariance Structures in Multilevel Models for Single-Case Data: A Monte Carlo Simulation Study

ERIC Educational Resources Information Center

Moeyaert, Mariola; Ugille, Maaike; Ferron, John M.; Beretvas, S. Natasha; Van den Noortgate, Wim

2016-01-01

The impact of misspecifying covariance matrices at the second and third levels of the three-level model is evaluated. Results indicate that ignoring existing covariance has no effect on the treatment effect estimate. In addition, the between-case variance estimates are unbiased when covariance is either modeled or ignored. If the research interest…

Identification of multiple leaks in pipeline: Linearized model, maximum likelihood, and super-resolution localization

NASA Astrophysics Data System (ADS)

Wang, Xun; Ghidaoui, Mohamed S.

2018-07-01

This paper considers the problem of identifying multiple leaks in a water-filled pipeline based on inverse transient wave theory. The analytical solution to this problem involves nonlinear interaction terms between the various leaks. This paper shows analytically and numerically that these nonlinear terms are of the order of the leak sizes to the power two and; thus, negligible. As a result of this simplification, a maximum likelihood (ML) scheme that identifies leak locations and leak sizes separately is formulated and tested. It is found that the ML estimation scheme is highly efficient and robust with respect to noise. In addition, the ML method is a super-resolution leak localization scheme because its resolvable leak distance (approximately 0.15λmin , where λmin is the minimum wavelength) is below the Nyquist-Shannon sampling theorem limit (0.5λmin). Moreover, the Cramér-Rao lower bound (CRLB) is derived and used to show the efficiency of the ML scheme estimates. The variance of the ML estimator approximates the CRLB proving that the ML scheme belongs to class of best unbiased estimator of leak localization methods.

Simulation of relationship between river discharge and sediment yield in the semi-arid river watersheds

NASA Astrophysics Data System (ADS)

Khaleghi, Mohammad Reza; Varvani, Javad

2018-02-01

Complex and variable nature of the river sediment yield caused many problems in estimating the long-term sediment yield and problems input into the reservoirs. Sediment Rating Curves (SRCs) are generally used to estimate the suspended sediment load of the rivers and drainage watersheds. Since the regression equations of the SRCs are obtained by logarithmic retransformation and have a little independent variable in this equation, they also overestimate or underestimate the true sediment load of the rivers. To evaluate the bias correction factors in Kalshor and Kashafroud watersheds, seven hydrometric stations of this region with suitable upstream watershed and spatial distribution were selected. Investigation of the accuracy index (ratio of estimated sediment yield to observed sediment yield) and the precision index of different bias correction factors of FAO, Quasi-Maximum Likelihood Estimator (QMLE), Smearing, and Minimum-Variance Unbiased Estimator (MVUE) with LSD test showed that FAO coefficient increases the estimated error in all of the stations. Application of MVUE in linear and mean load rating curves has not statistically meaningful effects. QMLE and smearing factors increased the estimated error in mean load rating curve, but that does not have any effect on linear rating curve estimation.

Accounting for selection and correlation in the analysis of two-stage genome-wide association studies.

PubMed

Robertson, David S; Prevost, A Toby; Bowden, Jack

2016-10-01

The problem of selection bias has long been recognized in the analysis of two-stage trials, where promising candidates are selected in stage 1 for confirmatory analysis in stage 2. To efficiently correct for bias, uniformly minimum variance conditionally unbiased estimators (UMVCUEs) have been proposed for a wide variety of trial settings, but where the population parameter estimates are assumed to be independent. We relax this assumption and derive the UMVCUE in the multivariate normal setting with an arbitrary known covariance structure. One area of application is the estimation of odds ratios (ORs) when combining a genome-wide scan with a replication study. Our framework explicitly accounts for correlated single nucleotide polymorphisms, as might occur due to linkage disequilibrium. We illustrate our approach on the measurement of the association between 11 genetic variants and the risk of Crohn's disease, as reported in Parkes and others (2007. Sequence variants in the autophagy gene IRGM and multiple other replicating loci contribute to Crohn's disease susceptibility. Nat. Gen. 39: (7), 830-832.), and show that the estimated ORs can vary substantially if both selection and correlation are taken into account. © The Author 2016. Published by Oxford University Press.

Sensor networks for optimal target localization with bearings-only measurements in constrained three-dimensional scenarios.

PubMed

Moreno-Salinas, David; Pascoal, Antonio; Aranda, Joaquin

2013-08-12

In this paper, we address the problem of determining the optimal geometric configuration of an acoustic sensor network that will maximize the angle-related information available for underwater target positioning. In the set-up adopted, a set of autonomous vehicles carries a network of acoustic units that measure the elevation and azimuth angles between a target and each of the receivers on board the vehicles. It is assumed that the angle measurements are corrupted by white Gaussian noise, the variance of which is distance-dependent. Using tools from estimation theory, the problem is converted into that of minimizing, by proper choice of the sensor positions, the trace of the inverse of the Fisher Information Matrix (also called the Cramer-Rao Bound matrix) to determine the sensor configuration that yields the minimum possible covariance of any unbiased target estimator. It is shown that the optimal configuration of the sensors depends explicitly on the intensity of the measurement noise, the constraints imposed on the sensor configuration, the target depth and the probabilistic distribution that defines the prior uncertainty in the target position. Simulation examples illustrate the key results derived.

Noncommuting observables in quantum detection and estimation theory

NASA Technical Reports Server (NTRS)

Helstrom, C. W.

1972-01-01

Basing decisions and estimates on simultaneous approximate measurements of noncommuting observables in a quantum receiver is shown to be equivalent to measuring commuting projection operators on a larger Hilbert space than that of the receiver itself. The quantum-mechanical Cramer-Rao inequalities derived from right logarithmic derivatives and symmetrized logarithmic derivatives of the density operator are compared, and it is shown that the latter give superior lower bounds on the error variances of individual unbiased estimates of arrival time and carrier frequency of a coherent signal. For a suitably weighted sum of the error variances of simultaneous estimates of these, the former yield the superior lower bound under some conditions.

Portfolio optimization with mean-variance model

NASA Astrophysics Data System (ADS)

Hoe, Lam Weng; Siew, Lam Weng

2016-06-01

Investors wish to achieve the target rate of return at the minimum level of risk in their investment. Portfolio optimization is an investment strategy that can be used to minimize the portfolio risk and can achieve the target rate of return. The mean-variance model has been proposed in portfolio optimization. The mean-variance model is an optimization model that aims to minimize the portfolio risk which is the portfolio variance. The objective of this study is to construct the optimal portfolio using the mean-variance model. The data of this study consists of weekly returns of 20 component stocks of FTSE Bursa Malaysia Kuala Lumpur Composite Index (FBMKLCI). The results of this study show that the portfolio composition of the stocks is different. Moreover, investors can get the return at minimum level of risk with the constructed optimal mean-variance portfolio.

Unbiased multi-fidelity estimate of failure probability of a free plane jet

NASA Astrophysics Data System (ADS)

Marques, Alexandre; Kramer, Boris; Willcox, Karen; Peherstorfer, Benjamin

2017-11-01

Estimating failure probability related to fluid flows is a challenge because it requires a large number of evaluations of expensive models. We address this challenge by leveraging multiple low fidelity models of the flow dynamics to create an optimal unbiased estimator. In particular, we investigate the effects of uncertain inlet conditions in the width of a free plane jet. We classify a condition as failure when the corresponding jet width is below a small threshold, such that failure is a rare event (failure probability is smaller than 0.001). We estimate failure probability by combining the frameworks of multi-fidelity importance sampling and optimal fusion of estimators. Multi-fidelity importance sampling uses a low fidelity model to explore the parameter space and create a biasing distribution. An unbiased estimate is then computed with a relatively small number of evaluations of the high fidelity model. In the presence of multiple low fidelity models, this framework offers multiple competing estimators. Optimal fusion combines all competing estimators into a single estimator with minimal variance. We show that this combined framework can significantly reduce the cost of estimating failure probabilities, and thus can have a large impact in fluid flow applications. This work was funded by DARPA.

[Analytic methods for seed models with genotype x environment interactions].

PubMed

Zhu, J

1996-01-01

Genetic models with genotype effect (G) and genotype x environment interaction effect (GE) are proposed for analyzing generation means of seed quantitative traits in crops. The total genetic effect (G) is partitioned into seed direct genetic effect (G0), cytoplasm genetic of effect (C), and maternal plant genetic effect (Gm). Seed direct genetic effect (G0) can be further partitioned into direct additive (A) and direct dominance (D) genetic components. Maternal genetic effect (Gm) can also be partitioned into maternal additive (Am) and maternal dominance (Dm) genetic components. The total genotype x environment interaction effect (GE) can also be partitioned into direct genetic by environment interaction effect (G0E), cytoplasm genetic by environment interaction effect (CE), and maternal genetic by environment interaction effect (GmE). G0E can be partitioned into direct additive by environment interaction (AE) and direct dominance by environment interaction (DE) genetic components. GmE can also be partitioned into maternal additive by environment interaction (AmE) and maternal dominance by environment interaction (DmE) genetic components. Partitions of genetic components are listed for parent, F1, F2 and backcrosses. A set of parents, their reciprocal F1 and F2 seeds is applicable for efficient analysis of seed quantitative traits. MINQUE(0/1) method can be used for estimating variance and covariance components. Unbiased estimation for covariance components between two traits can also be obtained by the MINQUE(0/1) method. Random genetic effects in seed models are predictable by the Adjusted Unbiased Prediction (AUP) approach with MINQUE(0/1) method. The jackknife procedure is suggested for estimation of sampling variances of estimated variance and covariance components and of predicted genetic effects, which can be further used in a t-test for parameter. Unbiasedness and efficiency for estimating variance components and predicting genetic effects are tested by Monte Carlo simulations.

76 FR 1145 - Alabama Power Company; Notice of Application for Amendment of License and Soliciting Comments...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-01-07

... drought-based temporary variance of the Martin Project rule curve and minimum flow releases at the Yates... requesting a drought- based temporary variance to the Martin Project rule curve. The rule curve variance...

Minimizing Statistical Bias with Queries.

DTIC Science & Technology

1995-09-14

method for optimally selecting these points would o er enormous savings in time and money. An active learning system will typically attempt to select data...research in active learning assumes that the sec- ond term of Equation 2 is approximately zero, that is, that the learner is unbiased. If this is the case...outperforms the variance- minimizing algorithm and random exploration. and e ective strategy for active learning . I have given empirical evidence that, with

On the additive and dominant variance and covariance of individuals within the genomic selection scope.

PubMed

Vitezica, Zulma G; Varona, Luis; Legarra, Andres

2013-12-01

Genomic evaluation models can fit additive and dominant SNP effects. Under quantitative genetics theory, additive or "breeding" values of individuals are generated by substitution effects, which involve both "biological" additive and dominant effects of the markers. Dominance deviations include only a portion of the biological dominant effects of the markers. Additive variance includes variation due to the additive and dominant effects of the markers. We describe a matrix of dominant genomic relationships across individuals, D, which is similar to the G matrix used in genomic best linear unbiased prediction. This matrix can be used in a mixed-model context for genomic evaluations or to estimate dominant and additive variances in the population. From the "genotypic" value of individuals, an alternative parameterization defines additive and dominance as the parts attributable to the additive and dominant effect of the markers. This approach underestimates the additive genetic variance and overestimates the dominance variance. Transforming the variances from one model into the other is trivial if the distribution of allelic frequencies is known. We illustrate these results with mouse data (four traits, 1884 mice, and 10,946 markers) and simulated data (2100 individuals and 10,000 markers). Variance components were estimated correctly in the model, considering breeding values and dominance deviations. For the model considering genotypic values, the inclusion of dominant effects biased the estimate of additive variance. Genomic models were more accurate for the estimation of variance components than their pedigree-based counterparts.

Effects of sample size on estimates of population growth rates calculated with matrix models.

PubMed

Fiske, Ian J; Bruna, Emilio M; Bolker, Benjamin M

2008-08-28

Matrix models are widely used to study the dynamics and demography of populations. An important but overlooked issue is how the number of individuals sampled influences estimates of the population growth rate (lambda) calculated with matrix models. Even unbiased estimates of vital rates do not ensure unbiased estimates of lambda-Jensen's Inequality implies that even when the estimates of the vital rates are accurate, small sample sizes lead to biased estimates of lambda due to increased sampling variance. We investigated if sampling variability and the distribution of sampling effort among size classes lead to biases in estimates of lambda. Using data from a long-term field study of plant demography, we simulated the effects of sampling variance by drawing vital rates and calculating lambda for increasingly larger populations drawn from a total population of 3842 plants. We then compared these estimates of lambda with those based on the entire population and calculated the resulting bias. Finally, we conducted a review of the literature to determine the sample sizes typically used when parameterizing matrix models used to study plant demography. We found significant bias at small sample sizes when survival was low (survival = 0.5), and that sampling with a more-realistic inverse J-shaped population structure exacerbated this bias. However our simulations also demonstrate that these biases rapidly become negligible with increasing sample sizes or as survival increases. For many of the sample sizes used in demographic studies, matrix models are probably robust to the biases resulting from sampling variance of vital rates. However, this conclusion may depend on the structure of populations or the distribution of sampling effort in ways that are unexplored. We suggest more intensive sampling of populations when individual survival is low and greater sampling of stages with high elasticities.

A sibling method for identifying vQTLs

PubMed Central

Domingue, Ben; Dawes, Christopher; Boardman, Jason; Siegal, Mark

2018-01-01

The propensity of a trait to vary within a population may have evolutionary, ecological, or clinical significance. In the present study we deploy sibling models to offer a novel and unbiased way to ascertain loci associated with the extent to which phenotypes vary (variance-controlling quantitative trait loci, or vQTLs). Previous methods for vQTL-mapping either exclude genetically related individuals or treat genetic relatedness among individuals as a complicating factor addressed by adjusting estimates for non-independence in phenotypes. The present method uses genetic relatedness as a tool to obtain unbiased estimates of variance effects rather than as a nuisance. The family-based approach, which utilizes random variation between siblings in minor allele counts at a locus, also allows controls for parental genotype, mean effects, and non-linear (dominance) effects that may spuriously appear to generate variation. Simulations show that the approach performs equally well as two existing methods (squared Z-score and DGLM) in controlling type I error rates when there is no unobserved confounding, and performs significantly better than these methods in the presence of small degrees of confounding. Using height and BMI as empirical applications, we investigate SNPs that alter within-family variation in height and BMI, as well as pathways that appear to be enriched. One significant SNP for BMI variability, in the MAST4 gene, replicated. Pathway analysis revealed one gene set, encoding members of several signaling pathways related to gap junction function, which appears significantly enriched for associations with within-family height variation in both datasets (while not enriched in analysis of mean levels). We recommend approximating laboratory random assignment of genotype using family data and more careful attention to the possible conflation of mean and variance effects. PMID:29617452

Host Genome Influence on Gut Microbial Composition and Microbial Prediction of Complex Traits in Pigs.

PubMed

Camarinha-Silva, Amelia; Maushammer, Maria; Wellmann, Robin; Vital, Marius; Preuss, Siegfried; Bennewitz, Jörn

2017-07-01

The aim of the present study was to analyze the interplay between gastrointestinal tract (GIT) microbiota, host genetics, and complex traits in pigs using extended quantitative-genetic methods. The study design consisted of 207 pigs that were housed and slaughtered under standardized conditions, and phenotyped for daily gain, feed intake, and feed conversion rate. The pigs were genotyped with a standard 60 K SNP chip. The GIT microbiota composition was analyzed by 16S rRNA gene amplicon sequencing technology. Eight from 49 investigated bacteria genera showed a significant narrow sense host heritability, ranging from 0.32 to 0.57. Microbial mixed linear models were applied to estimate the microbiota variance for each complex trait. The fraction of phenotypic variance explained by the microbial variance was 0.28, 0.21, and 0.16 for daily gain, feed conversion, and feed intake, respectively. The SNP data and the microbiota composition were used to predict the complex traits using genomic best linear unbiased prediction (G-BLUP) and microbial best linear unbiased prediction (M-BLUP) methods, respectively. The prediction accuracies of G-BLUP were 0.35, 0.23, and 0.20 for daily gain, feed conversion, and feed intake, respectively. The corresponding prediction accuracies of M-BLUP were 0.41, 0.33, and 0.33. Thus, in addition to SNP data, microbiota abundances are an informative source of complex trait predictions. Since the pig is a well-suited animal for modeling the human digestive tract, M-BLUP, in addition to G-BLUP, might be beneficial for predicting human predispositions to some diseases, and, consequently, for preventative and personalized medicine. Copyright © 2017 by the Genetics Society of America.

The Efficiency of Split Panel Designs in an Analysis of Variance Model

PubMed Central

Wang, Wei-Guo; Liu, Hai-Jun

2016-01-01

We consider split panel design efficiency in analysis of variance models, that is, the determination of the cross-sections series optimal proportion in all samples, to minimize parametric best linear unbiased estimators of linear combination variances. An orthogonal matrix is constructed to obtain manageable expression of variances. On this basis, we derive a theorem for analyzing split panel design efficiency irrespective of interest and budget parameters. Additionally, relative estimator efficiency based on the split panel to an estimator based on a pure panel or a pure cross-section is present. The analysis shows that the gains from split panel can be quite substantial. We further consider the efficiency of split panel design, given a budget, and transform it to a constrained nonlinear integer programming. Specifically, an efficient algorithm is designed to solve the constrained nonlinear integer programming. Moreover, we combine one at time designs and factorial designs to illustrate the algorithm’s efficiency with an empirical example concerning monthly consumer expenditure on food in 1985, in the Netherlands, and the efficient ranges of the algorithm parameters are given to ensure a good solution. PMID:27163447

Superresolution SAR Imaging Algorithm Based on Mvm and Weighted Norm Extrapolation

NASA Astrophysics Data System (ADS)

Zhang, P.; Chen, Q.; Li, Z.; Tang, Z.; Liu, J.; Zhao, L.

2013-08-01

In this paper, we present an extrapolation approach, which uses minimum weighted norm constraint and minimum variance spectrum estimation, for improving synthetic aperture radar (SAR) resolution. Minimum variance method is a robust high resolution method to estimate spectrum. Based on the theory of SAR imaging, the signal model of SAR imagery is analyzed to be feasible for using data extrapolation methods to improve the resolution of SAR image. The method is used to extrapolate the efficient bandwidth in phase history field and better results are obtained compared with adaptive weighted norm extrapolation (AWNE) method and traditional imaging method using simulated data and actual measured data.

The 'whys' and 'whens' of individual differences in thinking biases.

PubMed

De Neys, Wim; Bonnefon, Jean-François

2013-04-01

Although human thinking is often biased, some individuals are less susceptible to biases than others. These individual differences have been at the forefront of thinking research for more than a decade. We organize the literature in three key accounts (storage, monitoring, and inhibition failure) and propose that a critical but overlooked question concerns the time point at which individual variance arises: do biased and unbiased reasoners take different paths early on in the reasoning process or is the observed variance late to arise? We discuss how this focus on the 'whens' suggests that individual differences in thinking biases are less profound than traditionally assumed, in the sense that they might typically arise at a later stage of the reasoning process. Copyright © 2013 Elsevier Ltd. All rights reserved.

Analysis and application of minimum variance discrete time system identification

NASA Technical Reports Server (NTRS)

Kaufman, H.; Kotob, S.

1975-01-01

An on-line minimum variance parameter identifier is developed which embodies both accuracy and computational efficiency. The formulation results in a linear estimation problem with both additive and multiplicative noise. The resulting filter which utilizes both the covariance of the parameter vector itself and the covariance of the error in identification is proven to be mean square convergent and mean square consistent. The MV parameter identification scheme is then used to construct a stable state and parameter estimation algorithm.

Synthesis of correlation filters: a generalized space-domain approach for improved filter characteristics

NASA Astrophysics Data System (ADS)

Sudharsanan, Subramania I.; Mahalanobis, Abhijit; Sundareshan, Malur K.

1990-12-01

Discrete frequency domain design of Minimum Average Correlation Energy filters for optical pattern recognition introduces an implementational limitation of circular correlation. An alternative methodology which uses space domain computations to overcome this problem is presented. The technique is generalized to construct an improved synthetic discriminant function which satisfies the conflicting requirements of reduced noise variance and sharp correlation peaks to facilitate ease of detection. A quantitative evaluation of the performance characteristics of the new filter is conducted and is shown to compare favorably with the well known Minimum Variance Synthetic Discriminant Function and the space domain Minimum Average Correlation Energy filter, which are special cases of the present design.

Errors in the estimation of the variance: implications for multiple-probability fluctuation analysis.

PubMed

Saviane, Chiara; Silver, R Angus

2006-06-15

Synapses play a crucial role in information processing in the brain. Amplitude fluctuations of synaptic responses can be used to extract information about the mechanisms underlying synaptic transmission and its modulation. In particular, multiple-probability fluctuation analysis can be used to estimate the number of functional release sites, the mean probability of release and the amplitude of the mean quantal response from fits of the relationship between the variance and mean amplitude of postsynaptic responses, recorded at different probabilities. To determine these quantal parameters, calculate their uncertainties and the goodness-of-fit of the model, it is important to weight the contribution of each data point in the fitting procedure. We therefore investigated the errors associated with measuring the variance by determining the best estimators of the variance of the variance and have used simulations of synaptic transmission to test their accuracy and reliability under different experimental conditions. For central synapses, which generally have a low number of release sites, the amplitude distribution of synaptic responses is not normal, thus the use of a theoretical variance of the variance based on the normal assumption is not a good approximation. However, appropriate estimators can be derived for the population and for limited sample sizes using a more general expression that involves higher moments and introducing unbiased estimators based on the h-statistics. Our results are likely to be relevant for various applications of fluctuation analysis when few channels or release sites are present.

Multi-population Genomic Relationships for Estimating Current Genetic Variances Within and Genetic Correlations Between Populations.

PubMed

Wientjes, Yvonne C J; Bijma, Piter; Vandenplas, Jérémie; Calus, Mario P L

2017-10-01

Different methods are available to calculate multi-population genomic relationship matrices. Since those matrices differ in base population, it is anticipated that the method used to calculate genomic relationships affects the estimate of genetic variances, covariances, and correlations. The aim of this article is to define the multi-population genomic relationship matrix to estimate current genetic variances within and genetic correlations between populations. The genomic relationship matrix containing two populations consists of four blocks, one block for population 1, one block for population 2, and two blocks for relationships between the populations. It is known, based on literature, that by using current allele frequencies to calculate genomic relationships within a population, current genetic variances are estimated. In this article, we theoretically derived the properties of the genomic relationship matrix to estimate genetic correlations between populations and validated it using simulations. When the scaling factor of across-population genomic relationships is equal to the product of the square roots of the scaling factors for within-population genomic relationships, the genetic correlation is estimated unbiasedly even though estimated genetic variances do not necessarily refer to the current population. When this property is not met, the correlation based on estimated variances should be multiplied by a correction factor based on the scaling factors. In this study, we present a genomic relationship matrix which directly estimates current genetic variances as well as genetic correlations between populations. Copyright © 2017 by the Genetics Society of America.

A comparison of maximum likelihood and other estimators of eigenvalues from several correlated Monte Carlo samples

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

Beer, M.

1980-12-01

The maximum likelihood method for the multivariate normal distribution is applied to the case of several individual eigenvalues. Correlated Monte Carlo estimates of the eigenvalue are assumed to follow this prescription and aspects of the assumption are examined. Monte Carlo cell calculations using the SAM-CE and VIM codes for the TRX-1 and TRX-2 benchmark reactors, and SAM-CE full core results are analyzed with this method. Variance reductions of a few percent to a factor of 2 are obtained from maximum likelihood estimation as compared with the simple average and the minimum variance individual eigenvalue. The numerical results verify that themore » use of sample variances and correlation coefficients in place of the corresponding population statistics still leads to nearly minimum variance estimation for a sufficient number of histories and aggregates.« less

Applications of active adaptive noise control to jet engines

NASA Technical Reports Server (NTRS)

Shoureshi, Rahmat; Brackney, Larry

1993-01-01

During phase 2 research on the application of active noise control to jet engines, the development of multiple-input/multiple-output (MIMO) active adaptive noise control algorithms and acoustic/controls models for turbofan engines were considered. Specific goals for this research phase included: (1) implementation of a MIMO adaptive minimum variance active noise controller; and (2) turbofan engine model development. A minimum variance control law for adaptive active noise control has been developed, simulated, and implemented for single-input/single-output (SISO) systems. Since acoustic systems tend to be distributed, multiple sensors, and actuators are more appropriate. As such, the SISO minimum variance controller was extended to the MIMO case. Simulation and experimental results are presented. A state-space model of a simplified gas turbine engine is developed using the bond graph technique. The model retains important system behavior, yet is of low enough order to be useful for controller design. Expansion of the model to include multiple stages and spools is also discussed.

Large amplitude MHD waves upstream of the Jovian bow shock

NASA Technical Reports Server (NTRS)

Goldstein, M. L.; Smith, C. W.; Matthaeus, W. H.

1983-01-01

Observations of large amplitude magnetohydrodynamics (MHD) waves upstream of Jupiter's bow shock are analyzed. The waves are found to be right circularly polarized in the solar wind frame which suggests that they are propagating in the fast magnetosonic mode. A complete spectral and minimum variance eigenvalue analysis of the data was performed. The power spectrum of the magnetic fluctuations contains several peaks. The fluctuations at 2.3 mHz have a direction of minimum variance along the direction of the average magnetic field. The direction of minimum variance of these fluctuations lies at approximately 40 deg. to the magnetic field and is parallel to the radial direction. We argue that these fluctuations are waves excited by protons reflected off the Jovian bow shock. The inferred speed of the reflected protons is about two times the solar wind speed in the plasma rest frame. A linear instability analysis is presented which suggests an explanation for many of the observed features of the observations.

An unbiased risk estimator for image denoising in the presence of mixed poisson-gaussian noise.

PubMed

Le Montagner, Yoann; Angelini, Elsa D; Olivo-Marin, Jean-Christophe

2014-03-01

The behavior and performance of denoising algorithms are governed by one or several parameters, whose optimal settings depend on the content of the processed image and the characteristics of the noise, and are generally designed to minimize the mean squared error (MSE) between the denoised image returned by the algorithm and a virtual ground truth. In this paper, we introduce a new Poisson-Gaussian unbiased risk estimator (PG-URE) of the MSE applicable to a mixed Poisson-Gaussian noise model that unifies the widely used Gaussian and Poisson noise models in fluorescence bioimaging applications. We propose a stochastic methodology to evaluate this estimator in the case when little is known about the internal machinery of the considered denoising algorithm, and we analyze both theoretically and empirically the characteristics of the PG-URE estimator. Finally, we evaluate the PG-URE-driven parametrization for three standard denoising algorithms, with and without variance stabilizing transforms, and different characteristics of the Poisson-Gaussian noise mixture.

Genomic estimation of additive and dominance effects and impact of accounting for dominance on accuracy of genomic evaluation in sheep populations.

PubMed

Moghaddar, N; van der Werf, J H J

2017-12-01

The objectives of this study were to estimate the additive and dominance variance component of several weight and ultrasound scanned body composition traits in purebred and combined cross-bred sheep populations based on single nucleotide polymorphism (SNP) marker genotypes and then to investigate the effect of fitting additive and dominance effects on accuracy of genomic evaluation. Additive and dominance variance components were estimated in a mixed model equation based on "average information restricted maximum likelihood" using additive and dominance (co)variances between animals calculated from 48,599 SNP marker genotypes. Genomic prediction was based on genomic best linear unbiased prediction (GBLUP), and the accuracy of prediction was assessed based on a random 10-fold cross-validation. Across different weight and scanned body composition traits, dominance variance ranged from 0.0% to 7.3% of the phenotypic variance in the purebred population and from 7.1% to 19.2% in the combined cross-bred population. In the combined cross-bred population, the range of dominance variance decreased to 3.1% and 9.9% after accounting for heterosis effects. Accounting for dominance effects significantly improved the likelihood of the fitting model in the combined cross-bred population. This study showed a substantial dominance genetic variance for weight and ultrasound scanned body composition traits particularly in cross-bred population; however, improvement in the accuracy of genomic breeding values was small and statistically not significant. Dominance variance estimates in combined cross-bred population could be overestimated if heterosis is not fitted in the model. © 2017 Blackwell Verlag GmbH.

Estimating genetic effects and quantifying missing heritability explained by identified rare-variant associations.

PubMed

Liu, Dajiang J; Leal, Suzanne M

2012-10-05

Next-generation sequencing has led to many complex-trait rare-variant (RV) association studies. Although single-variant association analysis can be performed, it is grossly underpowered. Therefore, researchers have developed many RV association tests that aggregate multiple variant sites across a genetic region (e.g., gene), and test for the association between the trait and the aggregated genotype. After these aggregate tests detect an association, it is only possible to estimate the average genetic effect for a group of RVs. As a result of the "winner's curse," such an estimate can be biased. Although for common variants one can obtain unbiased estimates of genetic parameters by analyzing a replication sample, for RVs it is desirable to obtain unbiased genetic estimates for the study where the association is identified. This is because there can be substantial heterogeneity of RV sites and frequencies even among closely related populations. In order to obtain an unbiased estimate for aggregated RV analysis, we developed bootstrap-sample-split algorithms to reduce the bias of the winner's curse. The unbiased estimates are greatly important for understanding the population-specific contribution of RVs to the heritability of complex traits. We also demonstrate both theoretically and via simulations that for aggregate RV analysis the genetic variance for a gene or region will always be underestimated, sometimes substantially, because of the presence of noncausal variants or because of the presence of causal variants with effects of different magnitudes or directions. Therefore, even if RVs play a major role in the complex-trait etiologies, a portion of the heritability will remain missing, and the contribution of RVs to the complex-trait etiologies will be underestimated. Copyright © 2012 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

Angular dependence of primordial trispectra and CMB spectral distortions

NASA Astrophysics Data System (ADS)

Shiraishi, Maresuke; Bartolo, Nicola; Liguori, Michele

2016-10-01

Under the presence of anisotropic sources in the inflationary era, the trispectrum of the primordial curvature perturbation has a very specific angular dependence between each wavevector that is distinguishable from the one encountered when only scalar fields are present, characterized by an angular dependence described by Legendre polynomials. We examine the imprints left by curvature trispectra on the TTμ bispectrum, generated by the correlation between temperature anisotropies (T) and chemical potential spectral distortions (μ) of the Cosmic Microwave Background (CMB). Due to the angular dependence of the primordial signal, the corresponding TTμ bispectrum strongly differs in shape from TTμ sourced by the usual gNL or τNL local trispectra, enabling us to obtain an unbiased estimation. From a Fisher matrix analysis, we find that, in a cosmic-variance-limited (CVL) survey of TTμ, a minimum detectable value of the quadrupolar Legendre coefficient is d2 ~ 0.01, which is 4 orders of magnitude better than the best value attainable from the TTTT CMB trispectrum. In the case of an anisotropic inflationary model with a f(phi)F2 interaction (coupling the inflaton field phi with a vector kinetic term F2), the size of the curvature trispectrum is related to that of quadrupolar power spectrum asymmetry, g*. In this case, a CVL measurement of TTμ makes it possible to measure g* down to 10-3.

Modeling Multiplicative Error Variance: An Example Predicting Tree Diameter from Stump Dimensions in Baldcypress

Treesearch

Bernard R. Parresol

1993-01-01

In the context of forest modeling, it is often reasonable to assume a multiplicative heteroscedastic error structure to the data. Under such circumstances ordinary least squares no longer provides minimum variance estimates of the model parameters. Through study of the error structure, a suitable error variance model can be specified and its parameters estimated. This...

Quantum Theory of Three-Dimensional Superresolution Using Rotating-PSF Imagery

NASA Astrophysics Data System (ADS)

Prasad, S.; Yu, Z.

The inverse of the quantum Fisher information (QFI) matrix (and extensions thereof) provides the ultimate lower bound on the variance of any unbiased estimation of a parameter from statistical data, whether of intrinsically quantum mechanical or classical character. We calculate the QFI for Poisson-shot-noise-limited imagery using the rotating PSF that can localize and resolve point sources fully in all three dimensions. We also propose an experimental approach based on the use of computer generated hologram and projective measurements to realize the QFI-limited variance for the problem of super-resolving a closely spaced pair of point sources at a highly reduced photon cost. The paper presents a preliminary analysis of quantum-limited three-dimensional (3D) pair optical super-resolution (OSR) problem with potential applications to astronomical imaging and 3D space-debris localization.

Minimum number of measurements for evaluating soursop (Annona muricata L.) yield.

PubMed

Sánchez, C F B; Teodoro, P E; Londoño, S; Silva, L A; Peixoto, L A; Bhering, L L

2017-05-31

Repeatability studies on fruit species are of great importance to identify the minimum number of measurements necessary to accurately select superior genotypes. This study aimed to identify the most efficient method to estimate the repeatability coefficient (r) and predict the minimum number of measurements needed for a more accurate evaluation of soursop (Annona muricata L.) genotypes based on fruit yield. Sixteen measurements of fruit yield from 71 soursop genotypes were carried out between 2000 and 2016. In order to estimate r with the best accuracy, four procedures were used: analysis of variance, principal component analysis based on the correlation matrix, principal component analysis based on the phenotypic variance and covariance matrix, and structural analysis based on the correlation matrix. The minimum number of measurements needed to predict the actual value of individuals was estimated. Principal component analysis using the phenotypic variance and covariance matrix provided the most accurate estimates of both r and the number of measurements required for accurate evaluation of fruit yield in soursop. Our results indicate that selection of soursop genotypes with high fruit yield can be performed based on the third and fourth measurements in the early years and/or based on the eighth and ninth measurements at more advanced stages.

Uncertainty relation for the discrete Fourier transform.

PubMed

Massar, Serge; Spindel, Philippe

2008-05-16

We derive an uncertainty relation for two unitary operators which obey a commutation relation of the form UV=e(i phi) VU. Its most important application is to constrain how much a quantum state can be localized simultaneously in two mutually unbiased bases related by a discrete fourier transform. It provides an uncertainty relation which smoothly interpolates between the well-known cases of the Pauli operators in two dimensions and the continuous variables position and momentum. This work also provides an uncertainty relation for modular variables, and could find applications in signal processing. In the finite dimensional case the minimum uncertainty states, discrete analogues of coherent and squeezed states, are minimum energy solutions of Harper's equation, a discrete version of the harmonic oscillator equation.

Analysis of 20 magnetic clouds at 1 AU during a solar minimum

NASA Astrophysics Data System (ADS)

Gulisano, A. M.; Dasso, S.; Mandrini, C. H.; Démoulin, P.

We study 20 magnetic clouds, observed in situ by the spacecraft Wind, at the Lagrangian point L1, from 22 August, 1995, to 7 November, 1997. In previous works, assuming a cylindrical symmetry for the local magnetic configuration and a satellite trajectory crossing the axis of the cloud, we obtained their orientations using a minimum variance analysis. In this work we compute the orientations and magnetic configurations using a non-linear simultaneous fit of the geometric and physical parameters for a linear force-free model, including the possibility of a not null impact parameter. We quantify global magnitudes such as the relative magnetic helicity per unit length and compare the values found with both methods (minimum variance and the simultaneous fit). FULL TEXT IN SPANISH

Targeted estimation of nuisance parameters to obtain valid statistical inference.

PubMed

van der Laan, Mark J

2014-01-01

In order to obtain concrete results, we focus on estimation of the treatment specific mean, controlling for all measured baseline covariates, based on observing independent and identically distributed copies of a random variable consisting of baseline covariates, a subsequently assigned binary treatment, and a final outcome. The statistical model only assumes possible restrictions on the conditional distribution of treatment, given the covariates, the so-called propensity score. Estimators of the treatment specific mean involve estimation of the propensity score and/or estimation of the conditional mean of the outcome, given the treatment and covariates. In order to make these estimators asymptotically unbiased at any data distribution in the statistical model, it is essential to use data-adaptive estimators of these nuisance parameters such as ensemble learning, and specifically super-learning. Because such estimators involve optimal trade-off of bias and variance w.r.t. the infinite dimensional nuisance parameter itself, they result in a sub-optimal bias/variance trade-off for the resulting real-valued estimator of the estimand. We demonstrate that additional targeting of the estimators of these nuisance parameters guarantees that this bias for the estimand is second order and thereby allows us to prove theorems that establish asymptotic linearity of the estimator of the treatment specific mean under regularity conditions. These insights result in novel targeted minimum loss-based estimators (TMLEs) that use ensemble learning with additional targeted bias reduction to construct estimators of the nuisance parameters. In particular, we construct collaborative TMLEs (C-TMLEs) with known influence curve allowing for statistical inference, even though these C-TMLEs involve variable selection for the propensity score based on a criterion that measures how effective the resulting fit of the propensity score is in removing bias for the estimand. As a particular special case, we also demonstrate the required targeting of the propensity score for the inverse probability of treatment weighted estimator using super-learning to fit the propensity score.

Noise and analyzer-crystal angular position analysis for analyzer-based phase-contrast imaging

NASA Astrophysics Data System (ADS)

Majidi, Keivan; Li, Jun; Muehleman, Carol; Brankov, Jovan G.

2014-04-01

The analyzer-based phase-contrast x-ray imaging (ABI) method is emerging as a potential alternative to conventional radiography. Like many of the modern imaging techniques, ABI is a computed imaging method (meaning that images are calculated from raw data). ABI can simultaneously generate a number of planar parametric images containing information about absorption, refraction, and scattering properties of an object. These images are estimated from raw data acquired by measuring (sampling) the angular intensity profile of the x-ray beam passed through the object at different angular positions of the analyzer crystal. The noise in the estimated ABI parametric images depends upon imaging conditions like the source intensity (flux), measurements angular positions, object properties, and the estimation method. In this paper, we use the Cramér-Rao lower bound (CRLB) to quantify the noise properties in parametric images and to investigate the effect of source intensity, different analyzer-crystal angular positions and object properties on this bound, assuming a fixed radiation dose delivered to an object. The CRLB is the minimum bound for the variance of an unbiased estimator and defines the best noise performance that one can obtain regardless of which estimation method is used to estimate ABI parametric images. The main result of this paper is that the variance (hence the noise) in parametric images is directly proportional to the source intensity and only a limited number of analyzer-crystal angular measurements (eleven for uniform and three for optimal non-uniform) are required to get the best parametric images. The following angular measurements only spread the total dose to the measurements without improving or worsening CRLB, but the added measurements may improve parametric images by reducing estimation bias. Next, using CRLB we evaluate the multiple-image radiography, diffraction enhanced imaging and scatter diffraction enhanced imaging estimation techniques, though the proposed methodology can be used to evaluate any other ABI parametric image estimation technique.

Noise and Analyzer-Crystal Angular Position Analysis for Analyzer-Based Phase-Contrast Imaging

PubMed Central

Majidi, Keivan; Li, Jun; Muehleman, Carol; Brankov, Jovan G.

2014-01-01

The analyzer-based phase-contrast X-ray imaging (ABI) method is emerging as a potential alternative to conventional radiography. Like many of the modern imaging techniques, ABI is a computed imaging method (meaning that images are calculated from raw data). ABI can simultaneously generate a number of planar parametric images containing information about absorption, refraction, and scattering properties of an object. These images are estimated from raw data acquired by measuring (sampling) the angular intensity profile (AIP) of the X-ray beam passed through the object at different angular positions of the analyzer crystal. The noise in the estimated ABI parametric images depends upon imaging conditions like the source intensity (flux), measurements angular positions, object properties, and the estimation method. In this paper, we use the Cramér-Rao lower bound (CRLB) to quantify the noise properties in parametric images and to investigate the effect of source intensity, different analyzer-crystal angular positions and object properties on this bound, assuming a fixed radiation dose delivered to an object. The CRLB is the minimum bound for the variance of an unbiased estimator and defines the best noise performance that one can obtain regardless of which estimation method is used to estimate ABI parametric images. The main result of this manuscript is that the variance (hence the noise) in parametric images is directly proportional to the source intensity and only a limited number of analyzer-crystal angular measurements (eleven for uniform and three for optimal non-uniform) are required to get the best parametric images. The following angular measurements only spread the total dose to the measurements without improving or worsening CRLB, but the added measurements may improve parametric images by reducing estimation bias. Next, using CRLB we evaluate the Multiple-Image Radiography (MIR), Diffraction Enhanced Imaging (DEI) and Scatter Diffraction Enhanced Imaging (S-DEI) estimation techniques, though the proposed methodology can be used to evaluate any other ABI parametric image estimation technique. PMID:24651402

Estimation of transformation parameters for microarray data.

PubMed

Durbin, Blythe; Rocke, David M

2003-07-22

Durbin et al. (2002), Huber et al. (2002) and Munson (2001) independently introduced a family of transformations (the generalized-log family) which stabilizes the variance of microarray data up to the first order. We introduce a method for estimating the transformation parameter in tandem with a linear model based on the procedure outlined in Box and Cox (1964). We also discuss means of finding transformations within the generalized-log family which are optimal under other criteria, such as minimum residual skewness and minimum mean-variance dependency. R and Matlab code and test data are available from the authors on request.

Alzheimer Classification Using a Minimum Spanning Tree of High-Order Functional Network on fMRI Dataset

PubMed Central

Guo, Hao; Liu, Lei; Chen, Junjie; Xu, Yong; Jie, Xiang

2017-01-01

Functional magnetic resonance imaging (fMRI) is one of the most useful methods to generate functional connectivity networks of the brain. However, conventional network generation methods ignore dynamic changes of functional connectivity between brain regions. Previous studies proposed constructing high-order functional connectivity networks that consider the time-varying characteristics of functional connectivity, and a clustering method was performed to decrease computational cost. However, random selection of the initial clustering centers and the number of clusters negatively affected classification accuracy, and the network lost neurological interpretability. Here we propose a novel method that introduces the minimum spanning tree method to high-order functional connectivity networks. As an unbiased method, the minimum spanning tree simplifies high-order network structure while preserving its core framework. The dynamic characteristics of time series are not lost with this approach, and the neurological interpretation of the network is guaranteed. Simultaneously, we propose a multi-parameter optimization framework that involves extracting discriminative features from the minimum spanning tree high-order functional connectivity networks. Compared with the conventional methods, our resting-state fMRI classification method based on minimum spanning tree high-order functional connectivity networks greatly improved the diagnostic accuracy for Alzheimer's disease. PMID:29249926

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

Luis, Alfredo

The use of Renyi entropy as an uncertainty measure alternative to variance leads to the study of states with quantum fluctuations below the levels established by Gaussian states, which are the position-momentum minimum uncertainty states according to variance. We examine the quantum properties of states with exponential wave functions, which combine reduced fluctuations with practical feasibility.

A Bayesian Approach to Identifying Structural Nonlinearity using Free-Decay Response: Application to Damage Detection in Composites

DTIC Science & Technology

2010-03-03

obtainable while for the free-decay problem we simply have to include the initial conditions as random variables to be predicted. A different approach that...important and useful properties of MLEs is that, under regularity conditions , they are asymptotically unbiased and possess the minimum possible...becomes pLðzjh;s2G;MiÞ (i.e. the likelihood is conditional on the specified model). However, in this work we will only consider a single model and drop the

Maximal violation of Clauser-Horne-Shimony-Holt inequality for four-level systems

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

Fu Libin; Max-Planck-Institute for the Physics of Complex Systems, Noethnitzer Strasse 38, 01187 Dresden; Chen Jingling

2004-03-01

Clauser-Horne-Shimony-Holt inequality for bipartite systems of four dimensions is studied in detail by employing the unbiased eight-port beam splitters measurements. The uniform formulas for the maximum and minimum values of this inequality for such measurements are obtained. Based on these formulas, we show that an optimal nonmaximally entangled state is about 6% more resistant to noise than the maximally entangled one. We also give the optimal state and the optimal angles which are important for experimental realization.

Epidemiologic Evaluation of Measurement Data in the Presence of Detection Limits

PubMed Central

Lubin, Jay H.; Colt, Joanne S.; Camann, David; Davis, Scott; Cerhan, James R.; Severson, Richard K.; Bernstein, Leslie; Hartge, Patricia

2004-01-01

Quantitative measurements of environmental factors greatly improve the quality of epidemiologic studies but can pose challenges because of the presence of upper or lower detection limits or interfering compounds, which do not allow for precise measured values. We consider the regression of an environmental measurement (dependent variable) on several covariates (independent variables). Various strategies are commonly employed to impute values for interval-measured data, including assignment of one-half the detection limit to nondetected values or of “fill-in” values randomly selected from an appropriate distribution. On the basis of a limited simulation study, we found that the former approach can be biased unless the percentage of measurements below detection limits is small (5–10%). The fill-in approach generally produces unbiased parameter estimates but may produce biased variance estimates and thereby distort inference when 30% or more of the data are below detection limits. Truncated data methods (e.g., Tobit regression) and multiple imputation offer two unbiased approaches for analyzing measurement data with detection limits. If interest resides solely on regression parameters, then Tobit regression can be used. If individualized values for measurements below detection limits are needed for additional analysis, such as relative risk regression or graphical display, then multiple imputation produces unbiased estimates and nominal confidence intervals unless the proportion of missing data is extreme. We illustrate various approaches using measurements of pesticide residues in carpet dust in control subjects from a case–control study of non-Hodgkin lymphoma. PMID:15579415

Gene–Environment Correlation: Difficulties and a Natural Experiment–Based Strategy

PubMed Central

Li, Jiang; Liu, Hexuan; Guo, Guang

2013-01-01

Objectives. We explored how gene–environment correlations can result in endogenous models, how natural experiments can protect against this threat, and if unbiased estimates from natural experiments are generalizable to other contexts. Methods. We compared a natural experiment, the College Roommate Study, which measured genes and behaviors of college students and their randomly assigned roommates in a southern public university, with observational data from the National Longitudinal Study of Adolescent Health in 2008. We predicted exposure to exercising peers using genetic markers and estimated environmental effects on alcohol consumption. A mixed-linear model estimated an alcohol consumption variance that was attributable to genetic markers and across peer environments. Results. Peer exercise environment was associated with respondent genotype in observational data, but not in the natural experiment. The effects of peer drinking and presence of a general gene–environment interaction were similar between data sets. Conclusions. Natural experiments, like random roommate assignment, could protect against potential bias introduced by gene–environment correlations. When combined with representative observational data, unbiased and generalizable causal effects could be estimated. PMID:23927502

Blinded sample size re-estimation in three-arm trials with 'gold standard' design.

PubMed

Mütze, Tobias; Friede, Tim

2017-10-15

In this article, we study blinded sample size re-estimation in the 'gold standard' design with internal pilot study for normally distributed outcomes. The 'gold standard' design is a three-arm clinical trial design that includes an active and a placebo control in addition to an experimental treatment. We focus on the absolute margin approach to hypothesis testing in three-arm trials at which the non-inferiority of the experimental treatment and the assay sensitivity are assessed by pairwise comparisons. We compare several blinded sample size re-estimation procedures in a simulation study assessing operating characteristics including power and type I error. We find that sample size re-estimation based on the popular one-sample variance estimator results in overpowered trials. Moreover, sample size re-estimation based on unbiased variance estimators such as the Xing-Ganju variance estimator results in underpowered trials, as it is expected because an overestimation of the variance and thus the sample size is in general required for the re-estimation procedure to eventually meet the target power. To overcome this problem, we propose an inflation factor for the sample size re-estimation with the Xing-Ganju variance estimator and show that this approach results in adequately powered trials. Because of favorable features of the Xing-Ganju variance estimator such as unbiasedness and a distribution independent of the group means, the inflation factor does not depend on the nuisance parameter and, therefore, can be calculated prior to a trial. Moreover, we prove that the sample size re-estimation based on the Xing-Ganju variance estimator does not bias the effect estimate. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Variance estimation when using inverse probability of treatment weighting (IPTW) with survival analysis.

PubMed

Austin, Peter C

2016-12-30

Propensity score methods are used to reduce the effects of observed confounding when using observational data to estimate the effects of treatments or exposures. A popular method of using the propensity score is inverse probability of treatment weighting (IPTW). When using this method, a weight is calculated for each subject that is equal to the inverse of the probability of receiving the treatment that was actually received. These weights are then incorporated into the analyses to minimize the effects of observed confounding. Previous research has found that these methods result in unbiased estimation when estimating the effect of treatment on survival outcomes. However, conventional methods of variance estimation were shown to result in biased estimates of standard error. In this study, we conducted an extensive set of Monte Carlo simulations to examine different methods of variance estimation when using a weighted Cox proportional hazards model to estimate the effect of treatment. We considered three variance estimation methods: (i) a naïve model-based variance estimator; (ii) a robust sandwich-type variance estimator; and (iii) a bootstrap variance estimator. We considered estimation of both the average treatment effect and the average treatment effect in the treated. We found that the use of a bootstrap estimator resulted in approximately correct estimates of standard errors and confidence intervals with the correct coverage rates. The other estimators resulted in biased estimates of standard errors and confidence intervals with incorrect coverage rates. Our simulations were informed by a case study examining the effect of statin prescribing on mortality. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

Familial Sotos syndrome (cerebral gigantism): craniofacial and psychological characteristics.

PubMed

Bale, A E; Drum, M A; Parry, D M; Mulvihill, J J

1985-04-01

Most reported cases of Sotos syndrome are sporadic, but autosomal dominant and recessive inheritance patterns have been suggested. Ascertainment of a two-generation family through a 7-year-old proposita with a learning disability allowed the relatively unbiased study of two affected relatives. Developmental delay was not pronounced in the patient's mother or sister; craniofacial characteristics at variance with the characteristic description included acrocephaly and maxillary prominence. Steepness of the anterior cranial base angle and protrusion of the middle and lower face, shown in all three patients by cephalometric radiographs, deserve further evaluation as diagnostic criteria.

Sodium Binding Sites and Permeation Mechanism in the NaChBac Channel: A Molecular Dynamics Study.

PubMed

Guardiani, Carlo; Rodger, P Mark; Fedorenko, Olena A; Roberts, Stephen K; Khovanov, Igor A

2017-03-14

NaChBac was the first discovered bacterial sodium voltage-dependent channel, yet computational studies are still limited due to the lack of a crystal structure. In this work, a pore-only construct built using the NavMs template was investigated using unbiased molecular dynamics and metadynamics. The potential of mean force (PMF) from the unbiased run features four minima, three of which correspond to sites IN, CEN, and HFS discovered in NavAb. During the run, the selectivity filter (SF) is spontaneously occupied by two ions, and frequent access of a third one is often observed. In the innermost sites IN and CEN, Na + is fully hydrated by six water molecules and occupies an on-axis position. In site HFS sodium interacts with a glutamate and a serine from the same subunit and is forced to adopt an off-axis placement. Metadynamics simulations biasing one and two ions show an energy barrier in the SF that prevents single-ion permeation. An analysis of the permeation mechanism was performed both computing minimum energy paths in the axial-axial PMF and through a combination of Markov state modeling and transition path theory. Both approaches reveal a knock-on mechanism involving at least two but possibly three ions. The currents predicted from the unbiased simulation using linear response theory are in excellent agreement with single-channel patch-clamp recordings.

Reliability estimation of a N- M-cold-standby redundancy system in a multicomponent stress-strength model with generalized half-logistic distribution

NASA Astrophysics Data System (ADS)

Liu, Yiming; Shi, Yimin; Bai, Xuchao; Zhan, Pei

2018-01-01

In this paper, we study the estimation for the reliability of a multicomponent system, named N- M-cold-standby redundancy system, based on progressive Type-II censoring sample. In the system, there are N subsystems consisting of M statistically independent distributed strength components, and only one of these subsystems works under the impact of stresses at a time and the others remain as standbys. Whenever the working subsystem fails, one from the standbys takes its place. The system fails when the entire subsystems fail. It is supposed that the underlying distributions of random strength and stress both belong to the generalized half-logistic distribution with different shape parameter. The reliability of the system is estimated by using both classical and Bayesian statistical inference. Uniformly minimum variance unbiased estimator and maximum likelihood estimator for the reliability of the system are derived. Under squared error loss function, the exact expression of the Bayes estimator for the reliability of the system is developed by using the Gauss hypergeometric function. The asymptotic confidence interval and corresponding coverage probabilities are derived based on both the Fisher and the observed information matrices. The approximate highest probability density credible interval is constructed by using Monte Carlo method. Monte Carlo simulations are performed to compare the performances of the proposed reliability estimators. A real data set is also analyzed for an illustration of the findings.

Angular dependence of primordial trispectra and CMB spectral distortions

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

Shiraishi, Maresuke; Bartolo, Nicola; Liguori, Michele, E-mail: maresuke.shiraishi@ipmu.jp, E-mail: nicola.bartolo@pd.infn.it, E-mail: michele.liguori@pd.infn.it

2016-10-01

Under the presence of anisotropic sources in the inflationary era, the trispectrum of the primordial curvature perturbation has a very specific angular dependence between each wavevector that is distinguishable from the one encountered when only scalar fields are present, characterized by an angular dependence described by Legendre polynomials. We examine the imprints left by curvature trispectra on the TT μ bispectrum, generated by the correlation between temperature anisotropies (T) and chemical potential spectral distortions (μ) of the Cosmic Microwave Background (CMB). Due to the angular dependence of the primordial signal, the corresponding TT μ bispectrum strongly differs in shape frommore » TT μ sourced by the usual g {sub NL} or τ{sub NL} local trispectra, enabling us to obtain an unbiased estimation. From a Fisher matrix analysis, we find that, in a cosmic-variance-limited (CVL) survey of TT μ, a minimum detectable value of the quadrupolar Legendre coefficient is d {sub 2} ∼ 0.01, which is 4 orders of magnitude better than the best value attainable from the TTTT CMB trispectrum. In the case of an anisotropic inflationary model with a f (φ) F {sup 2} interaction (coupling the inflaton field φ with a vector kinetic term F {sup 2}), the size of the curvature trispectrum is related to that of quadrupolar power spectrum asymmetry, g {sub *}. In this case, a CVL measurement of TT μ makes it possible to measure g {sub *} down to 10{sup −3}.« less

In vivo tear film thickness measurement and tear film dynamics visualization using spectral domain OCT and an efficient delay estimator

NASA Astrophysics Data System (ADS)

Aranha dos Santos, Valentin; Schmetterer, Leopold; Gröschl, Martin; Garhofer, Gerhard; Werkmeister, René M.

2016-03-01

Dry eye syndrome is a highly prevalent disease of the ocular surface characterized by an instability of the tear film. Traditional methods used for the evaluation of tear film stability are invasive or show limited repeatability. Here we propose a new noninvasive approach to measure tear film thickness using an efficient delay estimator and ultrahigh resolution spectral domain OCT. Silicon wafer phantoms with layers of known thickness and group index were used to validate the estimator-based thickness measurement. A theoretical analysis of the fundamental limit of the precision of the estimator is presented and the analytical expression of the Cramér-Rao lower bound (CRLB), which is the minimum variance that may be achieved by any unbiased estimator, is derived. The performance of the estimator against noise was investigated using simulations. We found that the proposed estimator reaches the CRLB associated with the OCT amplitude signal. The technique was applied in vivo in healthy subjects and dry eye patients. Series of tear film thickness maps were generated, allowing for the visualization of tear film dynamics. Our results show that the central tear film thickness precisely measured in vivo with a coefficient of variation of about 0.65% and that repeatable tear film dynamics can be observed. The presented method has the potential of being an alternative to breakup time measurements (BUT) and could be used in clinical setting to study patients with dry eye disease and monitor their treatments.

Maximum Likelihood and Minimum Distance Applied to Univariate Mixture Distributions.

ERIC Educational Resources Information Center

Wang, Yuh-Yin Wu; Schafer, William D.

This Monte-Carlo study compared modified Newton (NW), expectation-maximization algorithm (EM), and minimum Cramer-von Mises distance (MD), used to estimate parameters of univariate mixtures of two components. Data sets were fixed at size 160 and manipulated by mean separation, variance ratio, component proportion, and non-normality. Results…

A Minimum Variance Algorithm for Overdetermined TOA Equations with an Altitude Constraint.

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

Romero, Louis A; Mason, John J.

We present a direct (non-iterative) method for solving for the location of a radio frequency (RF) emitter, or an RF navigation receiver, using four or more time of arrival (TOA) measurements and an assumed altitude above an ellipsoidal earth. Both the emitter tracking problem and the navigation application are governed by the same equations, but with slightly different interpreta- tions of several variables. We treat the assumed altitude as a soft constraint, with a specified noise level, just as the TOA measurements are handled, with their respective noise levels. With 4 or more TOA measurements and the assumed altitude, themore » problem is overdetermined and is solved in the weighted least squares sense for the 4 unknowns, the 3-dimensional position and time. We call the new technique the TAQMV (TOA Altitude Quartic Minimum Variance) algorithm, and it achieves the minimum possible error variance for given levels of TOA and altitude estimate noise. The method algebraically produces four solutions, the least-squares solution, and potentially three other low residual solutions, if they exist. In the lightly overdermined cases where multiple local minima in the residual error surface are more likely to occur, this algebraic approach can produce all of the minima even when an iterative approach fails to converge. Algorithm performance in terms of solution error variance and divergence rate for bas eline (iterative) and proposed approach are given in tables.« less

A method for minimum risk portfolio optimization under hybrid uncertainty

NASA Astrophysics Data System (ADS)

Egorova, Yu E.; Yazenin, A. V.

2018-03-01

In this paper, we investigate a minimum risk portfolio model under hybrid uncertainty when the profitability of financial assets is described by fuzzy random variables. According to Feng, the variance of a portfolio is defined as a crisp value. To aggregate fuzzy information the weakest (drastic) t-norm is used. We construct an equivalent stochastic problem of the minimum risk portfolio model and specify the stochastic penalty method for solving it.

Validation of sea ice models using an uncertainty-based distance metric for multiple model variables: NEW METRIC FOR SEA ICE MODEL VALIDATION

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

Urrego-Blanco, Jorge R.; Hunke, Elizabeth C.; Urban, Nathan M.

Here, we implement a variance-based distance metric (D n) to objectively assess skill of sea ice models when multiple output variables or uncertainties in both model predictions and observations need to be considered. The metric compares observations and model data pairs on common spatial and temporal grids improving upon highly aggregated metrics (e.g., total sea ice extent or volume) by capturing the spatial character of model skill. The D n metric is a gamma-distributed statistic that is more general than the χ 2 statistic commonly used to assess model fit, which requires the assumption that the model is unbiased andmore » can only incorporate observational error in the analysis. The D n statistic does not assume that the model is unbiased, and allows the incorporation of multiple observational data sets for the same variable and simultaneously for different variables, along with different types of variances that can characterize uncertainties in both observations and the model. This approach represents a step to establish a systematic framework for probabilistic validation of sea ice models. The methodology is also useful for model tuning by using the D n metric as a cost function and incorporating model parametric uncertainty as part of a scheme to optimize model functionality. We apply this approach to evaluate different configurations of the standalone Los Alamos sea ice model (CICE) encompassing the parametric uncertainty in the model, and to find new sets of model configurations that produce better agreement than previous configurations between model and observational estimates of sea ice concentration and thickness.« less

Validation of sea ice models using an uncertainty-based distance metric for multiple model variables: NEW METRIC FOR SEA ICE MODEL VALIDATION

DOE PAGES

Urrego-Blanco, Jorge R.; Hunke, Elizabeth C.; Urban, Nathan M.; ...

2017-04-01

Here, we implement a variance-based distance metric (D n) to objectively assess skill of sea ice models when multiple output variables or uncertainties in both model predictions and observations need to be considered. The metric compares observations and model data pairs on common spatial and temporal grids improving upon highly aggregated metrics (e.g., total sea ice extent or volume) by capturing the spatial character of model skill. The D n metric is a gamma-distributed statistic that is more general than the χ 2 statistic commonly used to assess model fit, which requires the assumption that the model is unbiased andmore » can only incorporate observational error in the analysis. The D n statistic does not assume that the model is unbiased, and allows the incorporation of multiple observational data sets for the same variable and simultaneously for different variables, along with different types of variances that can characterize uncertainties in both observations and the model. This approach represents a step to establish a systematic framework for probabilistic validation of sea ice models. The methodology is also useful for model tuning by using the D n metric as a cost function and incorporating model parametric uncertainty as part of a scheme to optimize model functionality. We apply this approach to evaluate different configurations of the standalone Los Alamos sea ice model (CICE) encompassing the parametric uncertainty in the model, and to find new sets of model configurations that produce better agreement than previous configurations between model and observational estimates of sea ice concentration and thickness.« less

Class Enumeration and Parameter Recovery of Growth Mixture Modeling and Second-Order Growth Mixture Modeling in the Presence of Measurement Noninvariance between Latent Classes

PubMed Central

Kim, Eun Sook; Wang, Yan

2017-01-01

Population heterogeneity in growth trajectories can be detected with growth mixture modeling (GMM). It is common that researchers compute composite scores of repeated measures and use them as multiple indicators of growth factors (baseline performance and growth) assuming measurement invariance between latent classes. Considering that the assumption of measurement invariance does not always hold, we investigate the impact of measurement noninvariance on class enumeration and parameter recovery in GMM through a Monte Carlo simulation study (Study 1). In Study 2, we examine the class enumeration and parameter recovery of the second-order growth mixture modeling (SOGMM) that incorporates measurement models at the first order level. Thus, SOGMM estimates growth trajectory parameters with reliable sources of variance, that is, common factor variance of repeated measures and allows heterogeneity in measurement parameters between latent classes. The class enumeration rates are examined with information criteria such as AIC, BIC, sample-size adjusted BIC, and hierarchical BIC under various simulation conditions. The results of Study 1 showed that the parameter estimates of baseline performance and growth factor means were biased to the degree of measurement noninvariance even when the correct number of latent classes was extracted. In Study 2, the class enumeration accuracy of SOGMM depended on information criteria, class separation, and sample size. The estimates of baseline performance and growth factor mean differences between classes were generally unbiased but the size of measurement noninvariance was underestimated. Overall, SOGMM is advantageous in that it yields unbiased estimates of growth trajectory parameters and more accurate class enumeration compared to GMM by incorporating measurement models. PMID:28928691

Testcross additive and dominance effects in best linear unbiased prediction of maize single-cross performance.

PubMed

Bernardo, R

1996-11-01

Best linear unbiased prediction (BLUP) has been found to be useful in maize (Zea mays L.) breeding. The advantage of including both testcross additive and dominance effects (Intralocus Model) in BLUP, rather than only testcross additive effects (Additive Model), has not been clearly demonstrated. The objective of this study was to compare the usefulness of Intralocus and Additive Models for BLUP of maize single-cross performance. Multilocation data from 1990 to 1995 were obtained from the hybrid testing program of Limagrain Genetics. Grain yield, moisture, stalk lodging, and root lodging of untested single crosses were predicted from (1) the performance of tested single crosses and (2) known genetic relationships among the parental inbreds. Correlations between predicted and observed performance were obtained with a delete-one cross-validation procedure. For the Intralocus Model, the correlations ranged from 0.50 to 0.66 for yield, 0.88 to 0.94 for moisture, 0.47 to 0.69 for stalk lodging, and 0.31 to 0.45 for root lodging. The BLUP procedure was consistently more effective with the Intralocus Model than with the Additive Model. When the Additive Model was used instead of the Intralocus Model, the reductions in the correlation were largest for root lodging (0.06-0.35), smallest for moisture (0.00-0.02), and intermediate for yield (0.02-0.06) and stalk lodging (0.02-0.08). The ratio of dominance variance (v D) to total genetic variance (v G) was highest for root lodging (0.47) and lowest for moisture (0.10). The Additive Model may be used if prior information indicates that VD for a given trait has little contribution to VG. Otherwise, the continued use of the Intralocus Model for BLUP of single-cross performance is recommended.

Kalman filter for statistical monitoring of forest cover across sub-continental regions [Symposium

Treesearch

Raymond L. Czaplewski

1991-01-01

The Kalman filter is a generalization of the composite estimator. The univariate composite estimate combines 2 prior estimates of population parameter with a weighted average where the scalar weight is inversely proportional to the variances. The composite estimator is a minimum variance estimator that requires no distributional assumptions other than estimates of the...

Solving portfolio selection problems with minimum transaction lots based on conditional-value-at-risk

NASA Astrophysics Data System (ADS)

Setiawan, E. P.; Rosadi, D.

2017-01-01

Portfolio selection problems conventionally means ‘minimizing the risk, given the certain level of returns’ from some financial assets. This problem is frequently solved with quadratic or linear programming methods, depending on the risk measure that used in the objective function. However, the solutions obtained by these method are in real numbers, which may give some problem in real application because each asset usually has its minimum transaction lots. In the classical approach considering minimum transaction lots were developed based on linear Mean Absolute Deviation (MAD), variance (like Markowitz’s model), and semi-variance as risk measure. In this paper we investigated the portfolio selection methods with minimum transaction lots with conditional value at risk (CVaR) as risk measure. The mean-CVaR methodology only involves the part of the tail of the distribution that contributed to high losses. This approach looks better when we work with non-symmetric return probability distribution. Solution of this method can be found with Genetic Algorithm (GA) methods. We provide real examples using stocks from Indonesia stocks market.

Regression calibration for models with two predictor variables measured with error and their interaction, using instrumental variables and longitudinal data.

PubMed

Strand, Matthew; Sillau, Stefan; Grunwald, Gary K; Rabinovitch, Nathan

2014-02-10

Regression calibration provides a way to obtain unbiased estimators of fixed effects in regression models when one or more predictors are measured with error. Recent development of measurement error methods has focused on models that include interaction terms between measured-with-error predictors, and separately, methods for estimation in models that account for correlated data. In this work, we derive explicit and novel forms of regression calibration estimators and associated asymptotic variances for longitudinal models that include interaction terms, when data from instrumental and unbiased surrogate variables are available but not the actual predictors of interest. The longitudinal data are fit using linear mixed models that contain random intercepts and account for serial correlation and unequally spaced observations. The motivating application involves a longitudinal study of exposure to two pollutants (predictors) - outdoor fine particulate matter and cigarette smoke - and their association in interactive form with levels of a biomarker of inflammation, leukotriene E4 (LTE 4 , outcome) in asthmatic children. Because the exposure concentrations could not be directly observed, we used measurements from a fixed outdoor monitor and urinary cotinine concentrations as instrumental variables, and we used concentrations of fine ambient particulate matter and cigarette smoke measured with error by personal monitors as unbiased surrogate variables. We applied the derived regression calibration methods to estimate coefficients of the unobserved predictors and their interaction, allowing for direct comparison of toxicity of the different pollutants. We used simulations to verify accuracy of inferential methods based on asymptotic theory. Copyright © 2013 John Wiley & Sons, Ltd.

Genomic BLUP including additive and dominant variation in purebreds and F1 crossbreds, with an application in pigs.

PubMed

Vitezica, Zulma G; Varona, Luis; Elsen, Jean-Michel; Misztal, Ignacy; Herring, William; Legarra, Andrès

2016-01-29

Most developments in quantitative genetics theory focus on the study of intra-breed/line concepts. With the availability of massive genomic information, it becomes necessary to revisit the theory for crossbred populations. We propose methods to construct genomic covariances with additive and non-additive (dominance) inheritance in the case of pure lines and crossbred populations. We describe substitution effects and dominant deviations across two pure parental populations and the crossbred population. Gene effects are assumed to be independent of the origin of alleles and allelic frequencies can differ between parental populations. Based on these assumptions, the theoretical variance components (additive and dominant) are obtained as a function of marker effects and allelic frequencies. The additive genetic variance in the crossbred population includes the biological additive and dominant effects of a gene and a covariance term. Dominance variance in the crossbred population is proportional to the product of the heterozygosity coefficients of both parental populations. A genomic BLUP (best linear unbiased prediction) equivalent model is presented. We illustrate this approach by using pig data (two pure lines and their cross, including 8265 phenotyped and genotyped sows). For the total number of piglets born, the dominance variance in the crossbred population represented about 13 % of the total genetic variance. Dominance variation is only marginally important for litter size in the crossbred population. We present a coherent marker-based model that includes purebred and crossbred data and additive and dominant actions. Using this model, it is possible to estimate breeding values, dominant deviations and variance components in a dataset that comprises data on purebred and crossbred individuals. These methods can be exploited to plan assortative mating in pig, maize or other species, in order to generate superior crossbred individuals in terms of performance.

Movement trajectory smoothness is not associated with the endpoint accuracy of rapid multi-joint arm movements in young and older adults

PubMed Central

Poston, Brach; Van Gemmert, Arend W.A.; Sharma, Siddharth; Chakrabarti, Somesh; Zavaremi, Shahrzad H.; Stelmach, George

2013-01-01

The minimum variance theory proposes that motor commands are corrupted by signal-dependent noise and smooth trajectories with low noise levels are selected to minimize endpoint error and endpoint variability. The purpose of the study was to determine the contribution of trajectory smoothness to the endpoint accuracy and endpoint variability of rapid multi-joint arm movements. Young and older adults performed arm movements (4 blocks of 25 trials) as fast and as accurately as possible to a target with the right (dominant) arm. Endpoint accuracy and endpoint variability along with trajectory smoothness and error were quantified for each block of trials. Endpoint error and endpoint variance were greater in older adults compared with young adults, but decreased at a similar rate with practice for the two age groups. The greater endpoint error and endpoint variance exhibited by older adults were primarily due to impairments in movement extent control and not movement direction control. The normalized jerk was similar for the two age groups, but was not strongly associated with endpoint error or endpoint variance for either group. However, endpoint variance was strongly associated with endpoint error for both the young and older adults. Finally, trajectory error was similar for both groups and was weakly associated with endpoint error for the older adults. The findings are not consistent with the predictions of the minimum variance theory, but support and extend previous observations that movement trajectories and endpoints are planned independently. PMID:23584101

Effects of important parameters variations on computing eigenspace-based minimum variance weights for ultrasound tissue harmonic imaging

NASA Astrophysics Data System (ADS)

Haji Heidari, Mehdi; Mozaffarzadeh, Moein; Manwar, Rayyan; Nasiriavanaki, Mohammadreza

2018-02-01

In recent years, the minimum variance (MV) beamforming has been widely studied due to its high resolution and contrast in B-mode Ultrasound imaging (USI). However, the performance of the MV beamformer is degraded at the presence of noise, as a result of the inaccurate covariance matrix estimation which leads to a low quality image. Second harmonic imaging (SHI) provides many advantages over the conventional pulse-echo USI, such as enhanced axial and lateral resolutions. However, the low signal-to-noise ratio (SNR) is a major problem in SHI. In this paper, Eigenspace-based minimum variance (EIBMV) beamformer has been employed for second harmonic USI. The Tissue Harmonic Imaging (THI) is achieved by Pulse Inversion (PI) technique. Using the EIBMV weights, instead of the MV ones, would lead to reduced sidelobes and improved contrast, without compromising the high resolution of the MV beamformer (even at the presence of a strong noise). In addition, we have investigated the effects of variations of the important parameters in computing EIBMV weights, i.e., K, L, and δ, on the resolution and contrast obtained in SHI. The results are evaluated using numerical data (using point target and cyst phantoms), and the proper parameters of EIBMV are indicated for THI.

Hydraulic geometry of river cross sections; theory of minimum variance

USGS Publications Warehouse

Williams, Garnett P.

1978-01-01

This study deals with the rates at which mean velocity, mean depth, and water-surface width increase with water discharge at a cross section on an alluvial stream. Such relations often follow power laws, the exponents in which are called hydraulic exponents. The Langbein (1964) minimum-variance theory is examined in regard to its validity and its ability to predict observed hydraulic exponents. The variables used with the theory were velocity, depth, width, bed shear stress, friction factor, slope (energy gradient), and stream power. Slope is often constant, in which case only velocity, depth, width, shear and friction factor need be considered. The theory was tested against a wide range of field data from various geographic areas of the United States. The original theory was intended to produce only the average hydraulic exponents for a group of cross sections in a similar type of geologic or hydraulic environment. The theory does predict these average exponents with a reasonable degree of accuracy. An attempt to forecast the exponents at any selected cross section was moderately successful. Empirical equations are more accurate than the minimum variance, Gauckler-Manning, or Chezy methods. Predictions of the exponent of width are most reliable, the exponent of depth fair, and the exponent of mean velocity poor. (Woodard-USGS)

Linear-array photoacoustic imaging using minimum variance-based delay multiply and sum adaptive beamforming algorithm

NASA Astrophysics Data System (ADS)

Mozaffarzadeh, Moein; Mahloojifar, Ali; Orooji, Mahdi; Kratkiewicz, Karl; Adabi, Saba; Nasiriavanaki, Mohammadreza

2018-02-01

In photoacoustic imaging, delay-and-sum (DAS) beamformer is a common beamforming algorithm having a simple implementation. However, it results in a poor resolution and high sidelobes. To address these challenges, a new algorithm namely delay-multiply-and-sum (DMAS) was introduced having lower sidelobes compared to DAS. To improve the resolution of DMAS, a beamformer is introduced using minimum variance (MV) adaptive beamforming combined with DMAS, so-called minimum variance-based DMAS (MVB-DMAS). It is shown that expanding the DMAS equation results in multiple terms representing a DAS algebra. It is proposed to use the MV adaptive beamformer instead of the existing DAS. MVB-DMAS is evaluated numerically and experimentally. In particular, at the depth of 45 mm MVB-DMAS results in about 31, 18, and 8 dB sidelobes reduction compared to DAS, MV, and DMAS, respectively. The quantitative results of the simulations show that MVB-DMAS leads to improvement in full-width-half-maximum about 96%, 94%, and 45% and signal-to-noise ratio about 89%, 15%, and 35% compared to DAS, DMAS, MV, respectively. In particular, at the depth of 33 mm of the experimental images, MVB-DMAS results in about 20 dB sidelobes reduction in comparison with other beamformers.

Mesoscale Gravity Wave Variances from AMSU-A Radiances

NASA Technical Reports Server (NTRS)

Wu, Dong L.

2004-01-01

A variance analysis technique is developed here to extract gravity wave (GW) induced temperature fluctuations from NOAA AMSU-A (Advanced Microwave Sounding Unit-A) radiance measurements. By carefully removing the instrument/measurement noise, the algorithm can produce reliable GW variances with the minimum detectable value as small as 0.1 K2. Preliminary analyses with AMSU-A data show GW variance maps in the stratosphere have very similar distributions to those found with the UARS MLS (Upper Atmosphere Research Satellite Microwave Limb Sounder). However, the AMSU-A offers better horizontal and temporal resolution for observing regional GW variability, such as activity over sub-Antarctic islands.

Magnetic resonance image restoration via dictionary learning under spatially adaptive constraints.

PubMed

Wang, Shanshan; Xia, Yong; Dong, Pei; Feng, David Dagan; Luo, Jianhua; Huang, Qiu

2013-01-01

This paper proposes a spatially adaptive constrained dictionary learning (SAC-DL) algorithm for Rician noise removal in magnitude magnetic resonance (MR) images. This algorithm explores both the strength of dictionary learning to preserve image structures and the robustness of local variance estimation to remove signal-dependent Rician noise. The magnitude image is first separated into a number of partly overlapping image patches. The statistics of each patch are collected and analyzed to obtain a local noise variance. To better adapt to Rician noise, a correction factor is formulated with the local signal-to-noise ratio (SNR). Finally, the trained dictionary is used to denoise each image patch under spatially adaptive constraints. The proposed algorithm has been compared to the popular nonlocal means (NLM) filtering and unbiased NLM (UNLM) algorithm on simulated T1-weighted, T2-weighted and PD-weighted MR images. Our results suggest that the SAC-DL algorithm preserves more image structures while effectively removing the noise than NLM and it is also superior to UNLM at low noise levels.

Statistical properties of the anomalous scaling exponent estimator based on time-averaged mean-square displacement

NASA Astrophysics Data System (ADS)

Sikora, Grzegorz; Teuerle, Marek; Wyłomańska, Agnieszka; Grebenkov, Denis

2017-08-01

The most common way of estimating the anomalous scaling exponent from single-particle trajectories consists of a linear fit of the dependence of the time-averaged mean-square displacement on the lag time at the log-log scale. We investigate the statistical properties of this estimator in the case of fractional Brownian motion (FBM). We determine the mean value, the variance, and the distribution of the estimator. Our theoretical results are confirmed by Monte Carlo simulations. In the limit of long trajectories, the estimator is shown to be asymptotically unbiased, consistent, and with vanishing variance. These properties ensure an accurate estimation of the scaling exponent even from a single (long enough) trajectory. As a consequence, we prove that the usual way to estimate the diffusion exponent of FBM is correct from the statistical point of view. Moreover, the knowledge of the estimator distribution is the first step toward new statistical tests of FBM and toward a more reliable interpretation of the experimental histograms of scaling exponents in microbiology.

Some refinements on the comparison of areal sampling methods via simulation

Treesearch

Jeffrey Gove

2017-01-01

The design of forest inventories and development of new sampling methods useful in such inventories normally have a two-fold target of design unbiasedness and minimum variance in mind. Many considerations such as costs go into the choices of sampling method for operational and other levels of inventory. However, the variance in terms of meeting a specified level of...

A comparison of coronal and interplanetary current sheet inclinations

NASA Technical Reports Server (NTRS)

Behannon, K. W.; Burlaga, L. F.; Hundhausen, A. J.

1983-01-01

The HAO white light K-coronameter observations show that the inclination of the heliospheric current sheet at the base of the corona can be both large (nearly vertical with respect to the solar equator) or small during Cararington rotations 1660 - 1666 and even on a single solar rotation. Voyager 1 and 2 magnetic field observations of crossing of the heliospheric current sheet at distances from the Sun of 1.4 and 2.8 AU. Two cases are considered, one in which the corresponding coronameter data indicate a nearly vertical (north-south) current sheet and another in which a nearly horizontal, near equatorial current sheet is indicated. For the crossings of the vertical current sheet, a variance analysis based on hour averages of the magnetic field data gave a minimum variance direction consistent with a steep inclination. The horizontal current sheet was observed by Voyager as a region of mixed polarity and low speeds lasting several days, consistent with multiple crossings of a horizontal but irregular and fluctuating current sheet at 1.4 AU. However, variance analysis of individual current sheet crossings in this interval using 1.92 see averages did not give minimum variance directions consistent with a horizontal current sheet.

Stretchy binary classification.

PubMed

Toh, Kar-Ann; Lin, Zhiping; Sun, Lei; Li, Zhengguo

2018-01-01

In this article, we introduce an analytic formulation for compressive binary classification. The formulation seeks to solve the least ℓ p -norm of the parameter vector subject to a classification error constraint. An analytic and stretchable estimation is conjectured where the estimation can be viewed as an extension of the pseudoinverse with left and right constructions. Our variance analysis indicates that the estimation based on the left pseudoinverse is unbiased and the estimation based on the right pseudoinverse is biased. Sparseness can be obtained for the biased estimation under certain mild conditions. The proposed estimation is investigated numerically using both synthetic and real-world data. Copyright © 2017 Elsevier Ltd. All rights reserved.

Empirical Bayes Estimation of Semi-parametric Hierarchical Mixture Models for Unbiased Characterization of Polygenic Disease Architectures

PubMed Central

Nishino, Jo; Kochi, Yuta; Shigemizu, Daichi; Kato, Mamoru; Ikari, Katsunori; Ochi, Hidenori; Noma, Hisashi; Matsui, Kota; Morizono, Takashi; Boroevich, Keith A.; Tsunoda, Tatsuhiko; Matsui, Shigeyuki

2018-01-01

Genome-wide association studies (GWAS) suggest that the genetic architecture of complex diseases consists of unexpectedly numerous variants with small effect sizes. However, the polygenic architectures of many diseases have not been well characterized due to lack of simple and fast methods for unbiased estimation of the underlying proportion of disease-associated variants and their effect-size distribution. Applying empirical Bayes estimation of semi-parametric hierarchical mixture models to GWAS summary statistics, we confirmed that schizophrenia was extremely polygenic [~40% of independent genome-wide SNPs are risk variants, most within odds ratio (OR = 1.03)], whereas rheumatoid arthritis was less polygenic (~4 to 8% risk variants, significant portion reaching OR = 1.05 to 1.1). For rheumatoid arthritis, stratified estimations revealed that expression quantitative loci in blood explained large genetic variance, and low- and high-frequency derived alleles were prone to be risk and protective, respectively, suggesting a predominance of deleterious-risk and advantageous-protective mutations. Despite genetic correlation, effect-size distributions for schizophrenia and bipolar disorder differed across allele frequency. These analyses distinguished disease polygenic architectures and provided clues for etiological differences in complex diseases. PMID:29740473

Minimum Variance Distortionless Response Beamformer with Enhanced Nulling Level Control via Dynamic Mutated Artificial Immune System

PubMed Central

Kiong, Tiong Sieh; Salem, S. Balasem; Paw, Johnny Koh Siaw; Sankar, K. Prajindra

2014-01-01

In smart antenna applications, the adaptive beamforming technique is used to cancel interfering signals (placing nulls) and produce or steer a strong beam toward the target signal according to the calculated weight vectors. Minimum variance distortionless response (MVDR) beamforming is capable of determining the weight vectors for beam steering; however, its nulling level on the interference sources remains unsatisfactory. Beamforming can be considered as an optimization problem, such that optimal weight vector should be obtained through computation. Hence, in this paper, a new dynamic mutated artificial immune system (DM-AIS) is proposed to enhance MVDR beamforming for controlling the null steering of interference and increase the signal to interference noise ratio (SINR) for wanted signals. PMID:25003136

Minimum variance distortionless response beamformer with enhanced nulling level control via dynamic mutated artificial immune system.

PubMed

Kiong, Tiong Sieh; Salem, S Balasem; Paw, Johnny Koh Siaw; Sankar, K Prajindra; Darzi, Soodabeh

2014-01-01

In smart antenna applications, the adaptive beamforming technique is used to cancel interfering signals (placing nulls) and produce or steer a strong beam toward the target signal according to the calculated weight vectors. Minimum variance distortionless response (MVDR) beamforming is capable of determining the weight vectors for beam steering; however, its nulling level on the interference sources remains unsatisfactory. Beamforming can be considered as an optimization problem, such that optimal weight vector should be obtained through computation. Hence, in this paper, a new dynamic mutated artificial immune system (DM-AIS) is proposed to enhance MVDR beamforming for controlling the null steering of interference and increase the signal to interference noise ratio (SINR) for wanted signals.

25 CFR 542.18 - How does a gaming operation apply for a variance from the standards of the part?

Code of Federal Regulations, 2010 CFR

2010-04-01

... 25 Indians 2 2010-04-01 2010-04-01 false How does a gaming operation apply for a variance from the standards of the part? 542.18 Section 542.18 Indians NATIONAL INDIAN GAMING COMMISSION, DEPARTMENT OF THE INTERIOR HUMAN SERVICES MINIMUM INTERNAL CONTROL STANDARDS § 542.18 How does a gaming operation apply for a...

Unraveling additive from nonadditive effects using genomic relationship matrices.

PubMed

Muñoz, Patricio R; Resende, Marcio F R; Gezan, Salvador A; Resende, Marcos Deon Vilela; de Los Campos, Gustavo; Kirst, Matias; Huber, Dudley; Peter, Gary F

2014-12-01

The application of quantitative genetics in plant and animal breeding has largely focused on additive models, which may also capture dominance and epistatic effects. Partitioning genetic variance into its additive and nonadditive components using pedigree-based models (P-genomic best linear unbiased predictor) (P-BLUP) is difficult with most commonly available family structures. However, the availability of dense panels of molecular markers makes possible the use of additive- and dominance-realized genomic relationships for the estimation of variance components and the prediction of genetic values (G-BLUP). We evaluated height data from a multifamily population of the tree species Pinus taeda with a systematic series of models accounting for additive, dominance, and first-order epistatic interactions (additive by additive, dominance by dominance, and additive by dominance), using either pedigree- or marker-based information. We show that, compared with the pedigree, use of realized genomic relationships in marker-based models yields a substantially more precise separation of additive and nonadditive components of genetic variance. We conclude that the marker-based relationship matrices in a model including additive and nonadditive effects performed better, improving breeding value prediction. Moreover, our results suggest that, for tree height in this population, the additive and nonadditive components of genetic variance are similar in magnitude. This novel result improves our current understanding of the genetic control and architecture of a quantitative trait and should be considered when developing breeding strategies. Copyright © 2014 by the Genetics Society of America.

A test of source-surface model predictions of heliospheric current sheet inclination

NASA Technical Reports Server (NTRS)

Burton, M. E.; Crooker, N. U.; Siscoe, G. L.; Smith, E. J.

1994-01-01

The orientation of the heliospheric current sheet predicted from a source surface model is compared with the orientation determined from minimum-variance analysis of International Sun-Earth Explorer (ISEE) 3 magnetic field data at 1 AU near solar maximum. Of the 37 cases analyzed, 28 have minimum variance normals that lie orthogonal to the predicted Parker spiral direction. For these cases, the correlation coefficient between the predicted and measured inclinations is 0.6. However, for the subset of 14 cases for which transient signatures (either interplanetary shocks or bidirectional electrons) are absent, the agreement in inclinations improves dramatically, with a correlation coefficient of 0.96. These results validate not only the use of the source surface model as a predictor but also the previously questioned usefulness of minimum variance analysis across complex sector boundaries. In addition, the results imply that interplanetary dynamics have little effect on current sheet inclination at 1 AU. The dependence of the correlation on transient occurrence suggests that the leading edge of a coronal mass ejection (CME), where transient signatures are detected, disrupts the heliospheric current sheet but that the sheet re-forms between the trailing legs of the CME. In this way the global structure of the heliosphere, reflected both in the source surface maps and in the interplanetary sector structure, can be maintained even when the CME occurrence rate is high.

Linear-array photoacoustic imaging using minimum variance-based delay multiply and sum adaptive beamforming algorithm.

PubMed

Mozaffarzadeh, Moein; Mahloojifar, Ali; Orooji, Mahdi; Kratkiewicz, Karl; Adabi, Saba; Nasiriavanaki, Mohammadreza

2018-02-01

In photoacoustic imaging, delay-and-sum (DAS) beamformer is a common beamforming algorithm having a simple implementation. However, it results in a poor resolution and high sidelobes. To address these challenges, a new algorithm namely delay-multiply-and-sum (DMAS) was introduced having lower sidelobes compared to DAS. To improve the resolution of DMAS, a beamformer is introduced using minimum variance (MV) adaptive beamforming combined with DMAS, so-called minimum variance-based DMAS (MVB-DMAS). It is shown that expanding the DMAS equation results in multiple terms representing a DAS algebra. It is proposed to use the MV adaptive beamformer instead of the existing DAS. MVB-DMAS is evaluated numerically and experimentally. In particular, at the depth of 45 mm MVB-DMAS results in about 31, 18, and 8 dB sidelobes reduction compared to DAS, MV, and DMAS, respectively. The quantitative results of the simulations show that MVB-DMAS leads to improvement in full-width-half-maximum about 96%, 94%, and 45% and signal-to-noise ratio about 89%, 15%, and 35% compared to DAS, DMAS, MV, respectively. In particular, at the depth of 33 mm of the experimental images, MVB-DMAS results in about 20 dB sidelobes reduction in comparison with other beamformers. (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

Unbiased Quantitative Models of Protein Translation Derived from Ribosome Profiling Data

PubMed Central

Gritsenko, Alexey A.; Hulsman, Marc; Reinders, Marcel J. T.; de Ridder, Dick

2015-01-01

Translation of RNA to protein is a core process for any living organism. While for some steps of this process the effect on protein production is understood, a holistic understanding of translation still remains elusive. In silico modelling is a promising approach for elucidating the process of protein synthesis. Although a number of computational models of the process have been proposed, their application is limited by the assumptions they make. Ribosome profiling (RP), a relatively new sequencing-based technique capable of recording snapshots of the locations of actively translating ribosomes, is a promising source of information for deriving unbiased data-driven translation models. However, quantitative analysis of RP data is challenging due to high measurement variance and the inability to discriminate between the number of ribosomes measured on a gene and their speed of translation. We propose a solution in the form of a novel multi-scale interpretation of RP data that allows for deriving models with translation dynamics extracted from the snapshots. We demonstrate the usefulness of this approach by simultaneously determining for the first time per-codon translation elongation and per-gene translation initiation rates of Saccharomyces cerevisiae from RP data for two versions of the Totally Asymmetric Exclusion Process (TASEP) model of translation. We do this in an unbiased fashion, by fitting the models using only RP data with a novel optimization scheme based on Monte Carlo simulation to keep the problem tractable. The fitted models match the data significantly better than existing models and their predictions show better agreement with several independent protein abundance datasets than existing models. Results additionally indicate that the tRNA pool adaptation hypothesis is incomplete, with evidence suggesting that tRNA post-transcriptional modifications and codon context may play a role in determining codon elongation rates. PMID:26275099

Unbiased Quantitative Models of Protein Translation Derived from Ribosome Profiling Data.

PubMed

Gritsenko, Alexey A; Hulsman, Marc; Reinders, Marcel J T; de Ridder, Dick

2015-08-01

Translation of RNA to protein is a core process for any living organism. While for some steps of this process the effect on protein production is understood, a holistic understanding of translation still remains elusive. In silico modelling is a promising approach for elucidating the process of protein synthesis. Although a number of computational models of the process have been proposed, their application is limited by the assumptions they make. Ribosome profiling (RP), a relatively new sequencing-based technique capable of recording snapshots of the locations of actively translating ribosomes, is a promising source of information for deriving unbiased data-driven translation models. However, quantitative analysis of RP data is challenging due to high measurement variance and the inability to discriminate between the number of ribosomes measured on a gene and their speed of translation. We propose a solution in the form of a novel multi-scale interpretation of RP data that allows for deriving models with translation dynamics extracted from the snapshots. We demonstrate the usefulness of this approach by simultaneously determining for the first time per-codon translation elongation and per-gene translation initiation rates of Saccharomyces cerevisiae from RP data for two versions of the Totally Asymmetric Exclusion Process (TASEP) model of translation. We do this in an unbiased fashion, by fitting the models using only RP data with a novel optimization scheme based on Monte Carlo simulation to keep the problem tractable. The fitted models match the data significantly better than existing models and their predictions show better agreement with several independent protein abundance datasets than existing models. Results additionally indicate that the tRNA pool adaptation hypothesis is incomplete, with evidence suggesting that tRNA post-transcriptional modifications and codon context may play a role in determining codon elongation rates.

Precision of systematic and random sampling in clustered populations: habitat patches and aggregating organisms.

PubMed

McGarvey, Richard; Burch, Paul; Matthews, Janet M

2016-01-01

Natural populations of plants and animals spatially cluster because (1) suitable habitat is patchy, and (2) within suitable habitat, individuals aggregate further into clusters of higher density. We compare the precision of random and systematic field sampling survey designs under these two processes of species clustering. Second, we evaluate the performance of 13 estimators for the variance of the sample mean from a systematic survey. Replicated simulated surveys, as counts from 100 transects, allocated either randomly or systematically within the study region, were used to estimate population density in six spatial point populations including habitat patches and Matérn circular clustered aggregations of organisms, together and in combination. The standard one-start aligned systematic survey design, a uniform 10 x 10 grid of transects, was much more precise. Variances of the 10 000 replicated systematic survey mean densities were one-third to one-fifth of those from randomly allocated transects, implying transect sample sizes giving equivalent precision by random survey would need to be three to five times larger. Organisms being restricted to patches of habitat was alone sufficient to yield this precision advantage for the systematic design. But this improved precision for systematic sampling in clustered populations is underestimated by standard variance estimators used to compute confidence intervals. True variance for the survey sample mean was computed from the variance of 10 000 simulated survey mean estimates. Testing 10 published and three newly proposed variance estimators, the two variance estimators (v) that corrected for inter-transect correlation (ν₈ and ν(W)) were the most accurate and also the most precise in clustered populations. These greatly outperformed the two "post-stratification" variance estimators (ν₂ and ν₃) that are now more commonly applied in systematic surveys. Similar variance estimator performance rankings were found with a second differently generated set of spatial point populations, ν₈ and ν(W) again being the best performers in the longer-range autocorrelated populations. However, no systematic variance estimators tested were free from bias. On balance, systematic designs bring more narrow confidence intervals in clustered populations, while random designs permit unbiased estimates of (often wider) confidence interval. The search continues for better estimators of sampling variance for the systematic survey mean.

Vegetation greenness impacts on maximum and minimum temperatures in northeast Colorado

USGS Publications Warehouse

Hanamean, J. R.; Pielke, R.A.; Castro, C. L.; Ojima, D.S.; Reed, Bradley C.; Gao, Z.

2003-01-01

The impact of vegetation on the microclimate has not been adequately considered in the analysis of temperature forecasting and modelling. To fill part of this gap, the following study was undertaken.A daily 850–700 mb layer mean temperature, computed from the National Center for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) reanalysis, and satellite-derived greenness values, as defined by NDVI (Normalised Difference Vegetation Index), were correlated with surface maximum and minimum temperatures at six sites in northeast Colorado for the years 1989–98. The NDVI values, representing landscape greenness, act as a proxy for latent heat partitioning via transpiration. These sites encompass a wide array of environments, from irrigated-urban to short-grass prairie. The explained variance (r2 value) of surface maximum and minimum temperature by only the 850–700 mb layer mean temperature was subtracted from the corresponding explained variance by the 850–700 mb layer mean temperature and NDVI values. The subtraction shows that by including NDVI values in the analysis, the r2 values, and thus the degree of explanation of the surface temperatures, increase by a mean of 6% for the maxima and 8% for the minima over the period March–October. At most sites, there is a seasonal dependence in the explained variance of the maximum temperatures because of the seasonal cycle of plant growth and senescence. Between individual sites, the highest increase in explained variance occurred at the site with the least amount of anthropogenic influence. This work suggests the vegetation state needs to be included as a factor in surface temperature forecasting, numerical modeling, and climate change assessments.

Change in mean temperature as a predictor of extreme temperature change in the Asia-Pacific region

NASA Astrophysics Data System (ADS)

Griffiths, G. M.; Chambers, L. E.; Haylock, M. R.; Manton, M. J.; Nicholls, N.; Baek, H.-J.; Choi, Y.; della-Marta, P. M.; Gosai, A.; Iga, N.; Lata, R.; Laurent, V.; Maitrepierre, L.; Nakamigawa, H.; Ouprasitwong, N.; Solofa, D.; Tahani, L.; Thuy, D. T.; Tibig, L.; Trewin, B.; Vediapan, K.; Zhai, P.

2005-08-01

Trends (1961-2003) in daily maximum and minimum temperatures, extremes and variance were found to be spatially coherent across the Asia-Pacific region. The majority of stations exhibited significant trends: increases in mean maximum and mean minimum temperature, decreases in cold nights and cool days, and increases in warm nights. No station showed a significant increase in cold days or cold nights, but a few sites showed significant decreases in hot days and warm nights. Significant decreases were observed in both maximum and minimum temperature standard deviation in China, Korea and some stations in Japan (probably reflecting urbanization effects), but also for some Thailand and coastal Australian sites. The South Pacific convergence zone (SPCZ) region between Fiji and the Solomon Islands showed a significant increase in maximum temperature variability.Correlations between mean temperature and the frequency of extreme temperatures were strongest in the tropical Pacific Ocean from French Polynesia to Papua New Guinea, Malaysia, the Philippines, Thailand and southern Japan. Correlations were weaker at continental or higher latitude locations, which may partly reflect urbanization.For non-urban stations, the dominant distribution change for both maximum and minimum temperature involved a change in the mean, impacting on one or both extremes, with no change in standard deviation. This occurred from French Polynesia to Papua New Guinea (except for maximum temperature changes near the SPCZ), in Malaysia, the Philippines, and several outlying Japanese islands. For urbanized stations the dominant change was a change in the mean and variance, impacting on one or both extremes. This result was particularly evident for minimum temperature.The results presented here, for non-urban tropical and maritime locations in the Asia-Pacific region, support the hypothesis that changes in mean temperature may be used to predict changes in extreme temperatures. At urbanized or higher latitude locations, changes in variance should be incorporated.

Obtaining Reliable Predictions of Terrestrial Energy Coupling From Real-Time Solar Wind Measurement

NASA Technical Reports Server (NTRS)

Weimer, Daniel R.

2001-01-01

The first draft of a manuscript titled "Variable time delays in the propagation of the interplanetary magnetic field" has been completed, for submission to the Journal of Geophysical Research. In the preparation of this manuscript all data and analysis programs had been updated to the highest temporal resolution possible, at 16 seconds or better. The program which computes the "measured" IMF propagation time delays from these data has also undergone another improvement. In another significant development, a technique has been developed in order to predict IMF phase plane orientations, and the resulting time delays, using only measurements from a single satellite at L1. The "minimum variance" method is used for this computation. Further work will be done on optimizing the choice of several parameters for the minimum variance calculation.

A strategy to find minimal energy nanocluster structures.

PubMed

Rogan, José; Varas, Alejandro; Valdivia, Juan Alejandro; Kiwi, Miguel

2013-11-05

An unbiased strategy to search for the global and local minimal energy structures of free standing nanoclusters is presented. Our objectives are twofold: to find a diverse set of low lying local minima, as well as the global minimum. To do so, we use massively the fast inertial relaxation engine algorithm as an efficient local minimizer. This procedure turns out to be quite efficient to reach the global minimum, and also most of the local minima. We test the method with the Lennard-Jones (LJ) potential, for which an abundant literature does exist, and obtain novel results, which include a new local minimum for LJ13 , 10 new local minima for LJ14 , and thousands of new local minima for 15≤N≤65. Insights on how to choose the initial configurations, analyzing the effectiveness of the method in reaching low-energy structures, including the global minimum, are developed as a function of the number of atoms of the cluster. Also, a novel characterization of the potential energy surface, analyzing properties of the local minima basins, is provided. The procedure constitutes a promising tool to generate a diverse set of cluster conformations, both two- and three-dimensional, that can be used as an input for refinement by means of ab initio methods. Copyright © 2013 Wiley Periodicals, Inc.

Predicting longitudinal trajectories of health probabilities with random-effects multinomial logit regression.

PubMed

Liu, Xian; Engel, Charles C

2012-12-20

Researchers often encounter longitudinal health data characterized with three or more ordinal or nominal categories. Random-effects multinomial logit models are generally applied to account for potential lack of independence inherent in such clustered data. When parameter estimates are used to describe longitudinal processes, however, random effects, both between and within individuals, need to be retransformed for correctly predicting outcome probabilities. This study attempts to go beyond existing work by developing a retransformation method that derives longitudinal growth trajectories of unbiased health probabilities. We estimated variances of the predicted probabilities by using the delta method. Additionally, we transformed the covariates' regression coefficients on the multinomial logit function, not substantively meaningful, to the conditional effects on the predicted probabilities. The empirical illustration uses the longitudinal data from the Asset and Health Dynamics among the Oldest Old. Our analysis compared three sets of the predicted probabilities of three health states at six time points, obtained from, respectively, the retransformation method, the best linear unbiased prediction, and the fixed-effects approach. The results demonstrate that neglect of retransforming random errors in the random-effects multinomial logit model results in severely biased longitudinal trajectories of health probabilities as well as overestimated effects of covariates on the probabilities. Copyright © 2012 John Wiley & Sons, Ltd.

Comparison of Kasai Autocorrelation and Maximum Likelihood Estimators for Doppler Optical Coherence Tomography

PubMed Central

Chan, Aaron C.; Srinivasan, Vivek J.

2013-01-01

In optical coherence tomography (OCT) and ultrasound, unbiased Doppler frequency estimators with low variance are desirable for blood velocity estimation. Hardware improvements in OCT mean that ever higher acquisition rates are possible, which should also, in principle, improve estimation performance. Paradoxically, however, the widely used Kasai autocorrelation estimator’s performance worsens with increasing acquisition rate. We propose that parametric estimators based on accurate models of noise statistics can offer better performance. We derive a maximum likelihood estimator (MLE) based on a simple additive white Gaussian noise model, and show that it can outperform the Kasai autocorrelation estimator. In addition, we also derive the Cramer Rao lower bound (CRLB), and show that the variance of the MLE approaches the CRLB for moderate data lengths and noise levels. We note that the MLE performance improves with longer acquisition time, and remains constant or improves with higher acquisition rates. These qualities may make it a preferred technique as OCT imaging speed continues to improve. Finally, our work motivates the development of more general parametric estimators based on statistical models of decorrelation noise. PMID:23446044

Statistical guides to estimating the number of undiscovered mineral deposits: an example with porphyry copper deposits

USGS Publications Warehouse

Singer, Donald A.; Menzie, W.D.; Cheng, Qiuming; Bonham-Carter, G. F.

2005-01-01

Estimating numbers of undiscovered mineral deposits is a fundamental part of assessing mineral resources. Some statistical tools can act as guides to low variance, unbiased estimates of the number of deposits. The primary guide is that the estimates must be consistent with the grade and tonnage models. Another statistical guide is the deposit density (i.e., the number of deposits per unit area of permissive rock in well-explored control areas). Preliminary estimates and confidence limits of the number of undiscovered deposits in a tract of given area may be calculated using linear regression and refined using frequency distributions with appropriate parameters. A Poisson distribution leads to estimates having lower relative variances than the regression estimates and implies a random distribution of deposits. Coefficients of variation are used to compare uncertainties of negative binomial, Poisson, or MARK3 empirical distributions that have the same expected number of deposits as the deposit density. Statistical guides presented here allow simple yet robust estimation of the number of undiscovered deposits in permissive terranes. 

Minimum number of measurements for evaluating Bertholletia excelsa.

PubMed

Baldoni, A B; Tonini, H; Tardin, F D; Botelho, S C C; Teodoro, P E

2017-09-27

Repeatability studies on fruit species are of great importance to identify the minimum number of measurements necessary to accurately select superior genotypes. This study aimed to identify the most efficient method to estimate the repeatability coefficient (r) and predict the minimum number of measurements needed for a more accurate evaluation of Brazil nut tree (Bertholletia excelsa) genotypes based on fruit yield. For this, we assessed the number of fruits and dry mass of seeds of 75 Brazil nut genotypes, from native forest, located in the municipality of Itaúba, MT, for 5 years. To better estimate r, four procedures were used: analysis of variance (ANOVA), principal component analysis based on the correlation matrix (CPCOR), principal component analysis based on the phenotypic variance and covariance matrix (CPCOV), and structural analysis based on the correlation matrix (mean r - AECOR). There was a significant effect of genotypes and measurements, which reveals the need to study the minimum number of measurements for selecting superior Brazil nut genotypes for a production increase. Estimates of r by ANOVA were lower than those observed with the principal component methodology and close to AECOR. The CPCOV methodology provided the highest estimate of r, which resulted in a lower number of measurements needed to identify superior Brazil nut genotypes for the number of fruits and dry mass of seeds. Based on this methodology, three measurements are necessary to predict the true value of the Brazil nut genotypes with a minimum accuracy of 85%.

On the design of classifiers for crop inventories

NASA Technical Reports Server (NTRS)

Heydorn, R. P.; Takacs, H. C.

1986-01-01

Crop proportion estimators that use classifications of satellite data to correct, in an additive way, a given estimate acquired from ground observations are discussed. A linear version of these estimators is optimal, in terms of minimum variance, when the regression of the ground observations onto the satellite observations in linear. When this regression is not linear, but the reverse regression (satellite observations onto ground observations) is linear, the estimator is suboptimal but still has certain appealing variance properties. In this paper expressions are derived for those regressions which relate the intercepts and slopes to conditional classification probabilities. These expressions are then used to discuss the question of classifier designs that can lead to low-variance crop proportion estimates. Variance expressions for these estimates in terms of classifier omission and commission errors are also derived.

Variance-reduced simulation of lattice discrete-time Markov chains with applications in reaction networks

NASA Astrophysics Data System (ADS)

Maginnis, P. A.; West, M.; Dullerud, G. E.

2016-10-01

We propose an algorithm to accelerate Monte Carlo simulation for a broad class of stochastic processes. Specifically, the class of countable-state, discrete-time Markov chains driven by additive Poisson noise, or lattice discrete-time Markov chains. In particular, this class includes simulation of reaction networks via the tau-leaping algorithm. To produce the speedup, we simulate pairs of fair-draw trajectories that are negatively correlated. Thus, when averaged, these paths produce an unbiased Monte Carlo estimator that has reduced variance and, therefore, reduced error. Numerical results for three example systems included in this work demonstrate two to four orders of magnitude reduction of mean-square error. The numerical examples were chosen to illustrate different application areas and levels of system complexity. The areas are: gene expression (affine state-dependent rates), aerosol particle coagulation with emission and human immunodeficiency virus infection (both with nonlinear state-dependent rates). Our algorithm views the system dynamics as a ;black-box;, i.e., we only require control of pseudorandom number generator inputs. As a result, typical codes can be retrofitted with our algorithm using only minor changes. We prove several analytical results. Among these, we characterize the relationship of covariances between paths in the general nonlinear state-dependent intensity rates case, and we prove variance reduction of mean estimators in the special case of affine intensity rates.

Optimal multi-type sensor placement for response and excitation reconstruction

NASA Astrophysics Data System (ADS)

Zhang, C. D.; Xu, Y. L.

2016-01-01

The need to perform dynamic response reconstruction always arises as the measurement of structural response is often limited to a few locations, especially for a large civil structure. Besides, it is usually very difficult, if not impossible, to measure external excitations under the operation condition of a structure. This study presents an algorithm for optimal placement of multi-type sensors, including strain gauges, displacement transducers and accelerometers, for the best reconstruction of responses of key structural components where there are no sensors installed and the best estimation of external excitations acting on the structure at the same time. The algorithm is developed in the framework of Kalman filter with unknown excitation, in which minimum-variance unbiased estimates of the generalized state of the structure and the external excitations are obtained by virtue of limited sensor measurements. The structural responses of key locations without sensors can then be reconstructed with the estimated generalized state and excitation. The asymptotic stability feature of the filter is utilized for optimal sensor placement. The number and spatial location of the multi-type sensors are determined by adding the optimal sensor which gains the maximal reduction of the estimation error of reconstructed responses. For the given mode number in response reconstruction and the given locations of external excitations, the optimal multi-sensor placement achieved by the proposed method is independent of the type and time evolution of external excitation. A simply-supported overhanging steel beam under multiple types of excitation is numerically studied to demonstrate the feasibility and superiority of the proposed method, and the experimental work is then carried out to testify the effectiveness of the proposed method.

Nutrients discharged to the Mississippi River from eastern Iowa watersheds, 1996-1997

USGS Publications Warehouse

Becher, Kent D.; Schnoebelen, Douglas J.; Akers, Kimberlee K.

2000-01-01

The introduction of nutrients from chemical fertilizer, animal manure, wastewater, and atmospheric deposition to the eastern Iowa environment creates a large potential for nutrient transport in watersheds. Agriculture constitutes 93 percent of all land use in eastern Iowa. As part of the U.S. Geological Survey National Water Quality Assessment Program, water samples were collected (typically monthly) from six small and six large watersheds in eastern Iowa between March 1996 and September 1997. A Geographic Information System (GIS) was used to determine land use and quantify inputs of nitrogen and phosphorus within the study area. Streamliow from the watersheds is to the Mississippi River. Chemical fertilizer and animal manure account for 92 percent of the estimated total nitrogen and 99.9 percent of the estimated total phosphorus input in the study area. Total nitrogen and total phosphorus loads for 1996 were estimated for nine of the 12 rivers and creeks using a minimum variance unbiased estimator model. A seasonal pattern of concentrations and loads was observed. The greatest concentrations and loads occur in the late spring to early summer in conjunction with row-crop fertilizer applications and spring nmoff and again in the late fall to early winter as vegetation goes into dormancy and additional fertilizer is applied to row-crop fields. The three largest rivers in eastern Iowa transported an estimated total of 79,000 metric tons of total nitrogen and 6,800 metric tons of total phosphorus to the Mississippi River in 1996. The estimated mass of total nitrogen and total phosphorus transported to the Mississippi River represents about 19 percent of all estimated nitrogen and 9 percent of all estimated phosphorus input to the study area.

Finite mixture model: A maximum likelihood estimation approach on time series data

NASA Astrophysics Data System (ADS)

Yen, Phoong Seuk; Ismail, Mohd Tahir; Hamzah, Firdaus Mohamad

2014-09-01

Recently, statistician emphasized on the fitting of finite mixture model by using maximum likelihood estimation as it provides asymptotic properties. In addition, it shows consistency properties as the sample sizes increases to infinity. This illustrated that maximum likelihood estimation is an unbiased estimator. Moreover, the estimate parameters obtained from the application of maximum likelihood estimation have smallest variance as compared to others statistical method as the sample sizes increases. Thus, maximum likelihood estimation is adopted in this paper to fit the two-component mixture model in order to explore the relationship between rubber price and exchange rate for Malaysia, Thailand, Philippines and Indonesia. Results described that there is a negative effect among rubber price and exchange rate for all selected countries.

Minimum-variance Brownian motion control of an optically trapped probe.

PubMed

Huang, Yanan; Zhang, Zhipeng; Menq, Chia-Hsiang

2009-10-20

This paper presents a theoretical and experimental investigation of the Brownian motion control of an optically trapped probe. The Langevin equation is employed to describe the motion of the probe experiencing random thermal force and optical trapping force. Since active feedback control is applied to suppress the probe's Brownian motion, actuator dynamics and measurement delay are included in the equation. The equation of motion is simplified to a first-order linear differential equation and transformed to a discrete model for the purpose of controller design and data analysis. The derived model is experimentally verified by comparing the model prediction to the measured response of a 1.87 microm trapped probe subject to proportional control. It is then employed to design the optimal controller that minimizes the variance of the probe's Brownian motion. Theoretical analysis is derived to evaluate the control performance of a specific optical trap. Both experiment and simulation are used to validate the design as well as theoretical analysis, and to illustrate the performance envelope of the active control. Moreover, adaptive minimum variance control is implemented to maintain the optimal performance in the case in which the system is time varying when operating the actively controlled optical trap in a complex environment.

Cost effective stream-gaging strategies for the Lower Colorado River basin; the Blythe field office operations

USGS Publications Warehouse

Moss, Marshall E.; Gilroy, Edward J.

1980-01-01

This report describes the theoretical developments and illustrates the applications of techniques that recently have been assembled to analyze the cost-effectiveness of federally funded stream-gaging activities in support of the Colorado River compact and subsequent adjudications. The cost effectiveness of 19 stream gages in terms of minimizing the sum of the variances of the errors of estimation of annual mean discharge is explored by means of a sequential-search optimization scheme. The search is conducted over a set of decision variables that describes the number of times that each gaging route is traveled in a year. A gage route is defined as the most expeditious circuit that is made from a field office to visit one or more stream gages and return to the office. The error variance is defined as a function of the frequency of visits to a gage by using optimal estimation theory. Currently a minimum of 12 visits per year is made to any gage. By changing to a six-visit minimum, the same total error variance can be attained for the 19 stations with a budget of 10% less than the current one. Other strategies are also explored. (USGS)

River meanders - Theory of minimum variance

USGS Publications Warehouse

Langbein, Walter Basil; Leopold, Luna Bergere

1966-01-01

Meanders are the result of erosion-deposition processes tending toward the most stable form in which the variability of certain essential properties is minimized. This minimization involves the adjustment of the planimetric geometry and the hydraulic factors of depth, velocity, and local slope.The planimetric geometry of a meander is that of a random walk whose most frequent form minimizes the sum of the squares of the changes in direction in each successive unit length. The direction angles are then sine functions of channel distance. This yields a meander shape typically present in meandering rivers and has the characteristic that the ratio of meander length to average radius of curvature in the bend is 4.7.Depth, velocity, and slope are shown by field observations to be adjusted so as to decrease the variance of shear and the friction factor in a meander curve over that in an otherwise comparable straight reach of the same riverSince theory and observation indicate meanders achieve the minimum variance postulated, it follows that for channels in which alternating pools and riffles occur, meandering is the most probable form of channel geometry and thus is more stable geometry than a straight or nonmeandering alinement.

Point focusing using loudspeaker arrays from the perspective of optimal beamforming.

PubMed

Bai, Mingsian R; Hsieh, Yu-Hao

2015-06-01

Sound focusing is to create a concentrated acoustic field in the region surrounded by a loudspeaker array. This problem was tackled in the previous research via the Helmholtz integral approach, brightness control, acoustic contrast control, etc. In this paper, the same problem was revisited from the perspective of beamforming. A source array model is reformulated in terms of the steering matrix between the source and the field points, which lends itself to the use of beamforming algorithms such as minimum variance distortionless response (MVDR) and linearly constrained minimum variance (LCMV) originally intended for sensor arrays. The beamforming methods are compared with the conventional methods in terms of beam pattern, directional index, and control effort. Objective tests are conducted to assess the audio quality by using perceptual evaluation of audio quality (PEAQ). Experiments of produced sound field and listening tests are conducted in a listening room, with results processed using analysis of variance and regression analysis. In contrast to the conventional energy-based methods, the results have shown that the proposed methods are phase-sensitive in light of the distortionless constraint in formulating the array filters, which helps enhance audio quality and focusing performance.

RFI in hybrid loops - Simulation and experimental results.

NASA Technical Reports Server (NTRS)

Ziemer, R. E.; Nelson, D. R.; Raghavan, H. R.

1972-01-01

A digital simulation of an imperfect second-order hybrid phase-locked loop (HPLL) operating in radio frequency interference (RFI) is described. Its performance is characterized in terms of phase error variance and phase error probability density function (PDF). Monte-Carlo simulation is used to show that the HPLL can be superior to the conventional phase-locked loops in RFI backgrounds when minimum phase error variance is the goodness criterion. Similar experimentally obtained data are given in support of the simulation data.

Eigenspace-based minimum variance adaptive beamformer combined with delay multiply and sum: experimental study

NASA Astrophysics Data System (ADS)

Mozaffarzadeh, Moein; Mahloojifar, Ali; Nasiriavanaki, Mohammadreza; Orooji, Mahdi

2018-02-01

Delay and sum (DAS) is the most common beamforming algorithm in linear-array photoacoustic imaging (PAI) as a result of its simple implementation. However, it leads to a low resolution and high sidelobes. Delay multiply and sum (DMAS) was used to address the incapabilities of DAS, providing a higher image quality. However, the resolution improvement is not well enough compared to eigenspace-based minimum variance (EIBMV). In this paper, the EIBMV beamformer has been combined with DMAS algebra, called EIBMV-DMAS, using the expansion of DMAS algorithm. The proposed method is used as the reconstruction algorithm in linear-array PAI. EIBMV-DMAS is experimentally evaluated where the quantitative and qualitative results show that it outperforms DAS, DMAS and EIBMV. The proposed method degrades the sidelobes for about 365 %, 221 % and 40 %, compared to DAS, DMAS and EIBMV, respectively. Moreover, EIBMV-DMAS improves the SNR about 158 %, 63 % and 20 %, respectively.

Charged particle tracking at Titan, and further applications

NASA Astrophysics Data System (ADS)

Bebesi, Zsofia; Erdos, Geza; Szego, Karoly

2016-04-01

We use the CAPS ion data of Cassini to investigate the dynamics and origin of Titan's atmospheric ions. We developed a 4th order Runge-Kutta method to calculate particle trajectories in a time reversed scenario. The test particle magnetic field environment imitates the curved magnetic environment in the vicinity of Titan. The minimum variance directions along the S/C trajectory have been calculated for all available Titan flybys, and we assumed a homogeneous field that is perpendicular to the minimum variance direction. Using this method the magnetic field lines have been calculated along the flyby orbits so we could select those observational intervals when Cassini and the upper atmosphere of Titan were magnetically connected. We have also taken the Kronian magnetodisc into consideration, and used different upstream magnetic field approximations depending on whether Titan was located inside of the magnetodisc current sheet, or in the lobe regions. We also discuss the code's applicability to comets.

Microstructure of the IMF turbulences at 2.5 AU

NASA Technical Reports Server (NTRS)

Mavromichalaki, H.; Vassilaki, A.; Marmatsouri, L.; Moussas, X.; Quenby, J. J.; Smith, E. J.

1995-01-01

A detailed analysis of small period (15-900 sec) magnetohydrodynamic (MHD) turbulences of the interplanetary magnetic field (IMF) has been made using Pioneer-11 high time resolution data (0.75 sec) inside a Corotating Interaction Region (CIR) at a heliocentric distance of 2.5 AU in 1973. The methods used are the hodogram analysis, the minimum variance matrix analysis and the cohenrence analysis. The minimum variance analysis gives evidence of linear polarized wave modes. Coherence analysis has shown that the field fluctuations are dominated by the magnetosonic fast modes with periods 15 sec to 15 min. However, it is also shown that some small amplitude Alfven waves are present in the trailing edge of this region with characteristic periods (15-200 sec). The observed wave modes are locally generated and possibly attributed to the scattering of Alfven waves energy into random magnetosonic waves.

Optical tomographic detection of rheumatoid arthritis with computer-aided classification schemes

NASA Astrophysics Data System (ADS)

Klose, Christian D.; Klose, Alexander D.; Netz, Uwe; Beuthan, Jürgen; Hielscher, Andreas H.

2009-02-01

A recent research study has shown that combining multiple parameters, drawn from optical tomographic images, leads to better classification results to identifying human finger joints that are affected or not affected by rheumatic arthritis RA. Building up on the research findings of the previous study, this article presents an advanced computer-aided classification approach for interpreting optical image data to detect RA in finger joints. Additional data are used including, for example, maximum and minimum values of the absorption coefficient as well as their ratios and image variances. Classification performances obtained by the proposed method were evaluated in terms of sensitivity, specificity, Youden index and area under the curve AUC. Results were compared to different benchmarks ("gold standard"): magnet resonance, ultrasound and clinical evaluation. Maximum accuracies (AUC=0.88) were reached when combining minimum/maximum-ratios and image variances and using ultrasound as gold standard.

Mutually unbiased product bases for multiple qudits

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

McNulty, Daniel; Pammer, Bogdan; Weigert, Stefan

We investigate the interplay between mutual unbiasedness and product bases for multiple qudits of possibly different dimensions. A product state of such a system is shown to be mutually unbiased to a product basis only if each of its factors is mutually unbiased to all the states which occur in the corresponding factors of the product basis. This result implies both a tight limit on the number of mutually unbiased product bases which the system can support and a complete classification of mutually unbiased product bases for multiple qubits or qutrits. In addition, only maximally entangled states can be mutuallymore » unbiased to a maximal set of mutually unbiased product bases.« less

Two-stage sequential sampling: A neighborhood-free adaptive sampling procedure

USGS Publications Warehouse

Salehi, M.; Smith, D.R.

2005-01-01

Designing an efficient sampling scheme for a rare and clustered population is a challenging area of research. Adaptive cluster sampling, which has been shown to be viable for such a population, is based on sampling a neighborhood of units around a unit that meets a specified condition. However, the edge units produced by sampling neighborhoods have proven to limit the efficiency and applicability of adaptive cluster sampling. We propose a sampling design that is adaptive in the sense that the final sample depends on observed values, but it avoids the use of neighborhoods and the sampling of edge units. Unbiased estimators of population total and its variance are derived using Murthy's estimator. The modified two-stage sampling design is easy to implement and can be applied to a wider range of populations than adaptive cluster sampling. We evaluate the proposed sampling design by simulating sampling of two real biological populations and an artificial population for which the variable of interest took the value either 0 or 1 (e.g., indicating presence and absence of a rare event). We show that the proposed sampling design is more efficient than conventional sampling in nearly all cases. The approach used to derive estimators (Murthy's estimator) opens the door for unbiased estimators to be found for similar sequential sampling designs. ?? 2005 American Statistical Association and the International Biometric Society.

The Cohesive Population Genetics of Molecular Drive

PubMed Central

Ohta, Tomoko; Dover, Gabriel A.

1984-01-01

The long-term population genetics of multigene families is influenced by several biased and unbiased mechanisms of nonreciprocal exchanges (gene conversion, unequal exchanges, transposition) between member genes, often distributed on several chromosomes. These mechanisms cause fluctuations in the copy number of variant genes in an individual and lead to a gradual replacement of an original family of n genes (A) in N number of individuals by a variant gene (a). The process for spreading a variant gene through a family and through a population is called molecular drive. Consideration of the known slow rates of nonreciprocal exchanges predicts that the population variance in the copy number of gene a per individual is small at any given generation during molecular drive. Genotypes at a given generation are expected only to range over a small section of all possible genotypes from one extreme (n number of A) to the other (n number of a). A theory is developed for estimating the size of the population variance by using the concept of identity coefficients. In particular, the variance in the course of spreading of a single mutant gene of a multigene family was investigated in detail, and the theory of identity coefficients at the state of steady decay of genetic variability proved to be useful. Monte Carlo simulations and numerical analysis based on realistic rates of exchange in families of known size reveal the correctness of the theoretical prediction and also assess the effect of bias in turnover. The population dynamics of molecular drive in gradually increasing the mean copy number of a variant gene without the generation of a large variance (population cohesion) is of significance regarding potential interactions between natural selection and molecular drive. PMID:6500260

Genome-Enabled Estimates of Additive and Nonadditive Genetic Variances and Prediction of Apple Phenotypes Across Environments

PubMed Central

Kumar, Satish; Molloy, Claire; Muñoz, Patricio; Daetwyler, Hans; Chagné, David; Volz, Richard

2015-01-01

The nonadditive genetic effects may have an important contribution to total genetic variation of phenotypes, so estimates of both the additive and nonadditive effects are desirable for breeding and selection purposes. Our main objectives were to: estimate additive, dominance and epistatic variances of apple (Malus × domestica Borkh.) phenotypes using relationship matrices constructed from genome-wide dense single nucleotide polymorphism (SNP) markers; and compare the accuracy of genomic predictions using genomic best linear unbiased prediction models with or without including nonadditive genetic effects. A set of 247 clonally replicated individuals was assessed for six fruit quality traits at two sites, and also genotyped using an Illumina 8K SNP array. Across several fruit quality traits, the additive, dominance, and epistatic effects contributed about 30%, 16%, and 19%, respectively, to the total phenotypic variance. Models ignoring nonadditive components yielded upwardly biased estimates of additive variance (heritability) for all traits in this study. The accuracy of genomic predicted genetic values (GEGV) varied from about 0.15 to 0.35 for various traits, and these were almost identical for models with or without including nonadditive effects. However, models including nonadditive genetic effects further reduced the bias of GEGV. Between-site genotypic correlations were high (>0.85) for all traits, and genotype-site interaction accounted for <10% of the phenotypic variability. The accuracy of prediction, when the validation set was present only at one site, was generally similar for both sites, and varied from about 0.50 to 0.85. The prediction accuracies were strongly influenced by trait heritability, and genetic relatedness between the training and validation families. PMID:26497141

The cohesive population genetics of molecular drive.

PubMed

Ohta, T; Dover, G A

1984-10-01

The long-term population genetics of multigene families is influenced by several biased and unbiased mechanisms of nonreciprocal exchanges (gene conversion, unequal exchanges, transposition) between member genes, often distributed on several chromosomes. These mechanisms cause fluctuations in the copy number of variant genes in an individual and lead to a gradual replacement of an original family of n genes (A) in N number of individuals by a variant gene (a). The process for spreading a variant gene through a family and through a population is called molecular drive. Consideration of the known slow rates of nonreciprocal exchanges predicts that the population variance in the copy number of gene a per individual is small at any given generation during molecular drive. Genotypes at a given generation are expected only to range over a small section of all possible genotypes from one extreme (n number of A) to the other (n number of a). A theory is developed for estimating the size of the population variance by using the concept of identity coefficients. In particular, the variance in the course of spreading of a single mutant gene of a multigene family was investigated in detail, and the theory of identity coefficients at the state of steady decay of genetic variability proved to be useful. Monte Carlo simulations and numerical analysis based on realistic rates of exchange in families of known size reveal the correctness of the theoretical prediction and also assess the effect of bias in turnover. The population dynamics of molecular drive in gradually increasing the mean copy number of a variant gene without the generation of a large variance (population cohesion) is of significance regarding potential interactions between natural selection and molecular drive.

Genomic-Enabled Prediction Kernel Models with Random Intercepts for Multi-environment Trials.

PubMed

Cuevas, Jaime; Granato, Italo; Fritsche-Neto, Roberto; Montesinos-Lopez, Osval A; Burgueño, Juan; Bandeira E Sousa, Massaine; Crossa, José

2018-03-28

In this study, we compared the prediction accuracy of the main genotypic effect model (MM) without G×E interactions, the multi-environment single variance G×E deviation model (MDs), and the multi-environment environment-specific variance G×E deviation model (MDe) where the random genetic effects of the lines are modeled with the markers (or pedigree). With the objective of further modeling the genetic residual of the lines, we incorporated the random intercepts of the lines ([Formula: see text]) and generated another three models. Each of these 6 models were fitted with a linear kernel method (Genomic Best Linear Unbiased Predictor, GB) and a Gaussian Kernel (GK) method. We compared these 12 model-method combinations with another two multi-environment G×E interactions models with unstructured variance-covariances (MUC) using GB and GK kernels (4 model-method). Thus, we compared the genomic-enabled prediction accuracy of a total of 16 model-method combinations on two maize data sets with positive phenotypic correlations among environments, and on two wheat data sets with complex G×E that includes some negative and close to zero phenotypic correlations among environments. The two models (MDs and MDE with the random intercept of the lines and the GK method) were computationally efficient and gave high prediction accuracy in the two maize data sets. Regarding the more complex G×E wheat data sets, the prediction accuracy of the model-method combination with G×E, MDs and MDe, including the random intercepts of the lines with GK method had important savings in computing time as compared with the G×E interaction multi-environment models with unstructured variance-covariances but with lower genomic prediction accuracy. Copyright © 2018 Cuevas et al.

Genomic-Enabled Prediction Kernel Models with Random Intercepts for Multi-environment Trials

PubMed Central

Cuevas, Jaime; Granato, Italo; Fritsche-Neto, Roberto; Montesinos-Lopez, Osval A.; Burgueño, Juan; Bandeira e Sousa, Massaine; Crossa, José

2018-01-01

In this study, we compared the prediction accuracy of the main genotypic effect model (MM) without G×E interactions, the multi-environment single variance G×E deviation model (MDs), and the multi-environment environment-specific variance G×E deviation model (MDe) where the random genetic effects of the lines are modeled with the markers (or pedigree). With the objective of further modeling the genetic residual of the lines, we incorporated the random intercepts of the lines (l) and generated another three models. Each of these 6 models were fitted with a linear kernel method (Genomic Best Linear Unbiased Predictor, GB) and a Gaussian Kernel (GK) method. We compared these 12 model-method combinations with another two multi-environment G×E interactions models with unstructured variance-covariances (MUC) using GB and GK kernels (4 model-method). Thus, we compared the genomic-enabled prediction accuracy of a total of 16 model-method combinations on two maize data sets with positive phenotypic correlations among environments, and on two wheat data sets with complex G×E that includes some negative and close to zero phenotypic correlations among environments. The two models (MDs and MDE with the random intercept of the lines and the GK method) were computationally efficient and gave high prediction accuracy in the two maize data sets. Regarding the more complex G×E wheat data sets, the prediction accuracy of the model-method combination with G×E, MDs and MDe, including the random intercepts of the lines with GK method had important savings in computing time as compared with the G×E interaction multi-environment models with unstructured variance-covariances but with lower genomic prediction accuracy. PMID:29476023

Estimating the theoretical semivariogram from finite numbers of measurements

USGS Publications Warehouse

Zheng, Li; Silliman, Stephen E.

2000-01-01

We investigate from a theoretical basis the impacts of the number, location, and correlation among measurement points on the quality of an estimate of the semivariogram. The unbiased nature of the semivariogram estimator ŷ(r) is first established for a general random process Z(x). The variance of ŷZ(r) is then derived as a function of the sampling parameters (the number of measurements and their locations). In applying this function to the case of estimating the semivariograms of the transmissivity and the hydraulic head field, it is shown that the estimation error depends on the number of the data pairs, the correlation among the data pairs (which, in turn, are determined by the form of the underlying semivariogram γ(r)), the relative locations of the data pairs, and the separation distance at which the semivariogram is to be estimated. Thus design of an optimal sampling program for semivariogram estimation should include consideration of each of these factors. Further, the function derived for the variance of ŷZ(r) is useful in determining the reliability of a semivariogram developed from a previously established sampling design.

Evaluation and optimization of sampling errors for the Monte Carlo Independent Column Approximation

NASA Astrophysics Data System (ADS)

Räisänen, Petri; Barker, W. Howard

2004-07-01

The Monte Carlo Independent Column Approximation (McICA) method for computing domain-average broadband radiative fluxes is unbiased with respect to the full ICA, but its flux estimates contain conditional random noise. McICA's sampling errors are evaluated here using a global climate model (GCM) dataset and a correlated-k distribution (CKD) radiation scheme. Two approaches to reduce McICA's sampling variance are discussed. The first is to simply restrict all of McICA's samples to cloudy regions. This avoids wasting precious few samples on essentially homogeneous clear skies. Clear-sky fluxes need to be computed separately for this approach, but this is usually done in GCMs for diagnostic purposes anyway. Second, accuracy can be improved by repeated sampling, and averaging those CKD terms with large cloud radiative effects. Although this naturally increases computational costs over the standard CKD model, random errors for fluxes and heating rates are reduced by typically 50% to 60%, for the present radiation code, when the total number of samples is increased by 50%. When both variance reduction techniques are applied simultaneously, globally averaged flux and heating rate random errors are reduced by a factor of #3.

A genetic modifier suggests that endurance exercise exacerbates Huntington's disease

PubMed Central

Corrochano, Silvia; Blanco, Gonzalo; Williams, Debbie; Wettstein, Jessica; Simon, Michelle; Kumar, Saumya; Moir, Lee; Agnew, Thomas; Stewart, Michelle; Landman, Allison; Kotiadis, Vassilios N; Duchen, Michael R; Wackerhage, Henning; Rubinsztein, David C; Brown, Steve D M

2018-01-01

Abstract Polyglutamine expansions in the huntingtin gene cause Huntington’s disease (HD). Huntingtin is ubiquitously expressed, leading to pathological alterations also in peripheral organs. Variations in the length of the polyglutamine tract explain up to 70% of the age-at-onset variance, with the rest of the variance attributed to genetic and environmental modifiers. To identify novel disease modifiers, we performed an unbiased mutagenesis screen on an HD mouse model, identifying a mutation in the skeletal muscle voltage-gated sodium channel (Scn4a, termed ‘draggen’ mutation) as a novel disease enhancer. Double mutant mice (HD; Scn4aDgn/+) had decreased survival, weight loss and muscle atrophy. Expression patterns show that the main tissue affected is skeletal muscle. Intriguingly, muscles from HD; Scn4aDgn/+ mice showed adaptive changes similar to those found in endurance exercise, including AMPK activation, fibre type switching and upregulation of mitochondrial biogenesis. Therefore, we evaluated the effects of endurance training on HD mice. Crucially, this training regime also led to detrimental effects on HD mice. Overall, these results reveal a novel role for skeletal muscle in modulating systemic HD pathogenesis, suggesting that some forms of physical exercise could be deleterious in neurodegeneration. PMID:29509900

On a stronger-than-best property for best prediction

NASA Astrophysics Data System (ADS)

Teunissen, P. J. G.

2008-03-01

The minimum mean squared error (MMSE) criterion is a popular criterion for devising best predictors. In case of linear predictors, it has the advantage that no further distributional assumptions need to be made, other then about the first- and second-order moments. In the spatial and Earth sciences, it is the best linear unbiased predictor (BLUP) that is used most often. Despite the fact that in this case only the first- and second-order moments need to be known, one often still makes statements about the complete distribution, in particular when statistical testing is involved. For such cases, one can do better than the BLUP, as shown in Teunissen (J Geod. doi: 10.1007/s00190-007-0140-6, 2006), and thus devise predictors that have a smaller MMSE than the BLUP. Hence, these predictors are to be preferred over the BLUP, if one really values the MMSE-criterion. In the present contribution, we will show, however, that the BLUP has another optimality property than the MMSE-property, provided that the distribution is Gaussian. It will be shown that in the Gaussian case, the prediction error of the BLUP has the highest possible probability of all linear unbiased predictors of being bounded in the weighted squared norm sense. This is a stronger property than the often advertised MMSE-property of the BLUP.

Criteria for the use of regression analysis for remote sensing of sediment and pollutants

NASA Technical Reports Server (NTRS)

Whitlock, C. H.; Kuo, C. Y.; Lecroy, S. R.

1982-01-01

An examination of limitations, requirements, and precision of the linear multiple-regression technique for quantification of marine environmental parameters is conducted. Both environmental and optical physics conditions have been defined for which an exact solution to the signal response equations is of the same form as the multiple regression equation. Various statistical parameters are examined to define a criteria for selection of an unbiased fit when upwelled radiance values contain error and are correlated with each other. Field experimental data are examined to define data smoothing requirements in order to satisfy the criteria of Daniel and Wood (1971). Recommendations are made concerning improved selection of ground-truth locations to maximize variance and to minimize physical errors associated with the remote sensing experiment.

An analytic technique for statistically modeling random atomic clock errors in estimation

NASA Technical Reports Server (NTRS)

Fell, P. J.

1981-01-01

Minimum variance estimation requires that the statistics of random observation errors be modeled properly. If measurements are derived through the use of atomic frequency standards, then one source of error affecting the observable is random fluctuation in frequency. This is the case, for example, with range and integrated Doppler measurements from satellites of the Global Positioning and baseline determination for geodynamic applications. An analytic method is presented which approximates the statistics of this random process. The procedure starts with a model of the Allan variance for a particular oscillator and develops the statistics of range and integrated Doppler measurements. A series of five first order Markov processes is used to approximate the power spectral density obtained from the Allan variance.

Approximate sample size formulas for the two-sample trimmed mean test with unequal variances.

PubMed

Luh, Wei-Ming; Guo, Jiin-Huarng

2007-05-01

Yuen's two-sample trimmed mean test statistic is one of the most robust methods to apply when variances are heterogeneous. The present study develops formulas for the sample size required for the test. The formulas are applicable for the cases of unequal variances, non-normality and unequal sample sizes. Given the specified alpha and the power (1-beta), the minimum sample size needed by the proposed formulas under various conditions is less than is given by the conventional formulas. Moreover, given a specified size of sample calculated by the proposed formulas, simulation results show that Yuen's test can achieve statistical power which is generally superior to that of the approximate t test. A numerical example is provided.

Collinearity and Causal Diagrams: A Lesson on the Importance of Model Specification.

PubMed

Schisterman, Enrique F; Perkins, Neil J; Mumford, Sunni L; Ahrens, Katherine A; Mitchell, Emily M

2017-01-01

Correlated data are ubiquitous in epidemiologic research, particularly in nutritional and environmental epidemiology where mixtures of factors are often studied. Our objectives are to demonstrate how highly correlated data arise in epidemiologic research and provide guidance, using a directed acyclic graph approach, on how to proceed analytically when faced with highly correlated data. We identified three fundamental structural scenarios in which high correlation between a given variable and the exposure can arise: intermediates, confounders, and colliders. For each of these scenarios, we evaluated the consequences of increasing correlation between the given variable and the exposure on the bias and variance for the total effect of the exposure on the outcome using unadjusted and adjusted models. We derived closed-form solutions for continuous outcomes using linear regression and empirically present our findings for binary outcomes using logistic regression. For models properly specified, total effect estimates remained unbiased even when there was almost perfect correlation between the exposure and a given intermediate, confounder, or collider. In general, as the correlation increased, the variance of the parameter estimate for the exposure in the adjusted models increased, while in the unadjusted models, the variance increased to a lesser extent or decreased. Our findings highlight the importance of considering the causal framework under study when specifying regression models. Strategies that do not take into consideration the causal structure may lead to biased effect estimation for the original question of interest, even under high correlation.

Estimating the dose response relationship for occupational radiation exposure measured with minimum detection level.

PubMed

Xue, Xiaonan; Shore, Roy E; Ye, Xiangyang; Kim, Mimi Y

2004-10-01

Occupational exposures are often recorded as zero when the exposure is below the minimum detection level (BMDL). This can lead to an underestimation of the doses received by individuals and can lead to biased estimates of risk in occupational epidemiologic studies. The extent of the exposure underestimation is increased with the magnitude of the minimum detection level (MDL) and the frequency of monitoring. This paper uses multiple imputation methods to impute values for the missing doses due to BMDL. A Gibbs sampling algorithm is developed to implement the method, which is applied to two distinct scenarios: when dose information is available for each measurement (but BMDL is recorded as zero or some other arbitrary value), or when the dose information available represents the summation of a series of measurements (e.g., only yearly cumulative exposure is available but based on, say, weekly measurements). Then the average of the multiple imputed exposure realizations for each individual is used to obtain an unbiased estimate of the relative risk associated with exposure. Simulation studies are used to evaluate the performance of the estimators. As an illustration, the method is applied to a sample of historical occupational radiation exposure data from the Oak Ridge National Laboratory.

Non-additive genetic variation in growth, carcass and fertility traits of beef cattle.

PubMed

Bolormaa, Sunduimijid; Pryce, Jennie E; Zhang, Yuandan; Reverter, Antonio; Barendse, William; Hayes, Ben J; Goddard, Michael E

2015-04-02

A better understanding of non-additive variance could lead to increased knowledge on the genetic control and physiology of quantitative traits, and to improved prediction of the genetic value and phenotype of individuals. Genome-wide panels of single nucleotide polymorphisms (SNPs) have been mainly used to map additive effects for quantitative traits, but they can also be used to investigate non-additive effects. We estimated dominance and epistatic effects of SNPs on various traits in beef cattle and the variance explained by dominance, and quantified the increase in accuracy of phenotype prediction by including dominance deviations in its estimation. Genotype data (729 068 real or imputed SNPs) and phenotypes on up to 16 traits of 10 191 individuals from Bos taurus, Bos indicus and composite breeds were used. A genome-wide association study was performed by fitting the additive and dominance effects of single SNPs. The dominance variance was estimated by fitting a dominance relationship matrix constructed from the 729 068 SNPs. The accuracy of predicted phenotypic values was evaluated by best linear unbiased prediction using the additive and dominance relationship matrices. Epistatic interactions (additive × additive) were tested between each of the 28 SNPs that are known to have additive effects on multiple traits, and each of the other remaining 729 067 SNPs. The number of significant dominance effects was greater than expected by chance and most of them were in the direction that is presumed to increase fitness and in the opposite direction to inbreeding depression. Estimates of dominance variance explained by SNPs varied widely between traits, but had large standard errors. The median dominance variance across the 16 traits was equal to 5% of the phenotypic variance. Including a dominance deviation in the prediction did not significantly increase its accuracy for any of the phenotypes. The number of additive × additive epistatic effects that were statistically significant was greater than expected by chance. Significant dominance and epistatic effects occur for growth, carcass and fertility traits in beef cattle but they are difficult to estimate precisely and including them in phenotype prediction does not increase its accuracy.

Source-space ICA for MEG source imaging.

PubMed

Jonmohamadi, Yaqub; Jones, Richard D

2016-02-01

One of the most widely used approaches in electroencephalography/magnetoencephalography (MEG) source imaging is application of an inverse technique (such as dipole modelling or sLORETA) on the component extracted by independent component analysis (ICA) (sensor-space ICA + inverse technique). The advantage of this approach over an inverse technique alone is that it can identify and localize multiple concurrent sources. Among inverse techniques, the minimum-variance beamformers offer a high spatial resolution. However, in order to have both high spatial resolution of beamformer and be able to take on multiple concurrent sources, sensor-space ICA + beamformer is not an ideal combination. We propose source-space ICA for MEG as a powerful alternative approach which can provide the high spatial resolution of the beamformer and handle multiple concurrent sources. The concept of source-space ICA for MEG is to apply the beamformer first and then singular value decomposition + ICA. In this paper we have compared source-space ICA with sensor-space ICA both in simulation and real MEG. The simulations included two challenging scenarios of correlated/concurrent cluster sources. Source-space ICA provided superior performance in spatial reconstruction of source maps, even though both techniques performed equally from a temporal perspective. Real MEG from two healthy subjects with visual stimuli were also used to compare performance of sensor-space ICA and source-space ICA. We have also proposed a new variant of minimum-variance beamformer called weight-normalized linearly-constrained minimum-variance with orthonormal lead-field. As sensor-space ICA-based source reconstruction is popular in EEG and MEG imaging, and given that source-space ICA has superior spatial performance, it is expected that source-space ICA will supersede its predecessor in many applications.

Minimum number of clusters and comparison of analysis methods for cross sectional stepped wedge cluster randomised trials with binary outcomes: A simulation study.

PubMed

Barker, Daniel; D'Este, Catherine; Campbell, Michael J; McElduff, Patrick

2017-03-09

Stepped wedge cluster randomised trials frequently involve a relatively small number of clusters. The most common frameworks used to analyse data from these types of trials are generalised estimating equations and generalised linear mixed models. A topic of much research into these methods has been their application to cluster randomised trial data and, in particular, the number of clusters required to make reasonable inferences about the intervention effect. However, for stepped wedge trials, which have been claimed by many researchers to have a statistical power advantage over the parallel cluster randomised trial, the minimum number of clusters required has not been investigated. We conducted a simulation study where we considered the most commonly used methods suggested in the literature to analyse cross-sectional stepped wedge cluster randomised trial data. We compared the per cent bias, the type I error rate and power of these methods in a stepped wedge trial setting with a binary outcome, where there are few clusters available and when the appropriate adjustment for a time trend is made, which by design may be confounding the intervention effect. We found that the generalised linear mixed modelling approach is the most consistent when few clusters are available. We also found that none of the common analysis methods for stepped wedge trials were both unbiased and maintained a 5% type I error rate when there were only three clusters. Of the commonly used analysis approaches, we recommend the generalised linear mixed model for small stepped wedge trials with binary outcomes. We also suggest that in a stepped wedge design with three steps, at least two clusters be randomised at each step, to ensure that the intervention effect estimator maintains the nominal 5% significance level and is also reasonably unbiased.

Design of a compensation for an ARMA model of a discrete time system. M.S. Thesis

NASA Technical Reports Server (NTRS)

Mainemer, C. I.

1978-01-01

The design of an optimal dynamic compensator for a multivariable discrete time system is studied. Also the design of compensators to achieve minimum variance control strategies for single input single output systems is analyzed. In the first problem the initial conditions of the plant are random variables with known first and second order moments, and the cost is the expected value of the standard cost, quadratic in the states and controls. The compensator is based on the minimum order Luenberger observer and it is found optimally by minimizing a performance index. Necessary and sufficient conditions for optimality of the compensator are derived. The second problem is solved in three different ways; two of them working directly in the frequency domain and one working in the time domain. The first and second order moments of the initial conditions are irrelevant to the solution. Necessary and sufficient conditions are derived for the compensator to minimize the variance of the output.

Propensity score analysis with partially observed covariates: How should multiple imputation be used?

PubMed

Leyrat, Clémence; Seaman, Shaun R; White, Ian R; Douglas, Ian; Smeeth, Liam; Kim, Joseph; Resche-Rigon, Matthieu; Carpenter, James R; Williamson, Elizabeth J

2017-01-01

Inverse probability of treatment weighting is a popular propensity score-based approach to estimate marginal treatment effects in observational studies at risk of confounding bias. A major issue when estimating the propensity score is the presence of partially observed covariates. Multiple imputation is a natural approach to handle missing data on covariates: covariates are imputed and a propensity score analysis is performed in each imputed dataset to estimate the treatment effect. The treatment effect estimates from each imputed dataset are then combined to obtain an overall estimate. We call this method MIte. However, an alternative approach has been proposed, in which the propensity scores are combined across the imputed datasets (MIps). Therefore, there are remaining uncertainties about how to implement multiple imputation for propensity score analysis: (a) should we apply Rubin's rules to the inverse probability of treatment weighting treatment effect estimates or to the propensity score estimates themselves? (b) does the outcome have to be included in the imputation model? (c) how should we estimate the variance of the inverse probability of treatment weighting estimator after multiple imputation? We studied the consistency and balancing properties of the MIte and MIps estimators and performed a simulation study to empirically assess their performance for the analysis of a binary outcome. We also compared the performance of these methods to complete case analysis and the missingness pattern approach, which uses a different propensity score model for each pattern of missingness, and a third multiple imputation approach in which the propensity score parameters are combined rather than the propensity scores themselves (MIpar). Under a missing at random mechanism, complete case and missingness pattern analyses were biased in most cases for estimating the marginal treatment effect, whereas multiple imputation approaches were approximately unbiased as long as the outcome was included in the imputation model. Only MIte was unbiased in all the studied scenarios and Rubin's rules provided good variance estimates for MIte. The propensity score estimated in the MIte approach showed good balancing properties. In conclusion, when using multiple imputation in the inverse probability of treatment weighting context, MIte with the outcome included in the imputation model is the preferred approach.

Thermospheric mass density model error variance as a function of time scale

NASA Astrophysics Data System (ADS)

Emmert, J. T.; Sutton, E. K.

2017-12-01

In the increasingly crowded low-Earth orbit environment, accurate estimation of orbit prediction uncertainties is essential for collision avoidance. Poor characterization of such uncertainty can result in unnecessary and costly avoidance maneuvers (false positives) or disregard of a collision risk (false negatives). Atmospheric drag is a major source of orbit prediction uncertainty, and is particularly challenging to account for because it exerts a cumulative influence on orbital trajectories and is therefore not amenable to representation by a single uncertainty parameter. To address this challenge, we examine the variance of measured accelerometer-derived and orbit-derived mass densities with respect to predictions by thermospheric empirical models, using the data-minus-model variance as a proxy for model uncertainty. Our analysis focuses mainly on the power spectrum of the residuals, and we construct an empirical model of the variance as a function of time scale (from 1 hour to 10 years), altitude, and solar activity. We find that the power spectral density approximately follows a power-law process but with an enhancement near the 27-day solar rotation period. The residual variance increases monotonically with altitude between 250 and 550 km. There are two components to the variance dependence on solar activity: one component is 180 degrees out of phase (largest variance at solar minimum), and the other component lags 2 years behind solar maximum (largest variance in the descending phase of the solar cycle).

The effects of drainage basin geomorphometry on minimum low flow discharge: the study of small watershed in Kelang River Valley in Peninsular Malaysia.

PubMed

Yunus, Ahmad Jailani Muhamed; Nakagoshi, Nobukazu; Salleh, Khairulmaini Osman

2003-03-01

This study investigate the relationships between geomorphometric properties and the minimum low flow discharge of undisturbed drainage basins in the Taman Bukit Cahaya Seri Alam Forest Reserve, Peninsular Malaysia. The drainage basins selected were third-order basins so as to facilitate a common base for sampling and performing an unbiased statistical analyses. Three levels of relationships were observed in the study. Significant relationships existed between the geomorphometric properties as shown by the correlation network analysis; secondly, individual geomorphometric properties were observed to influence minimum flow discharge; and finally, the multiple regression model set up showed that minimum flow discharge (Q min) was dependent of basin area (AU), stream length (LS), maximum relief (Hmax), average relief (HAV) and stream frequency (SF). These findings further enforced other studies of this nature that drainage basins were dynamic and functional entities whose operations were governed by complex interrelationships occurring within the basins. Changes to any of the geomorphometric properties would influence their role as basin regulators thus influencing a change in basin response. In the case of the basin's minimum low flow, a change in any of the properties considered in the regression model influenced the "time to peak" of flow. A shorter time period would mean higher discharge, which is generally considered the prerequisite to flooding. This research also conclude that the role of geomorphometric properties to control the water supply within the stream through out the year even though during the drought and less precipitations months. Drainage basins are sensitive entities and any deteriorations involve will generate reciprocals and response to the water supply as well as the habitat within the areas.

25 CFR 543.18 - What are the minimum internal control standards for the cage, vault, kiosk, cash and cash...

Code of Federal Regulations, 2014 CFR

2014-04-01

... available upon demand for each day, shift, and drop cycle (this is not required if the system does not track..., beverage containers, etc., into and out of the cage. (j) Variances. The operation must establish, as...

25 CFR 543.18 - What are the minimum internal control standards for the cage, vault, kiosk, cash and cash...

Code of Federal Regulations, 2013 CFR

2013-04-01

... available upon demand for each day, shift, and drop cycle (this is not required if the system does not track..., beverage containers, etc., into and out of the cage. (j) Variances. The operation must establish, as...

Mutually unbiased projectors and duality between lines and bases in finite quantum systems

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

Shalaby, M.; Vourdas, A., E-mail: a.vourdas@bradford.ac.uk

2013-10-15

Quantum systems with variables in the ring Z(d) are considered, and the concepts of weak mutually unbiased bases and mutually unbiased projectors are discussed. The lines through the origin in the Z(d)×Z(d) phase space, are classified into maximal lines (sets of d points), and sublines (sets of d{sub i} points where d{sub i}|d). The sublines are intersections of maximal lines. It is shown that there exists a duality between the properties of lines (resp., sublines), and the properties of weak mutually unbiased bases (resp., mutually unbiased projectors). -- Highlights: •Lines in discrete phase space. •Bases in finite quantum systems. •Dualitymore » between bases and lines. •Weak mutually unbiased bases.« less

Unbiased methods for removing systematics from galaxy clustering measurements

NASA Astrophysics Data System (ADS)

Elsner, Franz; Leistedt, Boris; Peiris, Hiranya V.

2016-02-01

Measuring the angular clustering of galaxies as a function of redshift is a powerful method for extracting information from the three-dimensional galaxy distribution. The precision of such measurements will dramatically increase with ongoing and future wide-field galaxy surveys. However, these are also increasingly sensitive to observational and astrophysical contaminants. Here, we study the statistical properties of three methods proposed for controlling such systematics - template subtraction, basic mode projection, and extended mode projection - all of which make use of externally supplied template maps, designed to characterize and capture the spatial variations of potential systematic effects. Based on a detailed mathematical analysis, and in agreement with simulations, we find that the template subtraction method in its original formulation returns biased estimates of the galaxy angular clustering. We derive closed-form expressions that should be used to correct results for this shortcoming. Turning to the basic mode projection algorithm, we prove it to be free of any bias, whereas we conclude that results computed with extended mode projection are biased. Within a simplified setup, we derive analytical expressions for the bias and discuss the options for correcting it in more realistic configurations. Common to all three methods is an increased estimator variance induced by the cleaning process, albeit at different levels. These results enable unbiased high-precision clustering measurements in the presence of spatially varying systematics, an essential step towards realizing the full potential of current and planned galaxy surveys.

Towards a sampling strategy for the assessment of forest condition at European level: combining country estimates.

PubMed

Travaglini, Davide; Fattorini, Lorenzo; Barbati, Anna; Bottalico, Francesca; Corona, Piermaria; Ferretti, Marco; Chirici, Gherardo

2013-04-01

A correct characterization of the status and trend of forest condition is essential to support reporting processes at national and international level. An international forest condition monitoring has been implemented in Europe since 1987 under the auspices of the International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests). The monitoring is based on harmonized methodologies, with individual countries being responsible for its implementation. Due to inconsistencies and problems in sampling design, however, the ICP Forests network is not able to produce reliable quantitative estimates of forest condition at European and sometimes at country level. This paper proposes (1) a set of requirements for status and change assessment and (2) a harmonized sampling strategy able to provide unbiased and consistent estimators of forest condition parameters and of their changes at both country and European level. Under the assumption that a common definition of forest holds among European countries, monitoring objectives, parameters of concern and accuracy indexes are stated. On the basis of fixed-area plot sampling performed independently in each country, an unbiased and consistent estimator of forest defoliation indexes is obtained at both country and European level, together with conservative estimators of their sampling variance and power in the detection of changes. The strategy adopts a probabilistic sampling scheme based on fixed-area plots selected by means of systematic or stratified schemes. Operative guidelines for its application are provided.

Statistical issues in quality control of proteomic analyses: good experimental design and planning.

PubMed

Cairns, David A

2011-03-01

Quality control is becoming increasingly important in proteomic investigations as experiments become more multivariate and quantitative. Quality control applies to all stages of an investigation and statistics can play a key role. In this review, the role of statistical ideas in the design and planning of an investigation is described. This involves the design of unbiased experiments using key concepts from statistical experimental design, the understanding of the biological and analytical variation in a system using variance components analysis and the determination of a required sample size to perform a statistically powerful investigation. These concepts are described through simple examples and an example data set from a 2-D DIGE pilot experiment. Each of these concepts can prove useful in producing better and more reproducible data. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Unbalanced and Minimal Point Equivalent Estimation Second-Order Split-Plot Designs

NASA Technical Reports Server (NTRS)

Parker, Peter A.; Kowalski, Scott M.; Vining, G. Geoffrey

2007-01-01

Restricting the randomization of hard-to-change factors in industrial experiments is often performed by employing a split-plot design structure. From an economic perspective, these designs minimize the experimental cost by reducing the number of resets of the hard-to- change factors. In this paper, unbalanced designs are considered for cases where the subplots are relatively expensive and the experimental apparatus accommodates an unequal number of runs per whole-plot. We provide construction methods for unbalanced second-order split- plot designs that possess the equivalence estimation optimality property, providing best linear unbiased estimates of the parameters; independent of the variance components. Unbalanced versions of the central composite and Box-Behnken designs are developed. For cases where the subplot cost approaches the whole-plot cost, minimal point designs are proposed and illustrated with a split-plot Notz design.

Analysis of genetic effects of nuclear-cytoplasmic interaction on quantitative traits: genetic model for diploid plants.

PubMed

Han, Lide; Yang, Jian; Zhu, Jun

2007-06-01

A genetic model was proposed for simultaneously analyzing genetic effects of nuclear, cytoplasm, and nuclear-cytoplasmic interaction (NCI) as well as their genotype by environment (GE) interaction for quantitative traits of diploid plants. In the model, the NCI effects were further partitioned into additive and dominance nuclear-cytoplasmic interaction components. Mixed linear model approaches were used for statistical analysis. On the basis of diallel cross designs, Monte Carlo simulations showed that the genetic model was robust for estimating variance components under several situations without specific effects. Random genetic effects were predicted by an adjusted unbiased prediction (AUP) method. Data on four quantitative traits (boll number, lint percentage, fiber length, and micronaire) in Upland cotton (Gossypium hirsutum L.) were analyzed as a worked example to show the effectiveness of the model.

Biased Brownian dynamics for rate constant calculation.

PubMed

Zou, G; Skeel, R D; Subramaniam, S

2000-08-01

An enhanced sampling method-biased Brownian dynamics-is developed for the calculation of diffusion-limited biomolecular association reaction rates with high energy or entropy barriers. Biased Brownian dynamics introduces a biasing force in addition to the electrostatic force between the reactants, and it associates a probability weight with each trajectory. A simulation loses weight when movement is along the biasing force and gains weight when movement is against the biasing force. The sampling of trajectories is then biased, but the sampling is unbiased when the trajectory outcomes are multiplied by their weights. With a suitable choice of the biasing force, more reacted trajectories are sampled. As a consequence, the variance of the estimate is reduced. In our test case, biased Brownian dynamics gives a sevenfold improvement in central processing unit (CPU) time with the choice of a simple centripetal biasing force.

Automated systematic random sampling and Cavalieri stereology of histologic sections demonstrating acute tubular necrosis after cardiac arrest and cardiopulmonary resuscitation in the mouse.

PubMed

Wakasaki, Rumie; Eiwaz, Mahaba; McClellan, Nicholas; Matsushita, Katsuyuki; Golgotiu, Kirsti; Hutchens, Michael P

2018-06-14

A technical challenge in translational models of kidney injury is determination of the extent of cell death. Histologic sections are commonly analyzed by area morphometry or unbiased stereology, but stereology requires specialized equipment. Therefore, a challenge to rigorous quantification would be addressed by an unbiased stereology tool with reduced equipment dependence. We hypothesized that it would be feasible to build a novel software component which would facilitate unbiased stereologic quantification on scanned slides, and that unbiased stereology would demonstrate greater precision and decreased bias compared with 2D morphometry. We developed a macro for the widely used image analysis program, Image J, and performed cardiac arrest with cardiopulmonary resuscitation (CA/CPR, a model of acute cardiorenal syndrome) in mice. Fluorojade-B stained kidney sections were analyzed using three methods to quantify cell death: gold standard stereology using a controlled stage and commercially-available software, unbiased stereology using the novel ImageJ macro, and quantitative 2D morphometry also using the novel macro. There was strong agreement between both methods of unbiased stereology (bias -0.004±0.006 with 95% limits of agreement -0.015 to 0.007). 2D morphometry demonstrated poor agreement and significant bias compared to either method of unbiased stereology. Unbiased stereology is facilitated by a novel macro for ImageJ and results agree with those obtained using gold-standard methods. Automated 2D morphometry overestimated tubular epithelial cell death and correlated modestly with values obtained from unbiased stereology. These results support widespread use of unbiased stereology for analysis of histologic outcomes of injury models.

Patterns and Prevalence of Core Profile Types in the WPPSI Standardization Sample.

ERIC Educational Resources Information Center

Glutting, Joseph J.; McDermott, Paul A.

1990-01-01

Found most representative subtest profiles for 1,200 children comprising standardization sample of Wechsler Preschool and Primary Scale of Intelligence (WPPSI). Grouped scaled scores from WPPSI subtests according to similar level and shape using sequential minimum-variance cluster analysis with independent replications. Obtained final solution of…

A Review on Sensor, Signal, and Information Processing Algorithms (PREPRINT)

DTIC Science & Technology

2010-01-01

processing [214], ambi- guity surface averaging [215], optimum uncertain field tracking, and optimal minimum variance track - before - detect [216]. In [217, 218...2) (2001) 739–746. [216] S. L. Tantum, L. W. Nolte, J. L. Krolik, K. Harmanci, The performance of matched-field track - before - detect methods using

A Comparison of Item Selection Techniques for Testlets

ERIC Educational Resources Information Center

Murphy, Daniel L.; Dodd, Barbara G.; Vaughn, Brandon K.

2010-01-01

This study examined the performance of the maximum Fisher's information, the maximum posterior weighted information, and the minimum expected posterior variance methods for selecting items in a computerized adaptive testing system when the items were grouped in testlets. A simulation study compared the efficiency of ability estimation among the…

Entropic uncertainty relations and locking: Tight bounds for mutually unbiased bases

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

Ballester, Manuel A.; Wehner, Stephanie

We prove tight entropic uncertainty relations for a large number of mutually unbiased measurements. In particular, we show that a bound derived from the result by Maassen and Uffink [Phys. Rev. Lett. 60, 1103 (1988)] for two such measurements can in fact be tight for up to {radical}(d) measurements in mutually unbiased bases. We then show that using more mutually unbiased bases does not always lead to a better locking effect. We prove that the optimal bound for the accessible information using up to {radical}(d) specific mutually unbiased bases is log d/2, which is the same as can be achievedmore » by using only two bases. Our result indicates that merely using mutually unbiased bases is not sufficient to achieve a strong locking effect and we need to look for additional properties.« less

Low genetic variance in the duration of the incubation period in a collared flycatcher (Ficedula albicollis) population.

PubMed

Husby, Arild; Gustafsson, Lars; Qvarnström, Anna

2012-01-01

The avian incubation period is associated with high energetic costs and mortality risks suggesting that there should be strong selection to reduce the duration to the minimum required for normal offspring development. Although there is much variation in the duration of the incubation period across species, there is also variation within species. It is necessary to estimate to what extent this variation is genetically determined if we want to predict the evolutionary potential of this trait. Here we use a long-term study of collared flycatchers to examine the genetic basis of variation in incubation duration. We demonstrate limited genetic variance as reflected in the low and nonsignificant additive genetic variance, with a corresponding heritability of 0.04 and coefficient of additive genetic variance of 2.16. Any selection acting on incubation duration will therefore be inefficient. To our knowledge, this is the first time heritability of incubation duration has been estimated in a natural bird population. © 2011 by The University of Chicago.

Wavelet-based multiscale analysis of minimum toe clearance variability in the young and elderly during walking.

PubMed

Khandoker, Ahsan H; Karmakar, Chandan K; Begg, Rezaul K; Palaniswami, Marimuthu

2007-01-01

As humans age or are influenced by pathology of the neuromuscular system, gait patterns are known to adjust, accommodating for reduced function in the balance control system. The aim of this study was to investigate the effectiveness of a wavelet based multiscale analysis of a gait variable [minimum toe clearance (MTC)] in deriving indexes for understanding age-related declines in gait performance and screening of balance impairments in the elderly. MTC during walking on a treadmill for 30 healthy young, 27 healthy elderly and 10 falls risk elderly subjects with a history of tripping falls were analyzed. The MTC signal from each subject was decomposed to eight detailed signals at different wavelet scales by using the discrete wavelet transform. The variances of detailed signals at scales 8 to 1 were calculated. The multiscale exponent (beta) was then estimated from the slope of the variance progression at successive scales. The variance at scale 5 was significantly (p<0.01) different between young and healthy elderly group. Results also suggest that the Beta between scales 1 to 2 are effective for recognizing falls risk gait patterns. Results have implication for quantifying gait dynamics in normal, ageing and pathological conditions. Early detection of gait pattern changes due to ageing and balance impairments using wavelet-based multiscale analysis might provide the opportunity to initiate preemptive measures to be undertaken to avoid injurious falls.

Determining size and dispersion of minimum viable populations for land management planning and species conservation

NASA Astrophysics Data System (ADS)

Lehmkuhl, John F.

1984-03-01

The concept of minimum populations of wildlife and plants has only recently been discussed in the literature. Population genetics has emerged as a basic underlying criterion for determining minimum population size. This paper presents a genetic framework and procedure for determining minimum viable population size and dispersion strategies in the context of multiple-use land management planning. A procedure is presented for determining minimum population size based on maintenance of genetic heterozygosity and reduction of inbreeding. A minimum effective population size ( N e ) of 50 breeding animals is taken from the literature as the minimum shortterm size to keep inbreeding below 1% per generation. Steps in the procedure adjust N e to account for variance in progeny number, unequal sex ratios, overlapping generations, population fluctuations, and period of habitat/population constraint. The result is an approximate census number that falls within a range of effective population size of 50 500 individuals. This population range defines the time range of short- to long-term population fitness and evolutionary potential. The length of the term is a relative function of the species generation time. Two population dispersion strategies are proposed: core population and dispersed population.

[Genetic diversity of wild Cynodon dactylon germplasm detected by SRAP markers].

PubMed

Yi, Yang-Jie; Zhang, Xin-Quan; Huang, Lin-Kai; Ling, Yao; Ma, Xiao; Liu, Wei

2008-01-01

Sequence-related amplified polymorphism (SRAP) molecular markers were used to detect the genetic diversity of 32 wild accessions of Cynodon dactylon collected from Sichuan, Chongqing, Guizhou and Tibet, China. The following results were obtained. (1) Fourteen primer pairs produced 132 polymorphic bands, averaged 9.4 bands per primer pair. The percentage of polymorphic bands in average was 79.8%. The Nei's genetic similarity coefficient of the tested accessions ranged from 0.591 to 0.957, and the average Nei's coefficient was 0.759. These results suggested that there was rich genetic diversity among the wild resources of Cynodon dactylon tested. (2) Thirty two wild accessions were clustered into four groups. Moreover, the accessions from the same origin frequently clustered into one group. The findings implied that a correlation among the wild resources, geographical and ecological environment. (3) Genetic differentiation between and within six eco-geographical groups of C. dactylon was estimated by Shannon's diversity index, which showed that 65.56% genetic variance existed within group, and 34.44% genetic variance was among groups. (4) Based on Nei's unbiased measures of genetic identity, UPGMA cluster analysis measures of six eco-geographical groups of Cynodon dactylon, indicated that there was a correlation between genetic differentiation and eco-geographical habits among the groups.

Precision and bias of selected analytes reported by the National Atmospheric Deposition Program and National Trends Network, 1983; and January 1980 through September 1984

USGS Publications Warehouse

Schroder, L.J.; Bricker, A.W.; Willoughby, T.C.

1985-01-01

Blind-audit samples with known analyte concentrations have been prepared by the U.S. Geological Survey and distributed to the National Atmospheric Deposition Program 's Central Analytical Laboratory. The difference between the National Atmospheric Deposition Program and National Trends Network reported analyte concentrations and known analyte concentrations have been calculated, and the bias has been determined. Calcium, magnesium , sodium, and chloride were biased at the 99-percent confidence limit; potassium and sulfate were unbiased at the 99-percent confidence limit, for 1983 results. Relative-percent differences between the measured and known analyte concentration for calcium , magnesium, sodium, potassium, chloride, and sulfate have been calculated for 1983. The median relative percent difference for calcium was 17.0; magnesium was 6.4; sodium was 10.8; potassium was 6.4; chloride was 17.2; and sulfate was -5.3. These relative percent differences should be used to correct the 1983 data before user-analysis of the data. Variances have been calculated for calcium, magnesium, sodium, potassium, chloride, and sulfate determinations. These variances should be applicable to natural-sample analyte concentrations reported by the National Atmospheric Deposition Program and National Trends Network for calendar year 1983. (USGS)

Uncertainty in Population Estimates for Endangered Animals and Improving the Recovery Process

PubMed Central

Haines, Aaron M.; Zak, Matthew; Hammond, Katie; Scott, J. Michael; Goble, Dale D.; Rachlow, Janet L.

2013-01-01

Simple Summary The objective of our study was to evaluate the mention of uncertainty (i.e., variance) associated with population size estimates within U.S. recovery plans for endangered animals. To do this we reviewed all finalized recovery plans for listed terrestrial vertebrate species. We found that more recent recovery plans reported more estimates of population size and uncertainty. Also, bird and mammal recovery plans reported more estimates of population size and uncertainty. We recommend that updated recovery plans combine uncertainty of population size estimates with a minimum detectable difference to aid in successful recovery. Abstract United States recovery plans contain biological information for a species listed under the Endangered Species Act and specify recovery criteria to provide basis for species recovery. The objective of our study was to evaluate whether recovery plans provide uncertainty (e.g., variance) with estimates of population size. We reviewed all finalized recovery plans for listed terrestrial vertebrate species to record the following data: (1) if a current population size was given, (2) if a measure of uncertainty or variance was associated with current estimates of population size and (3) if population size was stipulated for recovery. We found that 59% of completed recovery plans specified a current population size, 14.5% specified a variance for the current population size estimate and 43% specified population size as a recovery criterion. More recent recovery plans reported more estimates of current population size, uncertainty and population size as a recovery criterion. Also, bird and mammal recovery plans reported more estimates of population size and uncertainty compared to reptiles and amphibians. We suggest the use of calculating minimum detectable differences to improve confidence when delisting endangered animals and we identified incentives for individuals to get involved in recovery planning to improve access to quantitative data. PMID:26479531

Sampling intraspecific variability in leaf functional traits: Practical suggestions to maximize collected information.

PubMed

Petruzzellis, Francesco; Palandrani, Chiara; Savi, Tadeja; Alberti, Roberto; Nardini, Andrea; Bacaro, Giovanni

2017-12-01

The choice of the best sampling strategy to capture mean values of functional traits for a species/population, while maintaining information about traits' variability and minimizing the sampling size and effort, is an open issue in functional trait ecology. Intraspecific variability (ITV) of functional traits strongly influences sampling size and effort. However, while adequate information is available about intraspecific variability between individuals (ITV BI ) and among populations (ITV POP ), relatively few studies have analyzed intraspecific variability within individuals (ITV WI ). Here, we provide an analysis of ITV WI of two foliar traits, namely specific leaf area (SLA) and osmotic potential (π), in a population of Quercus ilex L. We assessed the baseline ITV WI level of variation between the two traits and provided the minimum and optimal sampling size in order to take into account ITV WI , comparing sampling optimization outputs with those previously proposed in the literature. Different factors accounted for different amount of variance of the two traits. SLA variance was mostly spread within individuals (43.4% of the total variance), while π variance was mainly spread between individuals (43.2%). Strategies that did not account for all the canopy strata produced mean values not representative of the sampled population. The minimum size to adequately capture the studied functional traits corresponded to 5 leaves taken randomly from 5 individuals, while the most accurate and feasible sampling size was 4 leaves taken randomly from 10 individuals. We demonstrate that the spatial structure of the canopy could significantly affect traits variability. Moreover, different strategies for different traits could be implemented during sampling surveys. We partially confirm sampling sizes previously proposed in the recent literature and encourage future analysis involving different traits.

A de-noising method using the improved wavelet threshold function based on noise variance estimation

NASA Astrophysics Data System (ADS)

Liu, Hui; Wang, Weida; Xiang, Changle; Han, Lijin; Nie, Haizhao

2018-01-01

The precise and efficient noise variance estimation is very important for the processing of all kinds of signals while using the wavelet transform to analyze signals and extract signal features. In view of the problem that the accuracy of traditional noise variance estimation is greatly affected by the fluctuation of noise values, this study puts forward the strategy of using the two-state Gaussian mixture model to classify the high-frequency wavelet coefficients in the minimum scale, which takes both the efficiency and accuracy into account. According to the noise variance estimation, a novel improved wavelet threshold function is proposed by combining the advantages of hard and soft threshold functions, and on the basis of the noise variance estimation algorithm and the improved wavelet threshold function, the research puts forth a novel wavelet threshold de-noising method. The method is tested and validated using random signals and bench test data of an electro-mechanical transmission system. The test results indicate that the wavelet threshold de-noising method based on the noise variance estimation shows preferable performance in processing the testing signals of the electro-mechanical transmission system: it can effectively eliminate the interference of transient signals including voltage, current, and oil pressure and maintain the dynamic characteristics of the signals favorably.

Dimensionality and noise in energy selective x-ray imaging

PubMed Central

Alvarez, Robert E.

2013-01-01

Purpose: To develop and test a method to quantify the effect of dimensionality on the noise in energy selective x-ray imaging. Methods: The Cramèr-Rao lower bound (CRLB), a universal lower limit of the covariance of any unbiased estimator, is used to quantify the noise. It is shown that increasing dimensionality always increases, or at best leaves the same, the variance. An analytic formula for the increase in variance in an energy selective x-ray system is derived. The formula is used to gain insight into the dependence of the increase in variance on the properties of the additional basis functions, the measurement noise covariance, and the source spectrum. The formula is also used with computer simulations to quantify the dependence of the additional variance on these factors. Simulated images of an object with three materials are used to demonstrate the trade-off of increased information with dimensionality and noise. The images are computed from energy selective data with a maximum likelihood estimator. Results: The increase in variance depends most importantly on the dimension and on the properties of the additional basis functions. With the attenuation coefficients of cortical bone, soft tissue, and adipose tissue as the basis functions, the increase in variance of the bone component from two to three dimensions is 1.4 × 103. With the soft tissue component, it is 2.7 × 104. If the attenuation coefficient of a high atomic number contrast agent is used as the third basis function, there is only a slight increase in the variance from two to three basis functions, 1.03 and 7.4 for the bone and soft tissue components, respectively. The changes in spectrum shape with beam hardening also have a substantial effect. They increase the variance by a factor of approximately 200 for the bone component and 220 for the soft tissue component as the soft tissue object thickness increases from 1 to 30 cm. Decreasing the energy resolution of the detectors increases the variance of the bone component markedly with three dimension processing, approximately a factor of 25 as the resolution decreases from 100 to 3 bins. The increase with two dimension processing for adipose tissue is a factor of two and with the contrast agent as the third material for two or three dimensions is also a factor of two for both components. The simulated images show that a maximum likelihood estimator can be used to process energy selective x-ray data to produce images with noise close to the CRLB. Conclusions: The method presented can be used to compute the effects of the object attenuation coefficients and the x-ray system properties on the relationship of dimensionality and noise in energy selective x-ray imaging systems. PMID:24320442

Gap-filling methods to impute eddy covariance flux data by preserving variance.

NASA Astrophysics Data System (ADS)

Kunwor, S.; Staudhammer, C. L.; Starr, G.; Loescher, H. W.

2015-12-01

To represent carbon dynamics, in terms of exchange of CO2 between the terrestrial ecosystem and the atmosphere, eddy covariance (EC) data has been collected using eddy flux towers from various sites across globe for more than two decades. However, measurements from EC data are missing for various reasons: precipitation, routine maintenance, or lack of vertical turbulence. In order to have estimates of net ecosystem exchange of carbon dioxide (NEE) with high precision and accuracy, robust gap-filling methods to impute missing data are required. While the methods used so far have provided robust estimates of the mean value of NEE, little attention has been paid to preserving the variance structures embodied by the flux data. Preserving the variance of these data will provide unbiased and precise estimates of NEE over time, which mimic natural fluctuations. We used a non-linear regression approach with moving windows of different lengths (15, 30, and 60-days) to estimate non-linear regression parameters for one year of flux data from a long-leaf pine site at the Joseph Jones Ecological Research Center. We used as our base the Michaelis-Menten and Van't Hoff functions. We assessed the potential physiological drivers of these parameters with linear models using micrometeorological predictors. We then used a parameter prediction approach to refine the non-linear gap-filling equations based on micrometeorological conditions. This provides us an opportunity to incorporate additional variables, such as vapor pressure deficit (VPD) and volumetric water content (VWC) into the equations. Our preliminary results indicate that improvements in gap-filling can be gained with a 30-day moving window with additional micrometeorological predictors (as indicated by lower root mean square error (RMSE) of the predicted values of NEE). Our next steps are to use these parameter predictions from moving windows to gap-fill the data with and without incorporation of potential driver variables of the parameters traditionally used. Then, comparisons of the predicted values from these methods and 'traditional' gap-filling methods (using 12 fixed monthly windows) will be assessed to show the scale of preserving variance. Further, this method will be applied to impute artificially created gaps for analyzing if variance is preserved.

Efficient design of cluster randomized trials with treatment-dependent costs and treatment-dependent unknown variances.

PubMed

van Breukelen, Gerard J P; Candel, Math J J M

2018-06-10

Cluster randomized trials evaluate the effect of a treatment on persons nested within clusters, where treatment is randomly assigned to clusters. Current equations for the optimal sample size at the cluster and person level assume that the outcome variances and/or the study costs are known and homogeneous between treatment arms. This paper presents efficient yet robust designs for cluster randomized trials with treatment-dependent costs and treatment-dependent unknown variances, and compares these with 2 practical designs. First, the maximin design (MMD) is derived, which maximizes the minimum efficiency (minimizes the maximum sampling variance) of the treatment effect estimator over a range of treatment-to-control variance ratios. The MMD is then compared with the optimal design for homogeneous variances and costs (balanced design), and with that for homogeneous variances and treatment-dependent costs (cost-considered design). The results show that the balanced design is the MMD if the treatment-to control cost ratio is the same at both design levels (cluster, person) and within the range for the treatment-to-control variance ratio. It still is highly efficient and better than the cost-considered design if the cost ratio is within the range for the squared variance ratio. Outside that range, the cost-considered design is better and highly efficient, but it is not the MMD. An example shows sample size calculation for the MMD, and the computer code (SPSS and R) is provided as supplementary material. The MMD is recommended for trial planning if the study costs are treatment-dependent and homogeneity of variances cannot be assumed. © 2018 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

Numerically stable algorithm for combining census and sample estimates with the multivariate composite estimator

Treesearch

R. L. Czaplewski

2009-01-01

The minimum variance multivariate composite estimator is a relatively simple sequential estimator for complex sampling designs (Czaplewski 2009). Such designs combine a probability sample of expensive field data with multiple censuses and/or samples of relatively inexpensive multi-sensor, multi-resolution remotely sensed data. Unfortunately, the multivariate composite...

Multiple Signal Classification for Determining Direction of Arrival of Frequency Hopping Spread Spectrum Signals

DTIC Science & Technology

2014-03-27

42 4.2.3 Number of Hops Hs . . . . . . . . . . . . . . . . . . . . . . . . . 45 4.2.4 Number of Sensors M... 45 4.5 Standard deviation vs. Ns. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 4.6 Bias...laboratory MTM multiple taper method MUSIC multiple signal classification MVDR minimum variance distortionless reposnse PSK phase shift keying QAM

Multi-Sensor Optimal Data Fusion Based on the Adaptive Fading Unscented Kalman Filter

PubMed Central

Gao, Bingbing; Hu, Gaoge; Gao, Shesheng; Gu, Chengfan

2018-01-01

This paper presents a new optimal data fusion methodology based on the adaptive fading unscented Kalman filter for multi-sensor nonlinear stochastic systems. This methodology has a two-level fusion structure: at the bottom level, an adaptive fading unscented Kalman filter based on the Mahalanobis distance is developed and serves as local filters to improve the adaptability and robustness of local state estimations against process-modeling error; at the top level, an unscented transformation-based multi-sensor optimal data fusion for the case of N local filters is established according to the principle of linear minimum variance to calculate globally optimal state estimation by fusion of local estimations. The proposed methodology effectively refrains from the influence of process-modeling error on the fusion solution, leading to improved adaptability and robustness of data fusion for multi-sensor nonlinear stochastic systems. It also achieves globally optimal fusion results based on the principle of linear minimum variance. Simulation and experimental results demonstrate the efficacy of the proposed methodology for INS/GNSS/CNS (inertial navigation system/global navigation satellite system/celestial navigation system) integrated navigation. PMID:29415509

Fast computation of an optimal controller for large-scale adaptive optics.

PubMed

Massioni, Paolo; Kulcsár, Caroline; Raynaud, Henri-François; Conan, Jean-Marc

2011-11-01

The linear quadratic Gaussian regulator provides the minimum-variance control solution for a linear time-invariant system. For adaptive optics (AO) applications, under the hypothesis of a deformable mirror with instantaneous response, such a controller boils down to a minimum-variance phase estimator (a Kalman filter) and a projection onto the mirror space. The Kalman filter gain can be computed by solving an algebraic Riccati matrix equation, whose computational complexity grows very quickly with the size of the telescope aperture. This "curse of dimensionality" makes the standard solvers for Riccati equations very slow in the case of extremely large telescopes. In this article, we propose a way of computing the Kalman gain for AO systems by means of an approximation that considers the turbulence phase screen as the cropped version of an infinite-size screen. We demonstrate the advantages of the methods for both off- and on-line computational time, and we evaluate its performance for classical AO as well as for wide-field tomographic AO with multiple natural guide stars. Simulation results are reported.

Multi-Sensor Optimal Data Fusion Based on the Adaptive Fading Unscented Kalman Filter.

PubMed

Gao, Bingbing; Hu, Gaoge; Gao, Shesheng; Zhong, Yongmin; Gu, Chengfan

2018-02-06

This paper presents a new optimal data fusion methodology based on the adaptive fading unscented Kalman filter for multi-sensor nonlinear stochastic systems. This methodology has a two-level fusion structure: at the bottom level, an adaptive fading unscented Kalman filter based on the Mahalanobis distance is developed and serves as local filters to improve the adaptability and robustness of local state estimations against process-modeling error; at the top level, an unscented transformation-based multi-sensor optimal data fusion for the case of N local filters is established according to the principle of linear minimum variance to calculate globally optimal state estimation by fusion of local estimations. The proposed methodology effectively refrains from the influence of process-modeling error on the fusion solution, leading to improved adaptability and robustness of data fusion for multi-sensor nonlinear stochastic systems. It also achieves globally optimal fusion results based on the principle of linear minimum variance. Simulation and experimental results demonstrate the efficacy of the proposed methodology for INS/GNSS/CNS (inertial navigation system/global navigation satellite system/celestial navigation system) integrated navigation.

Characterizing heterogeneity of disease incidence in a spatial hierarchy: a case study from a decade of observations of fusarium head blight of wheat.

PubMed

Kriss, A B; Paul, P A; Madden, L V

2012-09-01

A multilevel analysis of heterogeneity of disease incidence was conducted based on observations of Fusarium head blight (caused by Fusarium graminearum) in Ohio during the 2002-11 growing seasons. Sampling consisted of counting the number of diseased and healthy wheat spikes per 0.3 m of row at 10 sites (about 30 m apart) in a total of 67 to 159 sampled fields in 12 to 32 sampled counties per year. Incidence was then determined as the proportion of diseased spikes at each site. Spatial heterogeneity of incidence among counties, fields within counties, and sites within fields and counties was characterized by fitting a generalized linear mixed model to the data, using a complementary log-log link function, with the assumption that the disease status of spikes was binomially distributed conditional on the effects of county, field, and site. Based on the estimated variance terms, there was highly significant spatial heterogeneity among counties and among fields within counties each year; magnitude of the estimated variances was similar for counties and fields. The lowest level of heterogeneity was among sites within fields, and the site variance was either 0 or not significantly greater than 0 in 3 of the 10 years. Based on the variances, the intracluster correlation of disease status of spikes within sites indicated that spikes from the same site were somewhat more likely to share the same disease status relative to spikes from other sites, fields, or counties. The estimated best linear unbiased predictor (EBLUP) for each county was determined, showing large differences across the state in disease incidence (as represented by the link function of the estimated probability that a spike was diseased) but no consistency between years for the different counties. The effects of geographical location, corn and wheat acreage per county, and environmental conditions on the EBLUP for each county were not significant in the majority of years.

Modeling heterogeneous (co)variances from adjacent-SNP groups improves genomic prediction for milk protein composition traits.

PubMed

Gebreyesus, Grum; Lund, Mogens S; Buitenhuis, Bart; Bovenhuis, Henk; Poulsen, Nina A; Janss, Luc G

2017-12-05

Accurate genomic prediction requires a large reference population, which is problematic for traits that are expensive to measure. Traits related to milk protein composition are not routinely recorded due to costly procedures and are considered to be controlled by a few quantitative trait loci of large effect. The amount of variation explained may vary between regions leading to heterogeneous (co)variance patterns across the genome. Genomic prediction models that can efficiently take such heterogeneity of (co)variances into account can result in improved prediction reliability. In this study, we developed and implemented novel univariate and bivariate Bayesian prediction models, based on estimates of heterogeneous (co)variances for genome segments (BayesAS). Available data consisted of milk protein composition traits measured on cows and de-regressed proofs of total protein yield derived for bulls. Single-nucleotide polymorphisms (SNPs), from 50K SNP arrays, were grouped into non-overlapping genome segments. A segment was defined as one SNP, or a group of 50, 100, or 200 adjacent SNPs, or one chromosome, or the whole genome. Traditional univariate and bivariate genomic best linear unbiased prediction (GBLUP) models were also run for comparison. Reliabilities were calculated through a resampling strategy and using deterministic formula. BayesAS models improved prediction reliability for most of the traits compared to GBLUP models and this gain depended on segment size and genetic architecture of the traits. The gain in prediction reliability was especially marked for the protein composition traits β-CN, κ-CN and β-LG, for which prediction reliabilities were improved by 49 percentage points on average using the MT-BayesAS model with a 100-SNP segment size compared to the bivariate GBLUP. Prediction reliabilities were highest with the BayesAS model that uses a 100-SNP segment size. The bivariate versions of our BayesAS models resulted in extra gains of up to 6% in prediction reliability compared to the univariate versions. Substantial improvement in prediction reliability was possible for most of the traits related to milk protein composition using our novel BayesAS models. Grouping adjacent SNPs into segments provided enhanced information to estimate parameters and allowing the segments to have different (co)variances helped disentangle heterogeneous (co)variances across the genome.

Static vs stochastic optimization: A case study of FTSE Bursa Malaysia sectorial indices

NASA Astrophysics Data System (ADS)

Mamat, Nur Jumaadzan Zaleha; Jaaman, Saiful Hafizah; Ahmad, Rokiah@Rozita

2014-06-01

Traditional portfolio optimization methods in the likes of Markowitz' mean-variance model and semi-variance model utilize static expected return and volatility risk from historical data to generate an optimal portfolio. The optimal portfolio may not truly be optimal in reality due to the fact that maximum and minimum values from the data may largely influence the expected return and volatility risk values. This paper considers distributions of assets' return and volatility risk to determine a more realistic optimized portfolio. For illustration purposes, the sectorial indices data in FTSE Bursa Malaysia is employed. The results show that stochastic optimization provides more stable information ratio.

Designing occupancy studies: general advice and allocating survey effort

USGS Publications Warehouse

MacKenzie, D.I.; Royle, J. Andrew

2005-01-01

1. The fraction of sampling units in a landscape where a target species is present (occupancy) is an extensively used concept in ecology. Yet in many applications the species will not always be detected in a sampling unit even when present, resulting in biased estimates of occupancy. Given that sampling units are surveyed repeatedly within a relatively short timeframe, a number of similar methods have now been developed to provide unbiased occupancy estimates. However, practical guidance on the efficient design of occupancy studies has been lacking. 2. In this paper we comment on a number of general issues related to designing occupancy studies, including the need for clear objectives that are explicitly linked to science or management, selection of sampling units, timing of repeat surveys and allocation of survey effort. Advice on the number of repeat surveys per sampling unit is considered in terms of the variance of the occupancy estimator, for three possible study designs. 3. We recommend that sampling units should be surveyed a minimum of three times when detection probability is high (> 0.5 survey-1), unless a removal design is used. 4. We found that an optimal removal design will generally be the most efficient, but we suggest it may be less robust to assumption violations than a standard design. 5. Our results suggest that for a rare species it is more efficient to survey more sampling units less intensively, while for a common species fewer sampling units should be surveyed more intensively. 6. Synthesis and applications. Reliable inferences can only result from quality data. To make the best use of logistical resources, study objectives must be clearly defined; sampling units must be selected, and repeated surveys timed appropriately; and a sufficient number of repeated surveys must be conducted. Failure to do so may compromise the integrity of the study. The guidance given here on study design issues is particularly applicable to studies of species occurrence and distribution, habitat selection and modelling, metapopulation studies and monitoring programmes.

Reconstructing Images in Astrophysics, an Inverse Problem Point of View

NASA Astrophysics Data System (ADS)

Theys, Céline; Aime, Claude

2016-04-01

After a short introduction, a first section provides a brief tutorial to the physics of image formation and its detection in the presence of noises. The rest of the chapter focuses on the resolution of the inverse problem . In the general form, the observed image is given by a Fredholm integral containing the object and the response of the instrument. Its inversion is formulated using a linear algebra. The discretized object and image of size N × N are stored in vectors x and y of length N 2. They are related one another by the linear relation y = H x, where H is a matrix of size N 2 × N 2 that contains the elements of the instrument response. This matrix presents particular properties for a shift invariant point spread function for which the Fredholm integral is reduced to a convolution relation. The presence of noise complicates the resolution of the problem. It is shown that minimum variance unbiased solutions fail to give good results because H is badly conditioned, leading to the need of a regularized solution. Relative strength of regularization versus fidelity to the data is discussed and briefly illustrated on an example using L-curves. The origins and construction of iterative algorithms are explained, and illustrations are given for the algorithms ISRA , for a Gaussian additive noise, and Richardson-Lucy , for a pure photodetected image (Poisson statistics). In this latter case, the way the algorithm modifies the spatial frequencies of the reconstructed image is illustrated for a diluted array of apertures in space. Throughout the chapter, the inverse problem is formulated in matrix form for the general case of the Fredholm integral, while numerical illustrations are limited to the deconvolution case, allowing the use of discrete Fourier transforms, because of computer limitations.

Environmental characterization of a coffee processing workplace with obliterative bronchiolitis in former workers

PubMed Central

Duling, Matthew G.; LeBouf, Ryan F.; Cox-Ganser, Jean M.; Kreiss, Kathleen; Martin, Stephen B.; Bailey, Rachel L.

2018-01-01

Obliterative bronchiolitis in five former coffee processing employees at a single workplace prompted an exposure study of current workers. Exposure characterization was performed by observing processes, assessing the ventilation system and pressure relationships, analyzing headspace of flavoring samples, and collecting and analyzing personal breathing zone and area air samples for diacetyl and 2,3-pentanedione vapors and total inhalable dust by work area and job title. Mean airborne concentrations were calculated using the minimum variance unbiased estimator of the arithmetic mean. Workers in the grinding/packaging area for unflavored coffee had the highest mean diacetyl exposures, with personal concentrations averaging 93 parts per billion (ppb). This area was under positive pressure with respect to flavored coffee production (mean personal diacetyl levels of 80 ppb). The 2,3-pentanedione exposures were highest in the flavoring room with mean personal exposures of 122 ppb, followed by exposures in the unflavored coffee grinding/packaging area (53 ppb). Peak 15-min airborne concentrations of 14,300 ppb diacetyl and 13,800 ppb 2,3-pentanedione were measured at a small open hatch in the lid of a hopper containing ground unflavored coffee on the mezzanine over the grinding/packaging area. Three out of the four bulk coffee flavorings tested had at least a factor of two higher 2,3-pentanedione than diacetyl headspace measurements. At a coffee processing facility producing both unflavored and flavored coffee, we found the grinding and packaging of unflavored coffee generate simultaneous exposures to diacetyl and 2,3-pentanedione that were well in excess of the NIOSH proposed RELs and similar in magnitude to those in the areas using a flavoring substitute for diacetyl. These findings require physicians to be alert for obliterative bronchiolitis and employers, government, and public health consultants to assess the similarities and differences across the industry to motivate preventive intervention where indicated by exposures above the proposed RELs for diacetyl and 2,3-pentanedione. PMID:27105025

Bimetallic cages

NASA Astrophysics Data System (ADS)

Fournier, René; Afzal-Hussain, Sabeen

2013-02-01

We report the results of density functional theory for 39 clusters AxBy (x + y = 10 or 12) where A and B are metals from group 1, 2, 11, 12, 13, or 14 of the periodic table. The chemical compositions were chosen to satisfy an electronic shell closing criterion. We performed an unbiased search for the global minimum (GM) by taboo search in descriptor space in each case. Eight of the 39 putative GM are cages even though none of the clusters contains gold, a metal with a well known propensity to form cages. These cages are large enough to accommodate a dopant atom with an atomic radius varying between 0.7 Å and 1.2 Å. The chemical compositions most likely to produce cages have an element of group 11 alloyed with an element of group 2, 12, or 13.

SU-F-T-18: The Importance of Immobilization Devices in Brachytherapy Treatments of Vaginal Cuff

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

Shojaei, M; Dumitru, N; Pella, S

2016-06-15

Purpose: High dose rate brachytherapy is a highly localized radiation therapy that has a very high dose gradient. Thus one of the most important parts of the treatment is the immobilization. The smallest movement of the patient or applicator can result in dose variation to the surrounding tissues as well as to the tumor to be treated. We will revise the ML Cylinder treatments and their localization challenges. Methods: A retrospective study of 25 patients with 5 treatments each looking into the applicator’s placement in regard to the organs at risk. Motion possibilities for each applicator intra and inter fractionationmore » with their dosimetric implications were covered and measured in regard with their dose variance. The localization immobilization devices used were assessed for the capability to prevent motion before and during the treatment delivery. Results: We focused on the 100% isodose on central axis and a 15 degree displacement due to possible rotation analyzing the dose variations to the bladder and rectum walls. The average dose variation for bladder was 15% of the accepted tolerance, with a minimum variance of 11.1% and a maximum one of 23.14% on the central axis. For the off axis measurements we found an average variation of 16.84% of the accepted tolerance, with a minimum variance of 11.47% and a maximum one of 27.69%. For the rectum we focused on the rectum wall closest to the 120% isodose line. The average dose variation was 19.4%, minimum 11.3% and a maximum of 34.02% from the accepted tolerance values Conclusion: Improved immobilization devices are recommended. For inter-fractionation, localization devices are recommended in place with consistent planning in regards with the initial fraction. Many of the present immobilization devices produced for external radiotherapy can be used to improve the localization of HDR applicators during transportation of the patient and during treatment.« less

Differentiation of five body fluids from forensic samples by expression analysis of four microRNAs using quantitative PCR.

PubMed

Sauer, Eva; Reinke, Ann-Kathrin; Courts, Cornelius

2016-05-01

Applying molecular genetic approaches for the identification of forensically relevant body fluids, which often yield crucial information for the reconstruction of a potential crime, is a current topic of forensic research. Due to their body fluid specific expression patterns and stability against degradation, microRNAs (miRNA) emerged as a promising molecular species, with a range of candidate markers published. The analysis of miRNA via quantitative Real-Time PCR, however, should be based on a relevant strategy of normalization of non-biological variances to deliver reliable and biologically meaningful results. The herein presented work is the as yet most comprehensive study of forensic body fluid identification via miRNA expression analysis based on a thoroughly validated qPCR procedure and unbiased statistical decision making to identify single source samples. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

An information measure for class discrimination. [in remote sensing of crop observation

NASA Technical Reports Server (NTRS)

Shen, S. S.; Badhwar, G. D.

1986-01-01

This article describes a separability measure for class discrimination. This measure is based on the Fisher information measure for estimating the mixing proportion of two classes. The Fisher information measure not only provides a means to assess quantitatively the information content in the features for separating classes, but also gives the lower bound for the variance of any unbiased estimate of the mixing proportion based on observations of the features. Unlike most commonly used separability measures, this measure is not dependent on the form of the probability distribution of the features and does not imply a specific estimation procedure. This is important because the probability distribution function that describes the data for a given class does not have simple analytic forms, such as a Gaussian. Results of applying this measure to compare the information content provided by three Landsat-derived feature vectors for the purpose of separating small grains from other crops are presented.

Estimation of group means when adjusting for covariates in generalized linear models.

PubMed

Qu, Yongming; Luo, Junxiang

2015-01-01

Generalized linear models are commonly used to analyze categorical data such as binary, count, and ordinal outcomes. Adjusting for important prognostic factors or baseline covariates in generalized linear models may improve the estimation efficiency. The model-based mean for a treatment group produced by most software packages estimates the response at the mean covariate, not the mean response for this treatment group for the studied population. Although this is not an issue for linear models, the model-based group mean estimates in generalized linear models could be seriously biased for the true group means. We propose a new method to estimate the group mean consistently with the corresponding variance estimation. Simulation showed the proposed method produces an unbiased estimator for the group means and provided the correct coverage probability. The proposed method was applied to analyze hypoglycemia data from clinical trials in diabetes. Copyright © 2014 John Wiley & Sons, Ltd.

Prediction of episodic acidification in North-eastern USA: An empirical/mechanistic approach

USGS Publications Warehouse

Davies, T.D.; Tranter, M.; Wigington, P.J.; Eshleman, K.N.; Peters, N.E.; Van Sickle, J.; DeWalle, David R.; Murdoch, Peter S.

1999-01-01

Observations from the US Environmental Protection Agency's Episodic Response Project (ERP) in the North-eastern United States are used to develop an empirical/mechanistic scheme for prediction of the minimum values of acid neutralizing capacity (ANC) during episodes. An acidification episode is defined as a hydrological event during which ANC decreases. The pre-episode ANC is used to index the antecedent condition, and the stream flow increase reflects how much the relative contributions of sources of waters change during the episode. As much as 92% of the total variation in the minimum ANC in individual catchments can be explained (with levels of explanation >70% for nine of the 13 streams) by a multiple linear regression model that includes pre-episode ANC and change in discharge as independent variable. The predictive scheme is demonstrated to be regionally robust, with the regional variance explained ranging from 77 to 83%. The scheme is not successful for each ERP stream, and reasons are suggested for the individual failures. The potential for applying the predictive scheme to other watersheds is demonstrated by testing the model with data from the Panola Mountain Research Watershed in the South-eastern United States, where the variance explained by the model was 74%. The model can also be utilized to assess 'chemically new' and 'chemically old' water sources during acidification episodes.Observations from the US Environmental Protection Agency's Episodic Response Project (ERP) in the Northeastern United States are used to develop an empirical/mechanistic scheme for prediction of the minimum values of acid neutralizing capacity (ANC) during episodes. An acidification episode is defined as a hydrological event during which ANC decreases. The pre-episode ANC is used to index the antecedent condition, and the stream flow increase reflects how much the relative contributions of sources of waters change during the episode. As much as 92% of the total variation in the minimum ANC in individual catchments can be explained (with levels of explanation >70% for nine of the 13 streams) by a multiple linear regression model that includes pre-episode ANC and change in discharge as independent variables. The predictive scheme is demonstrated to be regionally robust, with the regional variance explained ranging from 77 to 83%. The scheme is not successful for each ERP stream, and reasons are suggested for the individual failures. The potential for applying the predictive scheme to other watersheds is demonstrated by testing the model with data from the Panola Mountain Research Watershed in the South-eastern United States, where the variance explained by the model was 74%. The model can also be utilized to assess `chemically new' and `chemically old' water sources during acidification episodes.

Variable variance Preisach model for multilayers with perpendicular magnetic anisotropy

NASA Astrophysics Data System (ADS)

Franco, A. F.; Gonzalez-Fuentes, C.; Morales, R.; Ross, C. A.; Dumas, R.; Åkerman, J.; Garcia, C.

2016-08-01

We present a variable variance Preisach model that fully accounts for the different magnetization processes of a multilayer structure with perpendicular magnetic anisotropy by adjusting the evolution of the interaction variance as the magnetization changes. We successfully compare in a quantitative manner the results obtained with this model to experimental hysteresis loops of several [CoFeB/Pd ] n multilayers. The effect of the number of repetitions and the thicknesses of the CoFeB and Pd layers on the magnetization reversal of the multilayer structure is studied, and it is found that many of the observed phenomena can be attributed to an increase of the magnetostatic interactions and subsequent decrease of the size of the magnetic domains. Increasing the CoFeB thickness leads to the disappearance of the perpendicular anisotropy, and such a minimum thickness of the Pd layer is necessary to achieve an out-of-plane magnetization.

Experimental study on an FBG strain sensor

NASA Astrophysics Data System (ADS)

Liu, Hong-lin; Zhu, Zheng-wei; Zheng, Yong; Liu, Bang; Xiao, Feng

2018-01-01

Landslides and other geological disasters occur frequently and often cause high financial and humanitarian cost. The real-time, early-warning monitoring of landslides has important significance in reducing casualties and property losses. In this paper, by taking the high initial precision and high sensitivity advantage of FBG, an FBG strain sensor is designed combining FBGs with inclinometer. The sensor was regarded as a cantilever beam with one end fixed. According to the anisotropic material properties of the inclinometer, a theoretical formula between the FBG wavelength and the deflection of the sensor was established using the elastic mechanics principle. Accuracy of the formula established had been verified through laboratory calibration testing and model slope monitoring experiments. The displacement of landslide could be calculated by the established theoretical formula using the changing values of FBG central wavelength obtained by the demodulation instrument remotely. Results showed that the maximum error at different heights was 9.09%; the average of the maximum error was 6.35%, and its corresponding variance was 2.12; the minimum error was 4.18%; the average of the minimum error was 5.99%, and its corresponding variance was 0.50. The maximum error of the theoretical and the measured displacement decrease gradually, and the variance of the error also decreases gradually. This indicates that the theoretical results are more and more reliable. It also shows that the sensor and the theoretical formula established in this paper can be used for remote, real-time, high precision and early warning monitoring of the slope.

A Framework of Covariance Projection on Constraint Manifold for Data Fusion.

PubMed

Bakr, Muhammad Abu; Lee, Sukhan

2018-05-17

A general framework of data fusion is presented based on projecting the probability distribution of true states and measurements around the predicted states and actual measurements onto the constraint manifold. The constraint manifold represents the constraints to be satisfied among true states and measurements, which is defined in the extended space with all the redundant sources of data such as state predictions and measurements considered as independent variables. By the general framework, we mean that it is able to fuse any correlated data sources while directly incorporating constraints and identifying inconsistent data without any prior information. The proposed method, referred to here as the Covariance Projection (CP) method, provides an unbiased and optimal solution in the sense of minimum mean square error (MMSE), if the projection is based on the minimum weighted distance on the constraint manifold. The proposed method not only offers a generalization of the conventional formula for handling constraints and data inconsistency, but also provides a new insight into data fusion in terms of a geometric-algebraic point of view. Simulation results are provided to show the effectiveness of the proposed method in handling constraints and data inconsistency.

Mutually unbiased phase states, phase uncertainties, and Gauss sums

NASA Astrophysics Data System (ADS)

Planat, M.; Rosu, H.

2005-10-01

Mutually unbiased bases (MUBs), which are such that the inner product between two vectors in different orthogonal bases is a constant equal to 1/sqrt{d}, with d the dimension of the finite Hilbert space, are becoming more and more studied for applications such as quantum tomography and cryptography, and in relation to entangled states and to the Heisenberg-Weil group of quantum optics. Complete sets of MUBs of cardinality d+1 have been derived for prime power dimensions d=pm using the tools of abstract algebra. Presumably, for non prime dimensions the cardinality is much less. Here we reinterpret MUBs as quantum phase states, i.e. as eigenvectors of Hermitian phase operators generalizing those introduced by Pegg and Barnett in 1989. We relate MUB states to additive characters of Galois fields (in odd characteristic p) and to Galois rings (in characteristic 2). Quantum Fourier transforms of the components in vectors of the bases define a more general class of MUBs with multiplicative characters and additive ones altogether. We investigate the complementary properties of the above phase operator with respect to the number operator. We also study the phase probability distribution and variance for general pure quantum electromagnetic states and find them to be related to the Gauss sums, which are sums over all elements of the field (or of the ring) of the product of multiplicative and additive characters. Finally, we relate the concepts of mutual unbiasedness and maximal entanglement. This allows to use well studied algebraic concepts as efficient tools in the study of entanglement and its information aspects.

Post-stratified estimation: with-in strata and total sample size recommendations

Treesearch

James A. Westfall; Paul L. Patterson; John W. Coulston

2011-01-01

Post-stratification is used to reduce the variance of estimates of the mean. Because the stratification is not fixed in advance, within-strata sample sizes can be quite small. The survey statistics literature provides some guidance on minimum within-strata sample sizes; however, the recommendations and justifications are inconsistent and apply broadly for many...

Interaction of cosmic ray muons with spent nuclear fuel dry casks and determination of lower detection limit

NASA Astrophysics Data System (ADS)

Chatzidakis, S.; Choi, C. K.; Tsoukalas, L. H.

2016-08-01

The potential non-proliferation monitoring of spent nuclear fuel sealed in dry casks interacting continuously with the naturally generated cosmic ray muons is investigated. Treatments on the muon RMS scattering angle by Moliere, Rossi-Greisen, Highland and, Lynch-Dahl were analyzed and compared with simplified Monte Carlo simulations. The Lynch-Dahl expression has the lowest error and appears to be appropriate when performing conceptual calculations for high-Z, thick targets such as dry casks. The GEANT4 Monte Carlo code was used to simulate dry casks with various fuel loadings and scattering variance estimates for each case were obtained. The scattering variance estimation was shown to be unbiased and using Chebyshev's inequality, it was found that 106 muons will provide estimates of the scattering variances that are within 1% of the true value at a 99% confidence level. These estimates were used as reference values to calculate scattering distributions and evaluate the asymptotic behavior for small variations on fuel loading. It is shown that the scattering distributions between a fully loaded dry cask and one with a fuel assembly missing initially overlap significantly but their distance eventually increases with increasing number of muons. One missing fuel assembly can be distinguished from a fully loaded cask with a small overlapping between the distributions which is the case of 100,000 muons. This indicates that the removal of a standard fuel assembly can be identified using muons providing that enough muons are collected. A Bayesian algorithm was developed to classify dry casks and provide a decision rule that minimizes the risk of making an incorrect decision. The algorithm performance was evaluated and the lower detection limit was determined.

A statistical test of unbiased evolution of body size in birds.

PubMed

Bokma, Folmer

2002-12-01

Of the approximately 9500 bird species, the vast majority is small-bodied. That is a general feature of evolutionary lineages, also observed for instance in mammals and plants. The avian interspecific body size distribution is right-skewed even on a logarithmic scale. That has previously been interpreted as evidence that body size evolution has been biased. However, a procedure to test for unbiased evolution from the shape of body size distributions was lacking. In the present paper unbiased body size evolution is defined precisely, and a statistical test is developed based on Monte Carlo simulation of unbiased evolution. Application of the test to birds suggests that it is highly unlikely that avian body size evolution has been unbiased as defined. Several possible explanations for this result are discussed. A plausible explanation is that the general model of unbiased evolution assumes that population size and generation time do not affect the evolutionary variability of body size; that is, that micro- and macroevolution are decoupled, which theory suggests is not likely to be the case.

Limited variance control in statistical low thrust guidance analysis. [stochastic algorithm for SEP comet Encke flyby mission

NASA Technical Reports Server (NTRS)

Jacobson, R. A.

1975-01-01

Difficulties arise in guiding a solar electric propulsion spacecraft due to nongravitational accelerations caused by random fluctuations in the magnitude and direction of the thrust vector. These difficulties may be handled by using a low thrust guidance law based on the linear-quadratic-Gaussian problem of stochastic control theory with a minimum terminal miss performance criterion. Explicit constraints are imposed on the variances of the control parameters, and an algorithm based on the Hilbert space extension of a parameter optimization method is presented for calculation of gains in the guidance law. The terminal navigation of a 1980 flyby mission to the comet Encke is used as an example.

Static vs stochastic optimization: A case study of FTSE Bursa Malaysia sectorial indices

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

Mamat, Nur Jumaadzan Zaleha; Jaaman, Saiful Hafizah; Ahmad, Rokiah Rozita

2014-06-19

Traditional portfolio optimization methods in the likes of Markowitz' mean-variance model and semi-variance model utilize static expected return and volatility risk from historical data to generate an optimal portfolio. The optimal portfolio may not truly be optimal in reality due to the fact that maximum and minimum values from the data may largely influence the expected return and volatility risk values. This paper considers distributions of assets' return and volatility risk to determine a more realistic optimized portfolio. For illustration purposes, the sectorial indices data in FTSE Bursa Malaysia is employed. The results show that stochastic optimization provides more stablemore » information ratio.« less

Mutually unbiased bases and semi-definite programming

NASA Astrophysics Data System (ADS)

Brierley, Stephen; Weigert, Stefan

2010-11-01

A complex Hilbert space of dimension six supports at least three but not more than seven mutually unbiased bases. Two computer-aided analytical methods to tighten these bounds are reviewed, based on a discretization of parameter space and on Gröbner bases. A third algorithmic approach is presented: the non-existence of more than three mutually unbiased bases in composite dimensions can be decided by a global optimization method known as semidefinite programming. The method is used to confirm that the spectral matrix cannot be part of a complete set of seven mutually unbiased bases in dimension six.

Dimensionality and noise in energy selective x-ray imaging

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

Alvarez, Robert E.

Purpose: To develop and test a method to quantify the effect of dimensionality on the noise in energy selective x-ray imaging.Methods: The Cramèr-Rao lower bound (CRLB), a universal lower limit of the covariance of any unbiased estimator, is used to quantify the noise. It is shown that increasing dimensionality always increases, or at best leaves the same, the variance. An analytic formula for the increase in variance in an energy selective x-ray system is derived. The formula is used to gain insight into the dependence of the increase in variance on the properties of the additional basis functions, the measurementmore » noise covariance, and the source spectrum. The formula is also used with computer simulations to quantify the dependence of the additional variance on these factors. Simulated images of an object with three materials are used to demonstrate the trade-off of increased information with dimensionality and noise. The images are computed from energy selective data with a maximum likelihood estimator.Results: The increase in variance depends most importantly on the dimension and on the properties of the additional basis functions. With the attenuation coefficients of cortical bone, soft tissue, and adipose tissue as the basis functions, the increase in variance of the bone component from two to three dimensions is 1.4 × 10{sup 3}. With the soft tissue component, it is 2.7 × 10{sup 4}. If the attenuation coefficient of a high atomic number contrast agent is used as the third basis function, there is only a slight increase in the variance from two to three basis functions, 1.03 and 7.4 for the bone and soft tissue components, respectively. The changes in spectrum shape with beam hardening also have a substantial effect. They increase the variance by a factor of approximately 200 for the bone component and 220 for the soft tissue component as the soft tissue object thickness increases from 1 to 30 cm. Decreasing the energy resolution of the detectors increases the variance of the bone component markedly with three dimension processing, approximately a factor of 25 as the resolution decreases from 100 to 3 bins. The increase with two dimension processing for adipose tissue is a factor of two and with the contrast agent as the third material for two or three dimensions is also a factor of two for both components. The simulated images show that a maximum likelihood estimator can be used to process energy selective x-ray data to produce images with noise close to the CRLB.Conclusions: The method presented can be used to compute the effects of the object attenuation coefficients and the x-ray system properties on the relationship of dimensionality and noise in energy selective x-ray imaging systems.« less

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

PubMed Central

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

2014-01-01

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

Simplified Estimation and Testing in Unbalanced Repeated Measures Designs.

PubMed

Spiess, Martin; Jordan, Pascal; Wendt, Mike

2018-05-07

In this paper we propose a simple estimator for unbalanced repeated measures design models where each unit is observed at least once in each cell of the experimental design. The estimator does not require a model of the error covariance structure. Thus, circularity of the error covariance matrix and estimation of correlation parameters and variances are not necessary. Together with a weak assumption about the reason for the varying number of observations, the proposed estimator and its variance estimator are unbiased. As an alternative to confidence intervals based on the normality assumption, a bias-corrected and accelerated bootstrap technique is considered. We also propose the naive percentile bootstrap for Wald-type tests where the standard Wald test may break down when the number of observations is small relative to the number of parameters to be estimated. In a simulation study we illustrate the properties of the estimator and the bootstrap techniques to calculate confidence intervals and conduct hypothesis tests in small and large samples under normality and non-normality of the errors. The results imply that the simple estimator is only slightly less efficient than an estimator that correctly assumes a block structure of the error correlation matrix, a special case of which is an equi-correlation matrix. Application of the estimator and the bootstrap technique is illustrated using data from a task switch experiment based on an experimental within design with 32 cells and 33 participants.

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

PubMed

Chaudhuri, Shomesh E; Merfeld, Daniel M

2013-03-01

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

Momentum Flux Determination Using the Multi-beam Poker Flat Incoherent Scatter Radar

NASA Technical Reports Server (NTRS)

Nicolls, M. J.; Fritts, D. C.; Janches, Diego; Heinselman, C. J.

2012-01-01

In this paper, we develop an estimator for the vertical flux of horizontal momentum with arbitrary beam pointing, applicable to the case of arbitrary but fixed beam pointing with systems such as the Poker Flat Incoherent Scatter Radar (PFISR). This method uses information from all available beams to resolve the variances of the wind field in addition to the vertical flux of both meridional and zonal momentum, targeted for high-frequency wave motions. The estimator utilises the full covariance of the distributed measurements, which provides a significant reduction in errors over the direct extension of previously developed techniques and allows for the calculation of an error covariance matrix of the estimated quantities. We find that for the PFISR experiment, we can construct an unbiased and robust estimator of the momentum flux if sufficient and proper beam orientations are chosen, which can in the future be optimized for the expected frequency distribution of momentum-containing scales. However, there is a potential trade-off between biases and standard errors introduced with the new approach, which must be taken into account when assessing the momentum fluxes. We apply the estimator to PFISR measurements on 23 April 2008 and 21 December 2007, from 60-85 km altitude, and show expected results as compared to mean winds and in relation to the measured vertical velocity variances.

Integrating Nonadditive Genomic Relationship Matrices into the Study of Genetic Architecture of Complex Traits.

PubMed

Nazarian, Alireza; Gezan, Salvador A

2016-03-01

The study of genetic architecture of complex traits has been dramatically influenced by implementing genome-wide analytical approaches during recent years. Of particular interest are genomic prediction strategies which make use of genomic information for predicting phenotypic responses instead of detecting trait-associated loci. In this work, we present the results of a simulation study to improve our understanding of the statistical properties of estimation of genetic variance components of complex traits, and of additive, dominance, and genetic effects through best linear unbiased prediction methodology. Simulated dense marker information was used to construct genomic additive and dominance matrices, and multiple alternative pedigree- and marker-based models were compared to determine if including a dominance term into the analysis may improve the genetic analysis of complex traits. Our results showed that a model containing a pedigree- or marker-based additive relationship matrix along with a pedigree-based dominance matrix provided the best partitioning of genetic variance into its components, especially when some degree of true dominance effects was expected to exist. Also, we noted that the use of a marker-based additive relationship matrix along with a pedigree-based dominance matrix had the best performance in terms of accuracy of correlations between true and estimated additive, dominance, and genetic effects. © The American Genetic Association 2015. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Linear and Order Statistics Combiners for Pattern Classification

NASA Technical Reports Server (NTRS)

Tumer, Kagan; Ghosh, Joydeep; Lau, Sonie (Technical Monitor)

2001-01-01

Several researchers have experimentally shown that substantial improvements can be obtained in difficult pattern recognition problems by combining or integrating the outputs of multiple classifiers. This chapter provides an analytical framework to quantify the improvements in classification results due to combining. The results apply to both linear combiners and order statistics combiners. We first show that to a first order approximation, the error rate obtained over and above the Bayes error rate, is directly proportional to the variance of the actual decision boundaries around the Bayes optimum boundary. Combining classifiers in output space reduces this variance, and hence reduces the 'added' error. If N unbiased classifiers are combined by simple averaging. the added error rate can be reduced by a factor of N if the individual errors in approximating the decision boundaries are uncorrelated. Expressions are then derived for linear combiners which are biased or correlated, and the effect of output correlations on ensemble performance is quantified. For order statistics based non-linear combiners, we derive expressions that indicate how much the median, the maximum and in general the i-th order statistic can improve classifier performance. The analysis presented here facilitates the understanding of the relationships among error rates, classifier boundary distributions, and combining in output space. Experimental results on several public domain data sets are provided to illustrate the benefits of combining and to support the analytical results.

Prediction of lethal/effective concentration/dose in the presence of multiple auxiliary covariates and components of variance

USGS Publications Warehouse

Gutreuter, S.; Boogaard, M.A.

2007-01-01

Predictors of the percentile lethal/effective concentration/dose are commonly used measures of efficacy and toxicity. Typically such quantal-response predictors (e.g., the exposure required to kill 50% of some population) are estimated from simple bioassays wherein organisms are exposed to a gradient of several concentrations of a single agent. The toxicity of an agent may be influenced by auxiliary covariates, however, and more complicated experimental designs may introduce multiple variance components. Prediction methods lag examples of those cases. A conventional two-stage approach consists of multiple bivariate predictions of, say, medial lethal concentration followed by regression of those predictions on the auxiliary covariates. We propose a more effective and parsimonious class of generalized nonlinear mixed-effects models for prediction of lethal/effective dose/concentration from auxiliary covariates. We demonstrate examples using data from a study regarding the effects of pH and additions of variable quantities 2???,5???-dichloro-4???- nitrosalicylanilide (niclosamide) on the toxicity of 3-trifluoromethyl-4- nitrophenol to larval sea lamprey (Petromyzon marinus). The new models yielded unbiased predictions and root-mean-squared errors (RMSEs) of prediction for the exposure required to kill 50 and 99.9% of some population that were 29 to 82% smaller, respectively, than those from the conventional two-stage procedure. The model class is flexible and easily implemented using commonly available software. ?? 2007 SETAC.

Cerebellar contribution to motor and cognitive performance in multiple sclerosis: An MRI sub-regional volumetric analysis.

PubMed

D'Ambrosio, Alessandro; Pagani, Elisabetta; Riccitelli, Gianna C; Colombo, Bruno; Rodegher, Mariaemma; Falini, Andrea; Comi, Giancarlo; Filippi, Massimo; Rocca, Maria A

2017-08-01

To investigate the role of cerebellar sub-regions on motor and cognitive performance in multiple sclerosis (MS) patients. Whole and sub-regional cerebellar volumes, brain volumes, T2 hyperintense lesion volumes (LV), and motor performance scores were obtained from 95 relapse-onset MS patients and 32 healthy controls (HC). MS patients also underwent an evaluation of working memory and processing speed functions. Cerebellar anterior and posterior lobes were segmented using the Spatially Unbiased Infratentorial Toolbox (SUIT) from Statistical Parametric Mapping (SPM12). Multivariate linear regression models assessed the relationship between magnetic resonance imaging (MRI) measures and motor/cognitive scores. Compared to HC, only secondary progressive multiple sclerosis (SPMS) patients had lower cerebellar volumes (total and posterior cerebellum). In MS patients, lower anterior cerebellar volume and brain T2 LV predicted worse motor performance, whereas lower posterior cerebellar volume and brain T2 LV predicted poor cognitive performance. Global measures of brain volume and infratentorial T2 LV were not selected by the final multivariate models. Cerebellar volumetric abnormalities are likely to play an important contribution to explain motor and cognitive performance in MS patients. Consistently with functional mapping studies, cerebellar posterior-inferior volume accounted for variance in cognitive measures, whereas anterior cerebellar volume accounted for variance in motor performance, supporting the assessment of cerebellar damage at sub-regional level.

Maximum nonlocality and minimum uncertainty using magic states

NASA Astrophysics Data System (ADS)

Howard, Mark

2015-04-01

We prove that magic states from the Clifford hierarchy give optimal solutions for tasks involving nonlocality and entropic uncertainty with respect to Pauli measurements. For both the nonlocality and uncertainty tasks, stabilizer states are the worst possible pure states, so our solutions have an operational interpretation as being highly nonstabilizer. The optimal strategy for a qudit version of the Clauser-Horne-Shimony-Holt game in prime dimensions is achieved by measuring maximally entangled states that are isomorphic to single-qudit magic states. These magic states have an appealingly simple form, and our proof shows that they are "balanced" with respect to all but one of the mutually unbiased stabilizer bases. Of all equatorial qudit states, magic states minimize the average entropic uncertainties for collision entropy and also, for small prime dimensions, min-entropy, a fact that may have implications for cryptography.

SIC-POVMS and MUBS: Geometrical Relationships in Prime Dimension

NASA Astrophysics Data System (ADS)

Appleby, D. M.

2009-03-01

The paper concerns Weyl-Heisenberg covariant SIC-POVMs (symmetric informationally complete positive operator valued measures) and full sets of MUBs (mutually unbiased bases) in prime dimension. When represented as vectors in generalized Bloch space a SIC-POVM forms a d2-1 dimensional regular simplex (d being the Hilbert space dimension). By contrast, the generalized Bloch vectors representing a full set of MUBs form d+1 mutually orthogonal d-1 dimensional regular simplices. In this paper we show that, in the Weyl-Heisenberg case, there are some simple geometrical relationships between the single SIC-POVM simplex and the d+1 MUB simplices. We go on to give geometrical interpretations of the minimum uncertainty states introduced by Wootters and Sussman, and by Appleby, Dang and Fuchs, and of the fiduciality condition given by Appleby, Dang and Fuchs.

Brownian ratchets: How stronger thermal noise can reduce diffusion

NASA Astrophysics Data System (ADS)

Spiechowicz, Jakub; Kostur, Marcin; Łuczka, Jerzy

2017-02-01

We study diffusion properties of an inertial Brownian motor moving on a ratchet substrate, i.e., a periodic structure with broken reflection symmetry. The motor is driven by an unbiased time-periodic symmetric force that takes the system out of thermal equilibrium. For selected parameter sets, the system is in a non-chaotic regime in which we can identify a non-monotonic dependence of the diffusion coefficient on temperature: for low temperature, it initially increases as the temperature grows, passes through its local maximum, next starts to diminish reaching its local minimum, and finally it monotonically increases in accordance with the Einstein linear relation. Particularly interesting is the temperature interval in which diffusion is suppressed by the thermal noise, and we explain this effect in terms of transition rates of a three-state stochastic model.

Brownian ratchets: How stronger thermal noise can reduce diffusion.

PubMed

Spiechowicz, Jakub; Kostur, Marcin; Łuczka, Jerzy

2017-02-01

We study diffusion properties of an inertial Brownian motor moving on a ratchet substrate, i.e., a periodic structure with broken reflection symmetry. The motor is driven by an unbiased time-periodic symmetric force that takes the system out of thermal equilibrium. For selected parameter sets, the system is in a non-chaotic regime in which we can identify a non-monotonic dependence of the diffusion coefficient on temperature: for low temperature, it initially increases as the temperature grows, passes through its local maximum, next starts to diminish reaching its local minimum, and finally it monotonically increases in accordance with the Einstein linear relation. Particularly interesting is the temperature interval in which diffusion is suppressed by the thermal noise, and we explain this effect in terms of transition rates of a three-state stochastic model.

Intelligent ensemble T-S fuzzy neural networks with RCDPSO_DM optimization for effective handling of complex clinical pathway variances.

PubMed

Du, Gang; Jiang, Zhibin; Diao, Xiaodi; Yao, Yang

2013-07-01

Takagi-Sugeno (T-S) fuzzy neural networks (FNNs) can be used to handle complex, fuzzy, uncertain clinical pathway (CP) variances. However, there are many drawbacks, such as slow training rate, propensity to become trapped in a local minimum and poor ability to perform a global search. In order to improve overall performance of variance handling by T-S FNNs, a new CP variance handling method is proposed in this study. It is based on random cooperative decomposing particle swarm optimization with double mutation mechanism (RCDPSO_DM) for T-S FNNs. Moreover, the proposed integrated learning algorithm, combining the RCDPSO_DM algorithm with a Kalman filtering algorithm, is applied to optimize antecedent and consequent parameters of constructed T-S FNNs. Then, a multi-swarm cooperative immigrating particle swarm algorithm ensemble method is used for intelligent ensemble T-S FNNs with RCDPSO_DM optimization to further improve stability and accuracy of CP variance handling. Finally, two case studies on liver and kidney poisoning variances in osteosarcoma preoperative chemotherapy are used to validate the proposed method. The result demonstrates that intelligent ensemble T-S FNNs based on the RCDPSO_DM achieves superior performances, in terms of stability, efficiency, precision and generalizability, over PSO ensemble of all T-S FNNs with RCDPSO_DM optimization, single T-S FNNs with RCDPSO_DM optimization, standard T-S FNNs, standard Mamdani FNNs and T-S FNNs based on other algorithms (cooperative particle swarm optimization and particle swarm optimization) for CP variance handling. Therefore, it makes CP variance handling more effective. Copyright © 2013 Elsevier Ltd. All rights reserved.

Assessing the Minimum Number of Synchronization Triggers Necessary for Temporal Variance Compensation in Commercial Electroencephalography (EEG) Systems

DTIC Science & Technology

2012-09-01

by the ARL Translational Neuroscience Branch. It covers the Emotiv EPOC,6 Advanced Brain Monitoring (ABM) B-Alert X10,7 Quasar 8 DSI helmet-based...Systems; ARL-TR-5945; U.S. Army Research Laboratory: Aberdeen Proving Ground, MD, 2012 4 Ibid. 5 Ibid. 6 EPOC is a trademark of Emotiv . 7 B

Foreign Language Training in U.S. Undergraduate IB Programs: Are We Providing Students What They Need to Be Successful?

ERIC Educational Resources Information Center

Johnson, Jim

2017-01-01

A growing number of U.S. business schools now offer an undergraduate degree in international business (IB), for which training in a foreign language is a requirement. However, there appears to be considerable variance in the minimum requirements for foreign language training across U.S. business schools, including the provision of…

Iterative Minimum Variance Beamformer with Low Complexity for Medical Ultrasound Imaging.

PubMed

Deylami, Ali Mohades; Asl, Babak Mohammadzadeh

2018-06-04

Minimum variance beamformer (MVB) improves the resolution and contrast of medical ultrasound images compared with delay and sum (DAS) beamformer. The weight vector of this beamformer should be calculated for each imaging point independently, with a cost of increasing computational complexity. The large number of necessary calculations limits this beamformer to application in real-time systems. A beamformer is proposed based on the MVB with lower computational complexity while preserving its advantages. This beamformer avoids matrix inversion, which is the most complex part of the MVB, by solving the optimization problem iteratively. The received signals from two imaging points close together do not vary much in medical ultrasound imaging. Therefore, using the previously optimized weight vector for one point as initial weight vector for the new neighboring point can improve the convergence speed and decrease the computational complexity. The proposed method was applied on several data sets, and it has been shown that the method can regenerate the results obtained by the MVB while the order of complexity is decreased from O(L 3 ) to O(L 2 ). Copyright © 2018 World Federation for Ultrasound in Medicine and Biology. Published by Elsevier Inc. All rights reserved.

GIS-based niche modeling for mapping species' habitats

USGS Publications Warehouse

Rotenberry, J.T.; Preston, K.L.; Knick, S.

2006-01-01

Ecological a??niche modelinga?? using presence-only locality data and large-scale environmental variables provides a powerful tool for identifying and mapping suitable habitat for species over large spatial extents. We describe a niche modeling approach that identifies a minimum (rather than an optimum) set of basic habitat requirements for a species, based on the assumption that constant environmental relationships in a species' distribution (i.e., variables that maintain a consistent value where the species occurs) are most likely to be associated with limiting factors. Environmental variables that take on a wide range of values where a species occurs are less informative because they do not limit a species' distribution, at least over the range of variation sampled. This approach is operationalized by partitioning Mahalanobis D2 (standardized difference between values of a set of environmental variables for any point and mean values for those same variables calculated from all points at which a species was detected) into independent components. The smallest of these components represents the linear combination of variables with minimum variance; increasingly larger components represent larger variances and are increasingly less limiting. We illustrate this approach using the California Gnatcatcher (Polioptila californica Brewster) and provide SAS code to implement it.

Spectral analysis comparisons of Fourier-theory-based methods and minimum variance (Capon) methods

NASA Astrophysics Data System (ADS)

Garbanzo-Salas, Marcial; Hocking, Wayne. K.

2015-09-01

In recent years, adaptive (data dependent) methods have been introduced into many areas where Fourier spectral analysis has traditionally been used. Although the data-dependent methods are often advanced as being superior to Fourier methods, they do require some finesse in choosing the order of the relevant filters. In performing comparisons, we have found some concerns about the mappings, particularly when related to cases involving many spectral lines or even continuous spectral signals. Using numerical simulations, several comparisons between Fourier transform procedures and minimum variance method (MVM) have been performed. For multiple frequency signals, the MVM resolves most of the frequency content only for filters that have more degrees of freedom than the number of distinct spectral lines in the signal. In the case of Gaussian spectral approximation, MVM will always underestimate the width, and can misappropriate the location of spectral line in some circumstances. Large filters can be used to improve results with multiple frequency signals, but are computationally inefficient. Significant biases can occur when using MVM to study spectral information or echo power from the atmosphere. Artifacts and artificial narrowing of turbulent layers is one such impact.

An Experience Oriented-Convergence Improved Gravitational Search Algorithm for Minimum Variance Distortionless Response Beamforming Optimum.

PubMed

Darzi, Soodabeh; Tiong, Sieh Kiong; Tariqul Islam, Mohammad; Rezai Soleymanpour, Hassan; Kibria, Salehin

2016-01-01

An experience oriented-convergence improved gravitational search algorithm (ECGSA) based on two new modifications, searching through the best experiments and using of a dynamic gravitational damping coefficient (α), is introduced in this paper. ECGSA saves its best fitness function evaluations and uses those as the agents' positions in searching process. In this way, the optimal found trajectories are retained and the search starts from these trajectories, which allow the algorithm to avoid the local optimums. Also, the agents can move faster in search space to obtain better exploration during the first stage of the searching process and they can converge rapidly to the optimal solution at the final stage of the search process by means of the proposed dynamic gravitational damping coefficient. The performance of ECGSA has been evaluated by applying it to eight standard benchmark functions along with six complicated composite test functions. It is also applied to adaptive beamforming problem as a practical issue to improve the weight vectors computed by minimum variance distortionless response (MVDR) beamforming technique. The results of implementation of the proposed algorithm are compared with some well-known heuristic methods and verified the proposed method in both reaching to optimal solutions and robustness.

Significant improvements of electrical discharge machining performance by step-by-step updated adaptive control laws

NASA Astrophysics Data System (ADS)

Zhou, Ming; Wu, Jianyang; Xu, Xiaoyi; Mu, Xin; Dou, Yunping

2018-02-01

In order to obtain improved electrical discharge machining (EDM) performance, we have dedicated more than a decade to correcting one essential EDM defect, the weak stability of the machining, by developing adaptive control systems. The instabilities of machining are mainly caused by complicated disturbances in discharging. To counteract the effects from the disturbances on machining, we theoretically developed three control laws from minimum variance (MV) control law to minimum variance and pole placements coupled (MVPPC) control law and then to a two-step-ahead prediction (TP) control law. Based on real-time estimation of EDM process model parameters and measured ratio of arcing pulses which is also called gap state, electrode discharging cycle was directly and adaptively tuned so that a stable machining could be achieved. To this end, we not only theoretically provide three proved control laws for a developed EDM adaptive control system, but also practically proved the TP control law to be the best in dealing with machining instability and machining efficiency though the MVPPC control law provided much better EDM performance than the MV control law. It was also shown that the TP control law also provided a burn free machining.

The performance of matched-field track-before-detect methods using shallow-water Pacific data.

PubMed

Tantum, Stacy L; Nolte, Loren W; Krolik, Jeffrey L; Harmanci, Kerem

2002-07-01

Matched-field track-before-detect processing, which extends the concept of matched-field processing to include modeling of the source dynamics, has recently emerged as a promising approach for maintaining the track of a moving source. In this paper, optimal Bayesian and minimum variance beamforming track-before-detect algorithms which incorporate a priori knowledge of the source dynamics in addition to the underlying uncertainties in the ocean environment are presented. A Markov model is utilized for the source motion as a means of capturing the stochastic nature of the source dynamics without assuming uniform motion. In addition, the relationship between optimal Bayesian track-before-detect processing and minimum variance track-before-detect beamforming is examined, revealing how an optimal tracking philosophy may be used to guide the modification of existing beamforming techniques to incorporate track-before-detect capabilities. Further, the benefits of implementing an optimal approach over conventional methods are illustrated through application of these methods to shallow-water Pacific data collected as part of the SWellEX-1 experiment. The results show that incorporating Markovian dynamics for the source motion provides marked improvement in the ability to maintain target track without the use of a uniform velocity hypothesis.

Unbiasedness

USGS Publications Warehouse

Link, W.A.; Armitage, Peter; Colton, Theodore

1998-01-01

Unbiasedness is probably the best known criterion for evaluating the performance of estimators. This note describes unbiasedness, demonstrating various failings of the criterion. It is shown that unbiased estimators might not exist, or might not be unique; an example of a unique but clearly unacceptable unbiased estimator is given. It is shown that unbiased estimators are not translation invariant. Various alternative criteria are described, and are illustrated through examples.

FAST TRACK COMMUNICATION: Affine constellations without mutually unbiased counterparts

NASA Astrophysics Data System (ADS)

Weigert, Stefan; Durt, Thomas

2010-10-01

It has been conjectured that a complete set of mutually unbiased bases in a space of dimension d exists if and only if there is an affine plane of order d. We introduce affine constellations and compare their existence properties with those of mutually unbiased constellations. The observed discrepancies make a deeper relation between the two existence problems unlikely.

Number-phase minimum-uncertainty state with reduced number uncertainty in a Kerr nonlinear interferometer

NASA Astrophysics Data System (ADS)

Kitagawa, M.; Yamamoto, Y.

1987-11-01

An alternative scheme for generating amplitude-squeezed states of photons based on unitary evolution which can properly be described by quantum mechanics is presented. This scheme is a nonlinear Mach-Zehnder interferometer containing an optical Kerr medium. The quasi-probability density (QPD) and photon-number distribution of the output field are calculated, and it is demonstrated that the reduced photon-number uncertainty and enhanced phase uncertainty maintain the minimum-uncertainty product. A self-phase-modulation of the single-mode quantized field in the Kerr medium is described based on localized operators. The spatial evolution of the state is demonstrated by QPD in the Schroedinger picture. It is shown that photon-number variance can be reduced to a level far below the limit for an ordinary squeezed state, and that the state prepared using this scheme remains a number-phase minimum-uncertainty state until the maximum reduction of number fluctuations is surpassed.

Relation between Pressure Balance Structures and Polar Plumes from Ulysses High Latitude Observations

NASA Technical Reports Server (NTRS)

Yamauchi, Yohei; Suess, Steven T.; Sakurai, Takashi

2002-01-01

Ulysses observations have shown that pressure balance structures (PBSs) are a common feature in high-latitude, fast solar wind near solar minimum. Previous studies of Ulysses/SWOOPS plasma data suggest these PBSs may be remnants of coronal polar plumes. Here we find support for this suggestion in an analysis of PBS magnetic structure. We used Ulysses magnetometer data and applied a minimum variance analysis to magnetic discontinuities in PBSs. We found that PBSs preferentially contain tangential discontinuities, as opposed to rotational discontinuities and to non-PBS regions in the solar wind. This suggests that PBSs contain structures like current sheets or plasmoids that may be associated with network activity at the base of plumes.

Relation Between Pressure Balance Structures and Polar Plumes from Ulysses High Latitude Observations

NASA Technical Reports Server (NTRS)

Yamauchi, Y.; Suess, Steven T.; Sakurai, T.; Whitaker, Ann F. (Technical Monitor)

2001-01-01

Ulysses observations have shown that pressure balance structures (PBSs) are a common feature in high-latitude, fast solar wind near solar minimum. Previous studies of Ulysses/SWOOPS plasma data suggest these PBSs may be remnants of coronal polar plumes. Here we find support for this suggestion in an analysis of PBS magnetic structure. We used Ulysses magnetometer data and applied a minimum variance analysis to discontinuities. We found that PBSs preferentially contain tangential discontinuities, as opposed to rotational discontinuities and to non-PBS regions in the solar wind. This suggests that PBSs contain structures like current sheets or plasmoids that may be associated with network activity at the base of plumes.

Gaussian statistics for palaeomagnetic vectors

USGS Publications Warehouse

Love, J.J.; Constable, C.G.

2003-01-01

With the aim of treating the statistics of palaeomagnetic directions and intensities jointly and consistently, we represent the mean and the variance of palaeomagnetic vectors, at a particular site and of a particular polarity, by a probability density function in a Cartesian three-space of orthogonal magnetic-field components consisting of a single (unimoda) non-zero mean, spherically-symmetrical (isotropic) Gaussian function. For palaeomagnetic data of mixed polarities, we consider a bimodal distribution consisting of a pair of such symmetrical Gaussian functions, with equal, but opposite, means and equal variances. For both the Gaussian and bi-Gaussian distributions, and in the spherical three-space of intensity, inclination, and declination, we obtain analytical expressions for the marginal density functions, the cumulative distributions, and the expected values and variances for each spherical coordinate (including the angle with respect to the axis of symmetry of the distributions). The mathematical expressions for the intensity and off-axis angle are closed-form and especially manageable, with the intensity distribution being Rayleigh-Rician. In the limit of small relative vectorial dispersion, the Gaussian (bi-Gaussian) directional distribution approaches a Fisher (Bingham) distribution and the intensity distribution approaches a normal distribution. In the opposite limit of large relative vectorial dispersion, the directional distributions approach a spherically-uniform distribution and the intensity distribution approaches a Maxwell distribution. We quantify biases in estimating the properties of the vector field resulting from the use of simple arithmetic averages, such as estimates of the intensity or the inclination of the mean vector, or the variances of these quantities. With the statistical framework developed here and using the maximum-likelihood method, which gives unbiased estimates in the limit of large data numbers, we demonstrate how to formulate the inverse problem, and how to estimate the mean and variance of the magnetic vector field, even when the data consist of mixed combinations of directions and intensities. We examine palaeomagnetic secular-variation data from Hawaii and Re??union, and although these two sites are on almost opposite latitudes, we find significant differences in the mean vector and differences in the local vectorial variances, with the Hawaiian data being particularly anisotropic. These observations are inconsistent with a description of the mean field as being a simple geocentric axial dipole and with secular variation being statistically symmetrical with respect to reflection through the equatorial plane. Finally, our analysis of palaeomagnetic acquisition data from the 1960 Kilauea flow in Hawaii and the Holocene Xitle flow in Mexico, is consistent with the widely held suspicion that directional data are more accurate than intensity data.

Gaussian statistics for palaeomagnetic vectors

NASA Astrophysics Data System (ADS)

Love, J. J.; Constable, C. G.

2003-03-01

With the aim of treating the statistics of palaeomagnetic directions and intensities jointly and consistently, we represent the mean and the variance of palaeomagnetic vectors, at a particular site and of a particular polarity, by a probability density function in a Cartesian three-space of orthogonal magnetic-field components consisting of a single (unimodal) non-zero mean, spherically-symmetrical (isotropic) Gaussian function. For palaeomagnetic data of mixed polarities, we consider a bimodal distribution consisting of a pair of such symmetrical Gaussian functions, with equal, but opposite, means and equal variances. For both the Gaussian and bi-Gaussian distributions, and in the spherical three-space of intensity, inclination, and declination, we obtain analytical expressions for the marginal density functions, the cumulative distributions, and the expected values and variances for each spherical coordinate (including the angle with respect to the axis of symmetry of the distributions). The mathematical expressions for the intensity and off-axis angle are closed-form and especially manageable, with the intensity distribution being Rayleigh-Rician. In the limit of small relative vectorial dispersion, the Gaussian (bi-Gaussian) directional distribution approaches a Fisher (Bingham) distribution and the intensity distribution approaches a normal distribution. In the opposite limit of large relative vectorial dispersion, the directional distributions approach a spherically-uniform distribution and the intensity distribution approaches a Maxwell distribution. We quantify biases in estimating the properties of the vector field resulting from the use of simple arithmetic averages, such as estimates of the intensity or the inclination of the mean vector, or the variances of these quantities. With the statistical framework developed here and using the maximum-likelihood method, which gives unbiased estimates in the limit of large data numbers, we demonstrate how to formulate the inverse problem, and how to estimate the mean and variance of the magnetic vector field, even when the data consist of mixed combinations of directions and intensities. We examine palaeomagnetic secular-variation data from Hawaii and Réunion, and although these two sites are on almost opposite latitudes, we find significant differences in the mean vector and differences in the local vectorial variances, with the Hawaiian data being particularly anisotropic. These observations are inconsistent with a description of the mean field as being a simple geocentric axial dipole and with secular variation being statistically symmetrical with respect to reflection through the equatorial plane. Finally, our analysis of palaeomagnetic acquisition data from the 1960 Kilauea flow in Hawaii and the Holocene Xitle flow in Mexico, is consistent with the widely held suspicion that directional data are more accurate than intensity data.

Attempts to Simulate Anisotropies of Solar Wind Fluctuations Using MHD with a Turning Magnetic Field

NASA Technical Reports Server (NTRS)

Ghosh, Sanjoy; Roberts, D. Aaron

2010-01-01

We examine a "two-component" model of the solar wind to see if any of the observed anisotropies of the fields can be explained in light of the need for various quantities, such as the magnetic minimum variance direction, to turn along with the Parker spiral. Previous results used a 3-D MHD spectral code to show that neither Q2D nor slab-wave components will turn their wave vectors in a turning Parker-like field, and that nonlinear interactions between the components are required to reproduce observations. In these new simulations we use higher resolution in both decaying and driven cases, and with and without a turning background field, to see what, if any, conditions lead to variance anisotropies similar to observations. We focus especially on the middle spectral range, and not the energy-containing scales, of the simulation for comparison with the solar wind. Preliminary results have shown that it is very difficult to produce the required variances with a turbulent cascade.

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

Aziz, Mohd Khairul Bazli Mohd, E-mail: mkbazli@yahoo.com; Yusof, Fadhilah, E-mail: fadhilahy@utm.my; Daud, Zalina Mohd, E-mail: zalina@ic.utm.my

Recently, many rainfall network design techniques have been developed, discussed and compared by many researchers. Present day hydrological studies require higher levels of accuracy from collected data. In numerous basins, the rain gauge stations are located without clear scientific understanding. In this study, an attempt is made to redesign rain gauge network for Johor, Malaysia in order to meet the required level of accuracy preset by rainfall data users. The existing network of 84 rain gauges in Johor is optimized and redesigned into a new locations by using rainfall, humidity, solar radiation, temperature and wind speed data collected during themore » monsoon season (November - February) of 1975 until 2008. This study used the combination of geostatistics method (variance-reduction method) and simulated annealing as the algorithm of optimization during the redesigned proses. The result shows that the new rain gauge location provides minimum value of estimated variance. This shows that the combination of geostatistics method (variance-reduction method) and simulated annealing is successful in the development of the new optimum rain gauge system.« less

Experimental demonstration of quantum teleportation of a squeezed state

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

Takei, Nobuyuki; Aoki, Takao; Yonezawa, Hidehiro

2005-10-15

Quantum teleportation of a squeezed state is demonstrated experimentally. Due to some inevitable losses in experiments, a squeezed vacuum necessarily becomes a mixed state which is no longer a minimum uncertainty state. We establish an operational method of evaluation for quantum teleportation of such a state using fidelity and discuss the classical limit for the state. The measured fidelity for the input state is 0.85{+-}0.05, which is higher than the classical case of 0.73{+-}0.04. We also verify that the teleportation process operates properly for the nonclassical state input and its squeezed variance is certainly transferred through the process. We observemore » the smaller variance of the teleported squeezed state than that for the vacuum state input.« less

Quantizing and sampling considerations in digital phased-locked loops

NASA Technical Reports Server (NTRS)

Hurst, G. T.; Gupta, S. C.

1974-01-01

The quantizer problem is first considered. The conditions under which the uniform white sequence model for the quantizer error is valid are established independent of the sampling rate. An equivalent spectral density is defined for the quantizer error resulting in an effective SNR value. This effective SNR may be used to determine quantized performance from infinitely fine quantized results. Attention is given to sampling rate considerations. Sampling rate characteristics of the digital phase-locked loop (DPLL) structure are investigated for the infinitely fine quantized system. The predicted phase error variance equation is examined as a function of the sampling rate. Simulation results are presented and a method is described which enables the minimum required sampling rate to be determined from the predicted phase error variance equations.

A stereotaxic, population-averaged T1w ovine brain atlas including cerebral morphology and tissue volumes

PubMed Central

Nitzsche, Björn; Frey, Stephen; Collins, Louis D.; Seeger, Johannes; Lobsien, Donald; Dreyer, Antje; Kirsten, Holger; Stoffel, Michael H.; Fonov, Vladimir S.; Boltze, Johannes

2015-01-01

Standard stereotaxic reference systems play a key role in human brain studies. Stereotaxic coordinate systems have also been developed for experimental animals including non-human primates, dogs, and rodents. However, they are lacking for other species being relevant in experimental neuroscience including sheep. Here, we present a spatial, unbiased ovine brain template with tissue probability maps (TPM) that offer a detailed stereotaxic reference frame for anatomical features and localization of brain areas, thereby enabling inter-individual and cross-study comparability. Three-dimensional data sets from healthy adult Merino sheep (Ovis orientalis aries, 12 ewes and 26 neutered rams) were acquired on a 1.5 T Philips MRI using a T1w sequence. Data were averaged by linear and non-linear registration algorithms. Moreover, animals were subjected to detailed brain volume analysis including examinations with respect to body weight (BW), age, and sex. The created T1w brain template provides an appropriate population-averaged ovine brain anatomy in a spatial standard coordinate system. Additionally, TPM for gray (GM) and white (WM) matter as well as cerebrospinal fluid (CSF) classification enabled automatic prior-based tissue segmentation using statistical parametric mapping (SPM). Overall, a positive correlation of GM volume and BW explained about 15% of the variance of GM while a positive correlation between WM and age was found. Absolute tissue volume differences were not detected, indeed ewes showed significantly more GM per bodyweight as compared to neutered rams. The created framework including spatial brain template and TPM represent a useful tool for unbiased automatic image preprocessing and morphological characterization in sheep. Therefore, the reported results may serve as a starting point for further experimental and/or translational research aiming at in vivo analysis in this species. PMID:26089780

Systematic random sampling of the comet assay.

PubMed

McArt, Darragh G; Wasson, Gillian R; McKerr, George; Saetzler, Kurt; Reed, Matt; Howard, C Vyvyan

2009-07-01

The comet assay is a technique used to quantify DNA damage and repair at a cellular level. In the assay, cells are embedded in agarose and the cellular content is stripped away leaving only the DNA trapped in an agarose cavity which can then be electrophoresed. The damaged DNA can enter the agarose and migrate while the undamaged DNA cannot and is retained. DNA damage is measured as the proportion of the migratory 'tail' DNA compared to the total DNA in the cell. The fundamental basis of these arbitrary values is obtained in the comet acquisition phase using fluorescence microscopy with a stoichiometric stain in tandem with image analysis software. Current methods deployed in such an acquisition are expected to be both objectively and randomly obtained. In this paper we examine the 'randomness' of the acquisition phase and suggest an alternative method that offers both objective and unbiased comet selection. In order to achieve this, we have adopted a survey sampling approach widely used in stereology, which offers a method of systematic random sampling (SRS). This is desirable as it offers an impartial and reproducible method of comet analysis that can be used both manually or automated. By making use of an unbiased sampling frame and using microscope verniers, we are able to increase the precision of estimates of DNA damage. Results obtained from a multiple-user pooled variation experiment showed that the SRS technique attained a lower variability than that of the traditional approach. The analysis of a single user with repetition experiment showed greater individual variances while not being detrimental to overall averages. This would suggest that the SRS method offers a better reflection of DNA damage for a given slide and also offers better user reproducibility.

Statistical procedures for determination and verification of minimum reporting levels for drinking water methods.

PubMed

Winslow, Stephen D; Pepich, Barry V; Martin, John J; Hallberg, George R; Munch, David J; Frebis, Christopher P; Hedrick, Elizabeth J; Krop, Richard A

2006-01-01

The United States Environmental Protection Agency's Office of Ground Water and Drinking Water has developed a single-laboratory quantitation procedure: the lowest concentration minimum reporting level (LCMRL). The LCMRL is the lowest true concentration for which future recovery is predicted to fall, with high confidence (99%), between 50% and 150%. The procedure takes into account precision and accuracy. Multiple concentration replicates are processed through the entire analytical method and the data are plotted as measured sample concentration (y-axis) versus true concentration (x-axis). If the data support an assumption of constant variance over the concentration range, an ordinary least-squares regression line is drawn; otherwise, a variance-weighted least-squares regression is used. Prediction interval lines of 99% confidence are drawn about the regression. At the points where the prediction interval lines intersect with data quality objective lines of 50% and 150% recovery, lines are dropped to the x-axis. The higher of the two values is the LCMRL. The LCMRL procedure is flexible because the data quality objectives (50-150%) and the prediction interval confidence (99%) can be varied to suit program needs. The LCMRL determination is performed during method development only. A simpler procedure for verification of data quality objectives at a given minimum reporting level (MRL) is also presented. The verification procedure requires a single set of seven samples taken through the entire method procedure. If the calculated prediction interval is contained within data quality recovery limits (50-150%), the laboratory performance at the MRL is verified.

The effectiveness of texture analysis for mapping forest land using the panchromatic bands of Landsat 7, SPOT, and IRS imagery

Treesearch

Michael L. Hoppus; Rachel I. Riemann; Andrew J. Lister; Mark V. Finco

2002-01-01

The panchromatic bands of Landsat 7, SPOT, and IRS satellite imagery provide an opportunity to evaluate the effectiveness of texture analysis of satellite imagery for mapping of land use/cover, especially forest cover. A variety of texture algorithms, including standard deviation, Ryherd-Woodcock minimum variance adaptive window, low pass etc., were applied to moving...

Solution Methods for Certain Evolution Equations

NASA Astrophysics Data System (ADS)

Vega-Guzman, Jose Manuel

Solution methods for certain linear and nonlinear evolution equations are presented in this dissertation. Emphasis is placed mainly on the analytical treatment of nonautonomous differential equations, which are challenging to solve despite the existent numerical and symbolic computational software programs available. Ideas from the transformation theory are adopted allowing one to solve the problems under consideration from a non-traditional perspective. First, the Cauchy initial value problem is considered for a class of nonautonomous and inhomogeneous linear diffusion-type equation on the entire real line. Explicit transformations are used to reduce the equations under study to their corresponding standard forms emphasizing on natural relations with certain Riccati(and/or Ermakov)-type systems. These relations give solvability results for the Cauchy problem of the parabolic equation considered. The superposition principle allows to solve formally this problem from an unconventional point of view. An eigenfunction expansion approach is also considered for this general evolution equation. Examples considered to corroborate the efficacy of the proposed solution methods include the Fokker-Planck equation, the Black-Scholes model and the one-factor Gaussian Hull-White model. The results obtained in the first part are used to solve the Cauchy initial value problem for certain inhomogeneous Burgers-type equation. The connection between linear (the Diffusion-type) and nonlinear (Burgers-type) parabolic equations is stress in order to establish a strong commutative relation. Traveling wave solutions of a nonautonomous Burgers equation are also investigated. Finally, it is constructed explicitly the minimum-uncertainty squeezed states for quantum harmonic oscillators. They are derived by the action of corresponding maximal kinematical invariance group on the standard ground state solution. It is shown that the product of the variances attains the required minimum value only at the instances that one variance is a minimum and the other is a maximum, when the squeezing of one of the variances occurs. Such explicit construction is possible due to the relation between the diffusion-type equation studied in the first part and the time-dependent Schrodinger equation. A modication of the radiation field operators for squeezed photons in a perfect cavity is also suggested with the help of a nonstandard solution of Heisenberg's equation of motion.

Eigenspace-based minimum variance beamformer combined with Wiener postfilter for medical ultrasound imaging.

PubMed

Zeng, Xing; Chen, Cheng; Wang, Yuanyuan

2012-12-01

In this paper, a new beamformer which combines the eigenspace-based minimum variance (ESBMV) beamformer with the Wiener postfilter is proposed for medical ultrasound imaging. The primary goal of this work is to further improve the medical ultrasound imaging quality on the basis of the ESBMV beamformer. In this method, we optimize the ESBMV weights with a Wiener postfilter. With the optimization of the Wiener postfilter, the output power of the new beamformer becomes closer to the actual signal power at the imaging point than the ESBMV beamformer. Different from the ordinary Wiener postfilter, the output signal and noise power needed in calculating the Wiener postfilter are estimated respectively by the orthogonal signal subspace and noise subspace constructed from the eigenstructure of the sample covariance matrix. We demonstrate the performance of the new beamformer when resolving point scatterers and cyst phantom using both simulated data and experimental data and compare it with the delay-and-sum (DAS), the minimum variance (MV) and the ESBMV beamformer. We use the full width at half maximum (FWHM) and the peak-side-lobe level (PSL) to quantify the performance of imaging resolution and the contrast ratio (CR) to quantify the performance of imaging contrast. The FWHM of the new beamformer is only 15%, 50% and 50% of those of the DAS, MV and ESBMV beamformer, while the PSL is 127.2dB, 115dB and 60dB lower. What is more, an improvement of 239.8%, 232.5% and 32.9% in CR using simulated data and an improvement of 814%, 1410.7% and 86.7% in CR using experimental data are achieved compared to the DAS, MV and ESBMV beamformer respectively. In addition, the effect of the sound speed error is investigated by artificially overestimating the speed used in calculating the propagation delay and the results show that the new beamformer provides better robustness against the sound speed errors. Therefore, the proposed beamformer offers a better performance than the DAS, MV and ESBMV beamformer, showing its potential in medical ultrasound imaging. Copyright © 2012 Elsevier B.V. All rights reserved.

Bias and robustness of uncertainty components estimates in transient climate projections

NASA Astrophysics Data System (ADS)

Hingray, Benoit; Blanchet, Juliette; Jean-Philippe, Vidal

2016-04-01

A critical issue in climate change studies is the estimation of uncertainties in projections along with the contribution of the different uncertainty sources, including scenario uncertainty, the different components of model uncertainty and internal variability. Quantifying the different uncertainty sources faces actually different problems. For instance and for the sake of simplicity, an estimate of model uncertainty is classically obtained from the empirical variance of the climate responses obtained for the different modeling chains. These estimates are however biased. Another difficulty arises from the limited number of members that are classically available for most modeling chains. In this case, the climate response of one given chain and the effect of its internal variability may be actually difficult if not impossible to separate. The estimate of scenario uncertainty, model uncertainty and internal variability components are thus likely to be not really robust. We explore the importance of the bias and the robustness of the estimates for two classical Analysis of Variance (ANOVA) approaches: a Single Time approach (STANOVA), based on the only data available for the considered projection lead time and a time series based approach (QEANOVA), which assumes quasi-ergodicity of climate outputs over the whole available climate simulation period (Hingray and Saïd, 2014). We explore both issues for a simple but classical configuration where uncertainties in projections are composed of two single sources: model uncertainty and internal climate variability. The bias in model uncertainty estimates is explored from theoretical expressions of unbiased estimators developed for both ANOVA approaches. The robustness of uncertainty estimates is explored for multiple synthetic ensembles of time series projections generated with MonteCarlo simulations. For both ANOVA approaches, when the empirical variance of climate responses is used to estimate model uncertainty, the bias is always positive. It can be especially high with STANOVA. In the most critical configurations, when the number of members available for each modeling chain is small (< 3) and when internal variability explains most of total uncertainty variance (75% or more), the overestimation is higher than 100% of the true model uncertainty variance. The bias can be considerably reduced with a time series ANOVA approach, owing to the multiple time steps accounted for. The longer the transient time period used for the analysis, the larger the reduction. When a quasi-ergodic ANOVA approach is applied to decadal data for the whole 1980-2100 period, the bias is reduced by a factor 2.5 to 20 depending on the projection lead time. In all cases, the bias is likely to be not negligible for a large number of climate impact studies resulting in a likely large overestimation of the contribution of model uncertainty to total variance. For both approaches, the robustness of all uncertainty estimates is higher when more members are available, when internal variability is smaller and/or the response-to-uncertainty ratio is higher. QEANOVA estimates are much more robust than STANOVA ones: QEANOVA simulated confidence intervals are roughly 3 to 5 times smaller than STANOVA ones. Excepted for STANOVA when less than 3 members is available, the robustness is rather high for total uncertainty and moderate for internal variability estimates. For model uncertainty or response-to-uncertainty ratio estimates, the robustness is conversely low for QEANOVA to very low for STANOVA. In the most critical configurations (small number of member, large internal variability), large over- or underestimation of uncertainty components is very thus likely. To propose relevant uncertainty analyses and avoid misleading interpretations, estimates of uncertainty components should be therefore bias corrected and ideally come with estimates of their robustness. This work is part of the COMPLEX Project (European Collaborative Project FP7-ENV-2012 number: 308601; http://www.complex.ac.uk/). Hingray, B., Saïd, M., 2014. Partitioning internal variability and model uncertainty components in a multimodel multireplicate ensemble of climate projections. J.Climate. doi:10.1175/JCLI-D-13-00629.1 Hingray, B., Blanchet, J. (revision) Unbiased estimators for uncertainty components in transient climate projections. J. Climate Hingray, B., Blanchet, J., Vidal, J.P. (revision) Robustness of uncertainty components estimates in climate projections. J.Climate

Aspects of mutually unbiased bases in odd-prime-power dimensions

NASA Astrophysics Data System (ADS)

Chaturvedi, S.

2002-04-01

We rephrase the Wootters-Fields construction [W. K. Wootters and B. C. Fields, Ann. Phys. 191, 363 (1989)] of a full set of mutually unbiased bases in a complex vector space of dimensions N=pr, where p is an odd prime, in terms of the character vectors of the cyclic group G of order p. This form may be useful in explicitly writing down mutually unbiased bases for N=pr.

Identification of modal parameters including unmeasured forces and transient effects

NASA Astrophysics Data System (ADS)

Cauberghe, B.; Guillaume, P.; Verboven, P.; Parloo, E.

2003-08-01

In this paper, a frequency-domain method to estimate modal parameters from short data records with known input (measured) forces and unknown input forces is presented. The method can be used for an experimental modal analysis, an operational modal analysis (output-only data) and the combination of both. A traditional experimental and operational modal analysis in the frequency domain starts respectively, from frequency response functions and spectral density functions. To estimate these functions accurately sufficient data have to be available. The technique developed in this paper estimates the modal parameters directly from the Fourier spectra of the outputs and the known input. Instead of using Hanning windows on these short data records the transient effects are estimated simultaneously with the modal parameters. The method is illustrated, tested and validated by Monte Carlo simulations and experiments. The presented method to process short data sequences leads to unbiased estimates with a small variance in comparison to the more traditional approaches.

On the International Agency for Research on Cancer classification of glyphosate as a probable human carcinogen.

PubMed

Tarone, Robert E

2018-01-01

The recent classification by International Agency for Research on Cancer (IARC) of the herbicide glyphosate as a probable human carcinogen has generated considerable discussion. The classification is at variance with evaluations of the carcinogenic potential of glyphosate by several national and international regulatory bodies. The basis for the IARC classification is examined under the assumptions that the IARC criteria are reasonable and that the body of scientific studies determined by IARC staff to be relevant to the evaluation of glyphosate by the Monograph Working Group is sufficiently complete. It is shown that the classification of glyphosate as a probable human carcinogen was the result of a flawed and incomplete summary of the experimental evidence evaluated by the Working Group. Rational and effective cancer prevention activities depend on scientifically sound and unbiased assessments of the carcinogenic potential of suspected agents. Implications of the erroneous classification of glyphosate with respect to the IARC Monograph Working Group deliberative process are discussed.

High-resolution Single Particle Analysis from Electron Cryo-microscopy Images Using SPHIRE

PubMed Central

Moriya, Toshio; Saur, Michael; Stabrin, Markus; Merino, Felipe; Voicu, Horatiu; Huang, Zhong; Penczek, Pawel A.; Raunser, Stefan; Gatsogiannis, Christos

2017-01-01

SPHIRE (SPARX for High-Resolution Electron Microscopy) is a novel open-source, user-friendly software suite for the semi-automated processing of single particle electron cryo-microscopy (cryo-EM) data. The protocol presented here describes in detail how to obtain a near-atomic resolution structure starting from cryo-EM micrograph movies by guiding users through all steps of the single particle structure determination pipeline. These steps are controlled from the new SPHIRE graphical user interface and require minimum user intervention. Using this protocol, a 3.5 Å structure of TcdA1, a Tc toxin complex from Photorhabdus luminescens, was derived from only 9500 single particles. This streamlined approach will help novice users without extensive processing experience and a priori structural information, to obtain noise-free and unbiased atomic models of their purified macromolecular complexes in their native state. PMID:28570515

Chaotic Signal Denoising Based on Hierarchical Threshold Synchrosqueezed Wavelet Transform

NASA Astrophysics Data System (ADS)

Wang, Wen-Bo; Jing, Yun-yu; Zhao, Yan-chao; Zhang, Lian-Hua; Wang, Xiang-Li

2017-12-01

In order to overcoming the shortcoming of single threshold synchrosqueezed wavelet transform(SWT) denoising method, an adaptive hierarchical threshold SWT chaotic signal denoising method is proposed. Firstly, a new SWT threshold function is constructed based on Stein unbiased risk estimation, which is two order continuous derivable. Then, by using of the new threshold function, a threshold process based on the minimum mean square error was implemented, and the optimal estimation value of each layer threshold in SWT chaotic denoising is obtained. The experimental results of the simulating chaotic signal and measured sunspot signals show that, the proposed method can filter the noise of chaotic signal well, and the intrinsic chaotic characteristic of the original signal can be recovered very well. Compared with the EEMD denoising method and the single threshold SWT denoising method, the proposed method can obtain better denoising result for the chaotic signal.

Prediction and Characterization of NaGaS2, A High Thermal Conductivity Mid-Infrared Nonlinear Optical Material for High-Power Laser Frequency Conversion.

PubMed

Hou, Dianwei; Nissimagoudar, Arun S; Bian, Qiang; Wu, Kui; Pan, Shilie; Li, Wu; Yang, Zhihua

2018-06-15

Infrared nonlinear optical (IR NLO) crystals are the major materials to widen the output range of solid-state lasers to mid- or far-infrared regions. The IR NLO crystals used in the middle IR region are still inadequate for high-power laser applications because of deleterious thermal effects (lensing and expansion), low laser-induced damage threshold, and two-photon absorption. Herein, the unbiased global minimum search method was used for the first time to search for IR NLO optical materials and ultimately found a new IR NLO material NaGaS 2 . It meets the stringent demands for IR NLO materials pumped by high-power laser with the highest thermal conductivity among common IR NLO materials able to avoid two-photon absorption, a classic nonlinear coefficient, and wide infrared transparency.

Path Integral Metadynamics.

PubMed

Quhe, Ruge; Nava, Marco; Tiwary, Pratyush; Parrinello, Michele

2015-04-14

We develop a new efficient approach for the simulation of static properties of quantum systems using path integral molecular dynamics in combination with metadynamics. We use the isomorphism between a quantum system and a classical one in which a quantum particle is mapped into a ring polymer. A history dependent biasing potential is built as a function of the elastic energy of the isomorphic polymer. This enhances fluctuations in the shape and size of the necklace in a controllable manner and allows escaping deep energy minima in a limited computer time. In this way, we are able to sample high free energy regions and cross barriers, which would otherwise be insurmountable with unbiased methods. This substantially improves the ability of finding the global free energy minimum as well as exploring other metastable states. The performance of the new technique is demonstrated by illustrative applications on model potentials of varying complexity.

Nonlinear vs. linear biasing in Trp-cage folding simulations

NASA Astrophysics Data System (ADS)

Spiwok, Vojtěch; Oborský, Pavel; Pazúriková, Jana; Křenek, Aleš; Králová, Blanka

2015-03-01

Biased simulations have great potential for the study of slow processes, including protein folding. Atomic motions in molecules are nonlinear, which suggests that simulations with enhanced sampling of collective motions traced by nonlinear dimensionality reduction methods may perform better than linear ones. In this study, we compare an unbiased folding simulation of the Trp-cage miniprotein with metadynamics simulations using both linear (principle component analysis) and nonlinear (Isomap) low dimensional embeddings as collective variables. Folding of the mini-protein was successfully simulated in 200 ns simulation with linear biasing and non-linear motion biasing. The folded state was correctly predicted as the free energy minimum in both simulations. We found that the advantage of linear motion biasing is that it can sample a larger conformational space, whereas the advantage of nonlinear motion biasing lies in slightly better resolution of the resulting free energy surface. In terms of sampling efficiency, both methods are comparable.

Nonlinear vs. linear biasing in Trp-cage folding simulations.

PubMed

Spiwok, Vojtěch; Oborský, Pavel; Pazúriková, Jana; Křenek, Aleš; Králová, Blanka

2015-03-21

Biased simulations have great potential for the study of slow processes, including protein folding. Atomic motions in molecules are nonlinear, which suggests that simulations with enhanced sampling of collective motions traced by nonlinear dimensionality reduction methods may perform better than linear ones. In this study, we compare an unbiased folding simulation of the Trp-cage miniprotein with metadynamics simulations using both linear (principle component analysis) and nonlinear (Isomap) low dimensional embeddings as collective variables. Folding of the mini-protein was successfully simulated in 200 ns simulation with linear biasing and non-linear motion biasing. The folded state was correctly predicted as the free energy minimum in both simulations. We found that the advantage of linear motion biasing is that it can sample a larger conformational space, whereas the advantage of nonlinear motion biasing lies in slightly better resolution of the resulting free energy surface. In terms of sampling efficiency, both methods are comparable.

Solar-cycle dependence of a model turbulence spectrum using IMP and ACE observations over 38 years

NASA Astrophysics Data System (ADS)

Burger, R. A.; Nel, A. E.; Engelbrecht, N. E.

2014-12-01

Ab initio modulation models require a number of turbulence quantities as input for any reasonable diffusion tensor. While turbulence transport models describe the radial evolution of such quantities, they in turn require observations in the inner heliosphere as input values. So far we have concentrated on solar minimum conditions (e.g. Engelbrecht and Burger 2013, ApJ), but are now looking at long-term modulation which requires turbulence data over at a least a solar magnetic cycle. As a start we analyzed 1-minute resolution data for the N-component of the magnetic field, from 1974 to 2012, covering about two solar magnetic cycles (initially using IMP and then ACE data). We assume a very simple three-stage power-law frequency spectrum, calculate the integral from the highest to the lowest frequency, and fit it to variances calculated with lags from 5 minutes to 80 hours. From the fit we then obtain not only the asymptotic variance at large lags, but also the spectral index of the inertial and the energy, as well as the breakpoint between the inertial and energy range (bendover scale) and between the energy and cutoff range (cutoff scale). All values given here are preliminary. The cutoff range is a constraint imposed in order to ensure a finite energy density; the spectrum is forced to be either flat or to decrease with decreasing frequency in this range. Given that cosmic rays sample magnetic fluctuations over long periods in their transport through the heliosphere, we average the spectra over at least 27 days. We find that the variance of the N-component has a clear solar cycle dependence, with smaller values (~6 nT2) during solar minimum and larger during solar maximum periods (~17 nT2), well correlated with the magnetic field magnitude (e.g. Smith et al. 2006, ApJ). Whereas the inertial range spectral index (-1.65 ± 0.06) does not show a significant solar cycle variation, the energy range index (-1.1 ± 0.3) seems to be anti-correlated with the variance (Bieber et al. 1993, JGR); both indices show close to normal distributions. In contrast, the variance (e.g. Burlaga and Ness, 1998, JGR), and both the bendover scale (see Ruiz et al. 2014, Solar Physics) and cutoff scale appear to be log-normal distributed.

A new Method for Determining the Interplanetary Current-Sheet Local Orientation

NASA Astrophysics Data System (ADS)

Blanco, J. J.; Rodríguez-pacheco, J.; Sequeiros, J.

2003-03-01

In this work we have developed a new method for determining the interplanetary current sheet local parameters. The method, called `HYTARO' (from Hyperbolic Tangent Rotation), is based on a modified Harris magnetic field. This method has been applied to a pool of 57 events, all of them recorded during solar minimum conditions. The model performance has been tested by comparing both, its outputs and noise response, with these of the `classic MVM' (from Minimum Variance Method). The results suggest that, despite the fact that in many cases they behave in a similar way, there are specific crossing conditions that produce an erroneous MVM response. Moreover, our method shows a lower noise level sensitivity than that of MVM.

Determining Metacarpophalangeal Flexion Angle Tolerance for Reliable Volumetric Joint Space Measurements by High-resolution Peripheral Quantitative Computed Tomography.

PubMed

Tom, Stephanie; Frayne, Mark; Manske, Sarah L; Burghardt, Andrew J; Stok, Kathryn S; Boyd, Steven K; Barnabe, Cheryl

2016-10-01

The position-dependence of a method to measure the joint space of metacarpophalangeal (MCP) joints using high-resolution peripheral quantitative computed tomography (HR-pQCT) was studied. Cadaveric MCP were imaged at 7 flexion angles between 0 and 30 degrees. The variability in reproducibility for mean, minimum, and maximum joint space widths and volume measurements was calculated for increasing degrees of flexion. Root mean square coefficient of variance values were < 5% under 20 degrees of flexion for mean, maximum, and volumetric joint spaces. Values for minimum joint space width were optimized under 10 degrees of flexion. MCP joint space measurements should be acquired at < 10 degrees of flexion in longitudinal studies.

Comparison of reproducibility of natural head position using two methods.

PubMed

Khan, Abdul Rahim; Rajesh, R N G; Dinesh, M R; Sanjay, N; Girish, K S; Venkataraghavan, Karthik

2012-01-01

Lateral cephalometric radiographs have become virtually indispensable to orthodontists in the treatment of patients. They are important in orthodontic growth analysis, diagnosis, treatment planning, monitoring of therapy and evaluation of final treatment outcome. The purpose of this study was to evaluate and compare the maximum reproducibility with minimum variation of natural head position using two methods, i.e. the mirror method and the fluid level device method. The study included two sets of 40 lateral cephalograms taken using two methods of obtaining natural head position: (1) The mirror method and (2) fluid level device method, with a time interval of 2 months. Inclusion criteria • Subjects were randomly selected aged between 18 to 26 years Exclusion criteria • History of orthodontic treatment • Any history of respiratory tract problem or chronic mouth breathing • Any congenital deformity • History of traumatically-induced deformity • History of myofacial pain syndrome • Any previous history of head and neck surgery. The result showed that both the methods for obtaining natural head position-the mirror method and fluid level device method were comparable, but maximum reproducibility was more with the fluid level device as shown by the Dahlberg's coefficient and Bland-Altman plot. The minimum variance was seen with the fluid level device method as shown by Precision and Pearson correlation. The mirror method and the fluid level device method used for obtaining natural head position were comparable without any significance, and the fluid level device method was more reproducible and showed less variance when compared to mirror method for obtaining natural head position. Fluid level device method was more reproducible and shows less variance when compared to mirror method for obtaining natural head position.

A two-step sensitivity analysis for hydrological signatures in Jinhua River Basin, East China

NASA Astrophysics Data System (ADS)

Pan, S.; Fu, G.; Chiang, Y. M.; Xu, Y. P.

2016-12-01

Owing to model complexity and large number of parameters, calibration and sensitivity analysis are difficult processes for distributed hydrological models. In this study, a two-step sensitivity analysis approach is proposed for analyzing the hydrological signatures in Jinhua River Basin, East China, using the Distributed Hydrology-Soil-Vegetation Model (DHSVM). A rough sensitivity analysis is firstly conducted to obtain preliminary influential parameters via Analysis of Variance. The number of parameters was greatly reduced from eighteen-three to sixteen. Afterwards, the sixteen parameters are further analyzed based on a variance-based global sensitivity analysis, i.e., Sobol's sensitivity analysis method, to achieve robust sensitivity rankings and parameter contributions. Parallel-Computing is applied to reduce computational burden in variance-based sensitivity analysis. The results reveal that only a few number of model parameters are significantly sensitive, including rain LAI multiplier, lateral conductivity, porosity, field capacity, wilting point of clay loam, understory monthly LAI, understory minimum resistance and root zone depths of croplands. Finally several hydrological signatures are used for investigating the performance of DHSVM. Results show that high value of efficiency criteria didn't indicate excellent performance of hydrological signatures. For most samples from Sobol's sensitivity analysis, water yield was simulated very well. However, lowest and maximum annual daily runoffs were underestimated. Most of seven-day minimum runoffs were overestimated. Nevertheless, good performances of the three signatures above still exist in a number of samples. Analysis of peak flow shows that small and medium floods are simulated perfectly while slight underestimations happen to large floods. The work in this study helps to further multi-objective calibration of DHSVM model and indicates where to improve the reliability and credibility of model simulation.

Dynamic and Geometric Analyses of Nudaurelia capensis ωVirus Maturation Reveal the Energy Landscape of Particle Transitions

PubMed Central

Tang, Jinghua; Kearney, Bradley M.; Wang, Qiu; Doerschuk, Peter C.; Baker, Timothy S.; Johnson, John E.

2014-01-01

Quasi-equivalent viruses that infect animals and bacteria require a maturation process in which particles transition from initially assembled procapsids to infectious virions. Nudaurelia capensis ω virus (NωV) is a T=4, eukaryotic, ssRNA virus that has proved to be an excellent model system for studying the mechanisms of viral maturation. Structures of NωV procapsids (diam. = 480 Å), a maturation intermediate (410 Å), and the mature virion (410 Å) were determined by electron cryo-microscopy and three-dimensional image reconstruction (cryoEM). The cryoEM density for each particle type was analyzed with a recently developed Maximum Likelihood Variance (MLV) method for characterizing microstates occupied in the ensemble of particles used for the reconstructions. The procapsid and the mature capsid had overall low variance (i.e. uniform particle populations) while the maturation intermediate (that had not undergone post-assembly autocatalytic cleavage) had roughly 2-4 times the variance of the first two particles. Without maturation cleavage the particles assume a variety of microstates, as the frustrated subunits cannot reach a minimum energy configuration. Geometric analyses of subunit coordinates provided a quantitative description of the particle reorganization during maturation. Superposition of the four quasi-equivalent subunits in the procapsid had an average root mean square deviation (RMSD) of 3Å while the mature particle had an RMSD of 11Å, showing that the subunits differentiate from near equivalent environments in the procapsid to strikingly non-equivalent environments during maturation. Autocatalytic cleavage is clearly required for the reorganized mature particle to reach the minimum energy state required for stability and infectivity. PMID:24591180

Dynamic and geometric analyses of Nudaurelia capensis ω virus maturation reveal the energy landscape of particle transitions.

PubMed

Tang, Jinghua; Kearney, Bradley M; Wang, Qiu; Doerschuk, Peter C; Baker, Timothy S; Johnson, John E

2014-04-01

Quasi-equivalent viruses that infect animals and bacteria require a maturation process in which particles transition from initially assembled procapsids to infectious virions. Nudaurelia capensis ω virus (NωV) is a T = 4, eukaryotic, single-stranded ribonucleic acid virus that has proved to be an excellent model system for studying the mechanisms of viral maturation. Structures of NωV procapsids (diameter = 480 Å), a maturation intermediate (410 Å), and the mature virion (410 Å) were determined by electron cryo-microscopy and three-dimensional image reconstruction (cryoEM). The cryoEM density for each particle type was analyzed with a recently developed maximum likelihood variance (MLV) method for characterizing microstates occupied in the ensemble of particles used for the reconstructions. The procapsid and the mature capsid had overall low variance (i.e., uniform particle populations) while the maturation intermediate (that had not undergone post-assembly autocatalytic cleavage) had roughly two to four times the variance of the first two particles. Without maturation cleavage, the particles assume a variety of microstates, as the frustrated subunits cannot reach a minimum energy configuration. Geometric analyses of subunit coordinates provided a quantitative description of the particle reorganization during maturation. Superposition of the four quasi-equivalent subunits in the procapsid had an average root mean square deviation (RMSD) of 3 Å while the mature particle had an RMSD of 11 Å, showing that the subunits differentiate from near equivalent environments in the procapsid to strikingly non-equivalent environments during maturation. Autocatalytic cleavage is clearly required for the reorganized mature particle to reach the minimum energy state required for stability and infectivity. Copyright © 2014 John Wiley & Sons, Ltd.

Comparative modeling and benchmarking data sets for human histone deacetylases and sirtuin families.

PubMed

Xia, Jie; Tilahun, Ermias Lemma; Kebede, Eyob Hailu; Reid, Terry-Elinor; Zhang, Liangren; Wang, Xiang Simon

2015-02-23

Histone deacetylases (HDACs) are an important class of drug targets for the treatment of cancers, neurodegenerative diseases, and other types of diseases. Virtual screening (VS) has become fairly effective approaches for drug discovery of novel and highly selective histone deacetylase inhibitors (HDACIs). To facilitate the process, we constructed maximal unbiased benchmarking data sets for HDACs (MUBD-HDACs) using our recently published methods that were originally developed for building unbiased benchmarking sets for ligand-based virtual screening (LBVS). The MUBD-HDACs cover all four classes including Class III (Sirtuins family) and 14 HDAC isoforms, composed of 631 inhibitors and 24609 unbiased decoys. Its ligand sets have been validated extensively as chemically diverse, while the decoy sets were shown to be property-matching with ligands and maximal unbiased in terms of "artificial enrichment" and "analogue bias". We also conducted comparative studies with DUD-E and DEKOIS 2.0 sets against HDAC2 and HDAC8 targets and demonstrate that our MUBD-HDACs are unique in that they can be applied unbiasedly to both LBVS and SBVS approaches. In addition, we defined a novel metric, i.e. NLBScore, to detect the "2D bias" and "LBVS favorable" effect within the benchmarking sets. In summary, MUBD-HDACs are the only comprehensive and maximal-unbiased benchmark data sets for HDACs (including Sirtuins) that are available so far. MUBD-HDACs are freely available at http://www.xswlab.org/ .

Life-history traits and effective population size in species with overlapping generations revisited: the importance of adult mortality.

PubMed

Waples, R S

2016-10-01

The relationship between life-history traits and the key eco-evolutionary parameters effective population size (Ne) and Ne/N is revisited for iteroparous species with overlapping generations, with a focus on the annual rate of adult mortality (d). Analytical methods based on populations with arbitrarily long adult lifespans are used to evaluate the influence of d on Ne, Ne/N and the factors that determine these parameters: adult abundance (N), generation length (T), age at maturity (α), the ratio of variance to mean reproductive success in one season by individuals of the same age (φ) and lifetime variance in reproductive success of individuals in a cohort (Vk•). Although the resulting estimators of N, T and Vk• are upwardly biased for species with short adult lifespans, the estimate of Ne/N is largely unbiased because biases in T are compensated for by biases in Vk• and N. For the first time, the contrasting effects of T and Vk• on Ne and Ne/N are jointly considered with respect to d and φ. A simple function of d and α based on the assumption of constant vital rates is shown to be a robust predictor (R(2)=0.78) of Ne/N in an empirical data set of life tables for 63 animal and plant species with diverse life histories. Results presented here should provide important context for interpreting the surge of genetically based estimates of Ne that has been fueled by the genomics revolution.

CMB EB and TB cross-spectrum estimation via pseudospectrum techniques

NASA Astrophysics Data System (ADS)

Grain, J.; Tristram, M.; Stompor, R.

2012-10-01

We discuss methods for estimating EB and TB spectra of the cosmic microwave background anisotropy maps covering limited sky area. Such odd-parity correlations are expected to vanish whenever parity is not broken. As this is indeed the case in the standard cosmologies, any evidence to the contrary would have a profound impact on our theories of the early Universe. Such correlations could also become a sensitive diagnostic of some particularly insidious instrumental systematics. In this work we introduce three different unbiased estimators based on the so-called standard and pure pseudo-spectrum techniques and later assess their performance by means of extensive Monte Carlo simulations performed for different experimental configurations. We find that a hybrid approach combining a pure estimate of B-mode multipoles with a standard one for E-mode (or T) multipoles, leads to the smallest error bars for both EB (or TB respectively) spectra as well as for the three other polarization-related angular power spectra (i.e., EE, BB, and TE). However, if both E and B multipoles are estimated using the pure technique, the loss of precision for the EB spectrum is not larger than ˜30%. Moreover, for the experimental configurations considered here, the statistical uncertainties-due to sampling variance and instrumental noise-of the pseudo-spectrum estimates is at most a factor ˜1.4 for TT, EE, and TE spectra and a factor ˜2 for BB, TB, and EB spectra, higher than the most optimistic Fisher estimate of the variance.

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

PubMed

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

2015-02-20

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

Conformational Smear Characterization and Binning of Single-Molecule Conductance Measurements for Enhanced Molecular Recognition.

PubMed

Korshoj, Lee E; Afsari, Sepideh; Chatterjee, Anushree; Nagpal, Prashant

2017-11-01

Electronic conduction or charge transport through single molecules depends primarily on molecular structure and anchoring groups and forms the basis for a wide range of studies from molecular electronics to DNA sequencing. Several high-throughput nanoelectronic methods such as mechanical break junctions, nanopores, conductive atomic force microscopy, scanning tunneling break junctions, and static nanoscale electrodes are often used for measuring single-molecule conductance. In these measurements, "smearing" due to conformational changes and other entropic factors leads to large variances in the observed molecular conductance, especially in individual measurements. Here, we show a method for characterizing smear in single-molecule conductance measurements and demonstrate how binning measurements according to smear can significantly enhance the use of individual conductance measurements for molecular recognition. Using quantum point contact measurements on single nucleotides within DNA macromolecules, we demonstrate that the distance over which molecular junctions are maintained is a measure of smear, and the resulting variance in unbiased single measurements depends on this smear parameter. Our ability to identify individual DNA nucleotides at 20× coverage increases from 81.3% accuracy without smear analysis to 93.9% with smear characterization and binning (SCRIB). Furthermore, merely 7 conductance measurements (7× coverage) are needed to achieve 97.8% accuracy for DNA nucleotide recognition when only low molecular smear measurements are used, which represents a significant improvement over contemporary sequencing methods. These results have important implications in a broad range of molecular electronics applications from designing robust molecular switches to nanoelectronic DNA sequencing.

A Bifactor Approach to Model Multifaceted Constructs in Statistical Mediation Analysis.

PubMed

Gonzalez, Oscar; MacKinnon, David P

Statistical mediation analysis allows researchers to identify the most important mediating constructs in the causal process studied. Identifying specific mediators is especially relevant when the hypothesized mediating construct consists of multiple related facets. The general definition of the construct and its facets might relate differently to an outcome. However, current methods do not allow researchers to study the relationships between general and specific aspects of a construct to an outcome simultaneously. This study proposes a bifactor measurement model for the mediating construct as a way to parse variance and represent the general aspect and specific facets of a construct simultaneously. Monte Carlo simulation results are presented to help determine the properties of mediated effect estimation when the mediator has a bifactor structure and a specific facet of a construct is the true mediator. This study also investigates the conditions when researchers can detect the mediated effect when the multidimensionality of the mediator is ignored and treated as unidimensional. Simulation results indicated that the mediation model with a bifactor mediator measurement model had unbiased and adequate power to detect the mediated effect with a sample size greater than 500 and medium a - and b -paths. Also, results indicate that parameter bias and detection of the mediated effect in both the data-generating model and the misspecified model varies as a function of the amount of facet variance represented in the mediation model. This study contributes to the largely unexplored area of measurement issues in statistical mediation analysis.

An Experience Oriented-Convergence Improved Gravitational Search Algorithm for Minimum Variance Distortionless Response Beamforming Optimum

PubMed Central

Darzi, Soodabeh; Tiong, Sieh Kiong; Tariqul Islam, Mohammad; Rezai Soleymanpour, Hassan; Kibria, Salehin

2016-01-01

An experience oriented-convergence improved gravitational search algorithm (ECGSA) based on two new modifications, searching through the best experiments and using of a dynamic gravitational damping coefficient (α), is introduced in this paper. ECGSA saves its best fitness function evaluations and uses those as the agents’ positions in searching process. In this way, the optimal found trajectories are retained and the search starts from these trajectories, which allow the algorithm to avoid the local optimums. Also, the agents can move faster in search space to obtain better exploration during the first stage of the searching process and they can converge rapidly to the optimal solution at the final stage of the search process by means of the proposed dynamic gravitational damping coefficient. The performance of ECGSA has been evaluated by applying it to eight standard benchmark functions along with six complicated composite test functions. It is also applied to adaptive beamforming problem as a practical issue to improve the weight vectors computed by minimum variance distortionless response (MVDR) beamforming technique. The results of implementation of the proposed algorithm are compared with some well-known heuristic methods and verified the proposed method in both reaching to optimal solutions and robustness. PMID:27399904

A New Look at Some Solar Wind Turbulence Puzzles

NASA Technical Reports Server (NTRS)

Roberts, Aaron

2006-01-01

Some aspects of solar wind turbulence have defied explanation. While it seems likely that the evolution of Alfvenicity and power spectra are largely explained by the shearing of an initial population of solar-generated Alfvenic fluctuations, the evolution of the anisotropies of the turbulence does not fit into the model so far. A two-component model, consisting of slab waves and quasi-two-dimensional fluctuations, offers some ideas, but does not account for the turning of both wave-vector-space power anisotropies and minimum variance directions in the fluctuating vectors as the Parker spiral turns. We will show observations that indicate that the minimum variance evolution is likely not due to traditional turbulence mechanisms, and offer arguments that the idea of two-component turbulence is at best a local approximation that is of little help in explaining the evolution of the fluctuations. Finally, time-permitting, we will discuss some observations that suggest that the low Alfvenicity of many regions of the solar wind in the inner heliosphere is not due to turbulent evolution, but rather to the existence of convected structures, including mini-clouds and other twisted flux tubes, that were formed with low Alfvenicity. There is still a role for turbulence in the above picture, but it is highly modified from the traditional views.

Hierarchical thinking in network biology: the unbiased modularization of biochemical networks.

PubMed

Papin, Jason A; Reed, Jennifer L; Palsson, Bernhard O

2004-12-01

As reconstructed biochemical reaction networks continue to grow in size and scope, there is a growing need to describe the functional modules within them. Such modules facilitate the study of biological processes by deconstructing complex biological networks into conceptually simple entities. The definition of network modules is often based on intuitive reasoning. As an alternative, methods are being developed for defining biochemical network modules in an unbiased fashion. These unbiased network modules are mathematically derived from the structure of the whole network under consideration.

Stream-temperature patterns of the Muddy Creek basin, Anne Arundel County, Maryland

USGS Publications Warehouse

Pluhowski, E.J.

1981-01-01

Using a water-balance equation based on a 4.25-year gaging-station record on North Fork Muddy Creek, the following mean annual values were obtained for the Muddy Creek basin: precipitation, 49.0 inches; evapotranspiration, 28.0 inches; runoff, 18.5 inches; and underflow, 2.5 inches. Average freshwater outflow from the Muddy Creek basin to the Rhode River estuary was 12.2 cfs during the period October 1, 1971, to December 31, 1975. Harmonic equations were used to describe seasonal maximum and minimum stream-temperature patterns at 12 sites in the basin. These equations were fitted to continuous water-temperature data obtained periodically at each site between November 1970 and June 1978. The harmonic equations explain at least 78 percent of the variance in maximum stream temperatures and 81 percent of the variance in minimum temperatures. Standard errors of estimate averaged 2.3C (Celsius) for daily maximum water temperatures and 2.1C for daily minimum temperatures. Mean annual water temperatures developed for a 5.4-year base period ranged from 11.9C at Muddy Creek to 13.1C at Many Fork Branch. The largest variations in stream temperatures were detected at thermograph sites below ponded reaches and where forest coverage was sparse or missing. At most sites the largest variations in daily water temperatures were recorded in April whereas the smallest were in September and October. The low thermal inertia of streams in the Muddy Creek basin tends to amplify the impact of surface energy-exchange processes on short-period stream-temperature patterns. Thus, in response to meteorologic events, wide ranging stream-temperature perturbations of as much as 6C have been documented in the basin. (USGS)

Impact of fitting dominance and additive effects on accuracy of genomic prediction of breeding values in layers.

PubMed

Heidaritabar, M; Wolc, A; Arango, J; Zeng, J; Settar, P; Fulton, J E; O'Sullivan, N P; Bastiaansen, J W M; Fernando, R L; Garrick, D J; Dekkers, J C M

2016-10-01

Most genomic prediction studies fit only additive effects in models to estimate genomic breeding values (GEBV). However, if dominance genetic effects are an important source of variation for complex traits, accounting for them may improve the accuracy of GEBV. We investigated the effect of fitting dominance and additive effects on the accuracy of GEBV for eight egg production and quality traits in a purebred line of brown layers using pedigree or genomic information (42K single-nucleotide polymorphism (SNP) panel). Phenotypes were corrected for the effect of hatch date. Additive and dominance genetic variances were estimated using genomic-based [genomic best linear unbiased prediction (GBLUP)-REML and BayesC] and pedigree-based (PBLUP-REML) methods. Breeding values were predicted using a model that included both additive and dominance effects and a model that included only additive effects. The reference population consisted of approximately 1800 animals hatched between 2004 and 2009, while approximately 300 young animals hatched in 2010 were used for validation. Accuracy of prediction was computed as the correlation between phenotypes and estimated breeding values of the validation animals divided by the square root of the estimate of heritability in the whole population. The proportion of dominance variance to total phenotypic variance ranged from 0.03 to 0.22 with PBLUP-REML across traits, from 0 to 0.03 with GBLUP-REML and from 0.01 to 0.05 with BayesC. Accuracies of GEBV ranged from 0.28 to 0.60 across traits. Inclusion of dominance effects did not improve the accuracy of GEBV, and differences in their accuracies between genomic-based methods were small (0.01-0.05), with GBLUP-REML yielding higher prediction accuracies than BayesC for egg production, egg colour and yolk weight, while BayesC yielded higher accuracies than GBLUP-REML for the other traits. In conclusion, fitting dominance effects did not impact accuracy of genomic prediction of breeding values in this population. © 2016 Blackwell Verlag GmbH.

Psychopathology in 7-year-old children: Differences in maternal and paternal ratings and the genetic epidemiology.

PubMed

Wesseldijk, Laura W; Fedko, Iryna O; Bartels, Meike; Nivard, Michel G; van Beijsterveldt, Catharina E M; Boomsma, Dorret I; Middeldorp, Christel M

2017-04-01

The assessment of children's psychopathology is often based on parental report. Earlier studies have suggested that rater bias can affect the estimates of genetic, shared environmental and unique environmental influences on differences between children. The availability of a large dataset of maternal as well as paternal ratings of psychopathology in 7-year old children enabled (i) the analysis of informant effects on these assessments, and (ii) to obtain more reliable estimates of the genetic and non-genetic effects. DSM-oriented measures of affective, anxiety, somatic, attention-deficit/hyperactivity, oppositional-defiant, conduct, and obsessive-compulsive problems were rated for 12,310 twin pairs from the Netherlands Twin Register by mothers (N = 12,085) and fathers (N = 8,516). The effects of genetic and non-genetic effects were estimated on the common and rater-specific variance. For all scales, mean scores on maternal ratings exceeded paternal ratings. Parents largely agreed on the ranking of their child's problems (r 0.60-0.75). The heritability was estimated over 55% for maternal and paternal ratings for all scales, except for conduct problems (44-46%). Unbiased shared environmental influences, i.e., on the common variance, were significant for affective (13%), oppositional (13%), and conduct problems (37%). In clinical settings, different cutoffs for (sub)clinical scores could be applied to paternal and maternal ratings of their child's psychopathology. Only for conduct problems, shared environmental and genetic influences explain an equal amount in differences between children. For the other scales, genetic factors explain the majority of the variance, especially for the common part that is free of rater bias. © 2016 The Authors. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics Published by Wiley Periodicals, Inc. © 2016 The Authors. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics Published by Wiley Periodicals, Inc.

Influential input classification in probabilistic multimedia models

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

Maddalena, Randy L.; McKone, Thomas E.; Hsieh, Dennis P.H.

1999-05-01

Monte Carlo analysis is a statistical simulation method that is often used to assess and quantify the outcome variance in complex environmental fate and effects models. Total outcome variance of these models is a function of (1) the uncertainty and/or variability associated with each model input and (2) the sensitivity of the model outcome to changes in the inputs. To propagate variance through a model using Monte Carlo techniques, each variable must be assigned a probability distribution. The validity of these distributions directly influences the accuracy and reliability of the model outcome. To efficiently allocate resources for constructing distributions onemore » should first identify the most influential set of variables in the model. Although existing sensitivity and uncertainty analysis methods can provide a relative ranking of the importance of model inputs, they fail to identify the minimum set of stochastic inputs necessary to sufficiently characterize the outcome variance. In this paper, we describe and demonstrate a novel sensitivity/uncertainty analysis method for assessing the importance of each variable in a multimedia environmental fate model. Our analyses show that for a given scenario, a relatively small number of input variables influence the central tendency of the model and an even smaller set determines the shape of the outcome distribution. For each input, the level of influence depends on the scenario under consideration. This information is useful for developing site specific models and improving our understanding of the processes that have the greatest influence on the variance in outcomes from multimedia models.« less

Comparative soil CO2 flux measurements and geostatistical estimation methods on Masaya volcano, Nicaragua

USGS Publications Warehouse

Lewicki, Jennifer L.; Bergfeld, Deborah; Cardellini, Carlo; Chiodini, Giovanni; Granieri, Domenico; Varley, Nick; Werner, Cynthia A.

2005-01-01

We present a comparative study of soil CO2 flux (FCO2">FCO2) measured by five groups (Groups 1–5) at the IAVCEI-CCVG Eighth Workshop on Volcanic Gases on Masaya volcano, Nicaragua. Groups 1–5 measured FCO2 using the accumulation chamber method at 5-m spacing within a 900 m2 grid during a morning (AM) period. These measurements were repeated by Groups 1–3 during an afternoon (PM) period. Measured FCO2 ranged from 218 to 14,719 g m−2 day−1. The variability of the five measurements made at each grid point ranged from ±5 to 167%. However, the arithmetic means of fluxes measured over the entire grid and associated total CO2 emission rate estimates varied between groups by only ±22%. All three groups that made PM measurements reported an 8–19% increase in total emissions over the AM results. Based on a comparison of measurements made during AM and PM times, we argue that this change is due in large part to natural temporal variability of gas flow, rather than to measurement error. In order to estimate the mean and associated CO2 emission rate of one data set and to map the spatial FCO2 distribution, we compared six geostatistical methods: arithmetic and minimum variance unbiased estimator means of uninterpolated data, and arithmetic means of data interpolated by the multiquadric radial basis function, ordinary kriging, multi-Gaussian kriging, and sequential Gaussian simulation methods. While the total CO2 emission rates estimated using the different techniques only varied by ±4.4%, the FCO2 maps showed important differences. We suggest that the sequential Gaussian simulation method yields the most realistic representation of the spatial distribution of FCO2, but a variety of geostatistical methods are appropriate to estimate the total CO2 emission rate from a study area, which is a primary goal in volcano monitoring research.

Spatial Statistical Data Fusion (SSDF)

NASA Technical Reports Server (NTRS)

Braverman, Amy J.; Nguyen, Hai M.; Cressie, Noel

2013-01-01

As remote sensing for scientific purposes has transitioned from an experimental technology to an operational one, the selection of instruments has become more coordinated, so that the scientific community can exploit complementary measurements. However, tech nological and scientific heterogeneity across devices means that the statistical characteristics of the data they collect are different. The challenge addressed here is how to combine heterogeneous remote sensing data sets in a way that yields optimal statistical estimates of the underlying geophysical field, and provides rigorous uncertainty measures for those estimates. Different remote sensing data sets may have different spatial resolutions, different measurement error biases and variances, and other disparate characteristics. A state-of-the-art spatial statistical model was used to relate the true, but not directly observed, geophysical field to noisy, spatial aggregates observed by remote sensing instruments. The spatial covariances of the true field and the covariances of the true field with the observations were modeled. The observations are spatial averages of the true field values, over pixels, with different measurement noise superimposed. A kriging framework is used to infer optimal (minimum mean squared error and unbiased) estimates of the true field at point locations from pixel-level, noisy observations. A key feature of the spatial statistical model is the spatial mixed effects model that underlies it. The approach models the spatial covariance function of the underlying field using linear combinations of basis functions of fixed size. Approaches based on kriging require the inversion of very large spatial covariance matrices, and this is usually done by making simplifying assumptions about spatial covariance structure that simply do not hold for geophysical variables. In contrast, this method does not require these assumptions, and is also computationally much faster. This method is fundamentally different than other approaches to data fusion for remote sensing data because it is inferential rather than merely descriptive. All approaches combine data in a way that minimizes some specified loss function. Most of these are more or less ad hoc criteria based on what looks good to the eye, or some criteria that relate only to the data at hand.

MSEBAG: a dynamic classifier ensemble generation based on `minimum-sufficient ensemble' and bagging

NASA Astrophysics Data System (ADS)

Chen, Lei; Kamel, Mohamed S.

2016-01-01

In this paper, we propose a dynamic classifier system, MSEBAG, which is characterised by searching for the 'minimum-sufficient ensemble' and bagging at the ensemble level. It adopts an 'over-generation and selection' strategy and aims to achieve a good bias-variance trade-off. In the training phase, MSEBAG first searches for the 'minimum-sufficient ensemble', which maximises the in-sample fitness with the minimal number of base classifiers. Then, starting from the 'minimum-sufficient ensemble', a backward stepwise algorithm is employed to generate a collection of ensembles. The objective is to create a collection of ensembles with a descending fitness on the data, as well as a descending complexity in the structure. MSEBAG dynamically selects the ensembles from the collection for the decision aggregation. The extended adaptive aggregation (EAA) approach, a bagging-style algorithm performed at the ensemble level, is employed for this task. EAA searches for the competent ensembles using a score function, which takes into consideration both the in-sample fitness and the confidence of the statistical inference, and averages the decisions of the selected ensembles to label the test pattern. The experimental results show that the proposed MSEBAG outperforms the benchmarks on average.

Comparative Modeling and Benchmarking Data Sets for Human Histone Deacetylases and Sirtuin Families

PubMed Central

Xia, Jie; Tilahun, Ermias Lemma; Kebede, Eyob Hailu; Reid, Terry-Elinor; Zhang, Liangren; Wang, Xiang Simon

2015-01-01

Histone Deacetylases (HDACs) are an important class of drug targets for the treatment of cancers, neurodegenerative diseases and other types of diseases. Virtual screening (VS) has become fairly effective approaches for drug discovery of novel and highly selective Histone Deacetylases Inhibitors (HDACIs). To facilitate the process, we constructed the Maximal Unbiased Benchmarking Data Sets for HDACs (MUBD-HDACs) using our recently published methods that were originally developed for building unbiased benchmarking sets for ligand-based virtual screening (LBVS). The MUBD-HDACs covers all 4 Classes including Class III (Sirtuins family) and 14 HDACs isoforms, composed of 631 inhibitors and 24,609 unbiased decoys. Its ligand sets have been validated extensively as chemically diverse, while the decoy sets were shown to be property-matching with ligands and maximal unbiased in terms of “artificial enrichment” and “analogue bias”. We also conducted comparative studies with DUD-E and DEKOIS 2.0 sets against HDAC2 and HDAC8 targets, and demonstrate that our MUBD-HDACs is unique in that it can be applied unbiasedly to both LBVS and SBVS approaches. In addition, we defined a novel metric, i.e. NLBScore, to detect the “2D bias” and “LBVS favorable” effect within the benchmarking sets. In summary, MUBD-HDACs is the only comprehensive and maximal-unbiased benchmark data sets for HDACs (including Sirtuins) that is available so far. MUBD-HDACs is freely available at http://www.xswlab.org/. PMID:25633490

Response to selection while maximizing genetic variance in small populations.

PubMed

Cervantes, Isabel; Gutiérrez, Juan Pablo; Meuwissen, Theo H E

2016-09-20

Rare breeds represent a valuable resource for future market demands. These populations are usually well-adapted, but their low census compromises the genetic diversity and future of these breeds. Since improvement of a breed for commercial traits may also confer higher probabilities of survival for the breed, it is important to achieve good responses to artificial selection. Therefore, efficient genetic management of these populations is essential to ensure that they respond adequately to genetic selection in possible future artificial selection scenarios. Scenarios that maximize the maximum genetic variance in a unique population could be a valuable option. The aim of this work was to study the effect of the maximization of genetic variance to increase selection response and improve the capacity of a population to adapt to a new environment/production system. We simulated a random scenario (A), a full-sib scenario (B), a scenario applying the maximum variance total (MVT) method (C), a MVT scenario with a restriction on increases in average inbreeding (D), a MVT scenario with a restriction on average individual increases in inbreeding (E), and a minimum coancestry scenario (F). Twenty replicates of each scenario were simulated for 100 generations, followed by 10 generations of selection. Effective population size was used to monitor the outcomes of these scenarios. Although the best response to selection was achieved in scenarios B and C, they were discarded because they are unpractical. Scenario A was also discarded because of its low response to selection. Scenario D yielded less response to selection and a smaller effective population size than scenario E, for which response to selection was higher during early generations because of the moderately structured population. In scenario F, response to selection was slightly higher than in Scenario E in the last generations. Application of MVT with a restriction on individual increases in inbreeding resulted in the largest response to selection during early generations, but if inbreeding depression is a concern, a minimum coancestry scenario is then a valuable alternative, in particular for a long-term response to selection.

A high-resolution speleothem record of western equatorial Pacific rainfall: Implications for Holocene ENSO evolution

NASA Astrophysics Data System (ADS)

Chen, Sang; Hoffmann, Sharon S.; Lund, David C.; Cobb, Kim M.; Emile-Geay, Julien; Adkins, Jess F.

2016-05-01

The El Niño-Southern Oscillation (ENSO) is the primary driver of interannual climate variability in the tropics and subtropics. Despite substantial progress in understanding ocean-atmosphere feedbacks that drive ENSO today, relatively little is known about its behavior on centennial and longer timescales. Paleoclimate records from lakes, corals, molluscs and deep-sea sediments generally suggest that ENSO variability was weaker during the mid-Holocene (4-6 kyr BP) than the late Holocene (0-4 kyr BP). However, discrepancies amongst the records preclude a clear timeline of Holocene ENSO evolution and therefore the attribution of ENSO variability to specific climate forcing mechanisms. Here we present δ18 O results from a U-Th dated speleothem in Malaysian Borneo sampled at sub-annual resolution. The δ18 O of Borneo rainfall is a robust proxy of regional convective intensity and precipitation amount, both of which are directly influenced by ENSO activity. Our estimates of stalagmite δ18 O variance at ENSO periods (2-7 yr) show a significant reduction in interannual variability during the mid-Holocene (3240-3380 and 5160-5230 yr BP) relative to both the late Holocene (2390-2590 yr BP) and early Holocene (6590-6730 yr BP). The Borneo results are therefore inconsistent with lacustrine records of ENSO from the eastern equatorial Pacific that show little or no ENSO variance during the early Holocene. Instead, our results support coral, mollusc and foraminiferal records from the central and eastern equatorial Pacific that show a mid-Holocene minimum in ENSO variance. Reduced mid-Holocene interannual δ18 O variability in Borneo coincides with an overall minimum in mean δ18 O from 3.5 to 5.5 kyr BP. Persistent warm pool convection would tend to enhance the Walker circulation during the mid-Holocene, which likely contributed to reduced ENSO variance during this period. This finding implies that both convective intensity and interannual variability in Borneo are driven by coupled air-sea dynamics that are sensitive to precessional insolation forcing. Isolating the exact mechanisms that drive long-term ENSO evolution will require additional high-resolution paleoclimatic reconstructions and further investigation of Holocene tropical climate evolution using coupled climate models.

The Impact of Truth Surrogate Variance on Quality Assessment/Assurance in Wind Tunnel Testing

NASA Technical Reports Server (NTRS)

DeLoach, Richard

2016-01-01

Minimum data volume requirements for wind tunnel testing are reviewed and shown to depend on error tolerance, response model complexity, random error variance in the measurement environment, and maximum acceptable levels of inference error risk. Distinctions are made between such related concepts as quality assurance and quality assessment in response surface modeling, as well as between precision and accuracy. Earlier research on the scaling of wind tunnel tests is extended to account for variance in the truth surrogates used at confirmation sites in the design space to validate proposed response models. A model adequacy metric is presented that represents the fraction of the design space within which model predictions can be expected to satisfy prescribed quality specifications. The impact of inference error on the assessment of response model residuals is reviewed. The number of sites where reasonably well-fitted response models actually predict inadequately is shown to be considerably less than the number of sites where residuals are out of tolerance. The significance of such inference error effects on common response model assessment strategies is examined.

[Determination and principal component analysis of mineral elements based on ICP-OES in Nitraria roborowskii fruits from different regions].

PubMed

Yuan, Yuan-Yuan; Zhou, Yu-Bi; Sun, Jing; Deng, Juan; Bai, Ying; Wang, Jie; Lu, Xue-Feng

2017-06-01

The content of elements in fifteen different regions of Nitraria roborowskii samples were determined by inductively coupled plasma-atomic emission spectrometry(ICP-OES), and its elemental characteristics were analyzed by principal component analysis. The results indicated that 18 mineral elements were detected in N. roborowskii of which V cannot be detected. In addition, contents of Na, K and Ca showed high concentration. Ti showed maximum content variance, while K is minimum. Four principal components were gained from the original data. The cumulative variance contribution rate is 81.542% and the variance contribution of the first principal component was 44.997%, indicating that Cr, Fe, P and Ca were the characteristic elements of N. roborowskii.Thus, the established method was simple, precise and can be used for determination of mineral elements in N.roborowskii Kom. fruits. The elemental distribution characteristics among N.roborowskii fruits are related to geographical origins which were clearly revealed by PCA. All the results will provide good basis for comprehensive utilization of N.roborowskii. Copyright© by the Chinese Pharmaceutical Association.

Plasma dynamics on current-carrying magnetic flux tubes

NASA Technical Reports Server (NTRS)

Swift, Daniel W.

1992-01-01

A 1D numerical simulation is used to investigate the evolution of a plasma in a current-carrying magnetic flux tube of variable cross section. A large potential difference, parallel to the magnetic field, is applied across the domain. The result is that density minimum tends to deepen, primarily in the cathode end, and the entire potential drop becomes concentrated across the region of density minimum. The evolution of the simulation shows some sensitivity to particle boundary conditions, but the simulations inevitably evolve into a final state with a nearly stationary double layer near the cathode end. The simulation results are at sufficient variance with observations that it appears unlikely that auroral electrons can be explained by a simple process of acceleration through a field-aligned potential drop.

Statistical and population genetics issues of two Hungarian datasets from the aspect of DNA evidence interpretation.

PubMed

Szabolcsi, Zoltán; Farkas, Zsuzsa; Borbély, Andrea; Bárány, Gusztáv; Varga, Dániel; Heinrich, Attila; Völgyi, Antónia; Pamjav, Horolma

2015-11-01

When the DNA profile from a crime-scene matches that of a suspect, the weight of DNA evidence depends on the unbiased estimation of the match probability of the profiles. For this reason, it is required to establish and expand the databases that reflect the actual allele frequencies in the population applied. 21,473 complete DNA profiles from Databank samples were used to establish the allele frequency database to represent the population of Hungarian suspects. We used fifteen STR loci (PowerPlex ESI16) including five, new ESS loci. The aim was to calculate the statistical, forensic efficiency parameters for the Databank samples and compare the newly detected data to the earlier report. The population substructure caused by relatedness may influence the frequency of profiles estimated. As our Databank profiles were considered non-random samples, possible relationships between the suspects can be assumed. Therefore, population inbreeding effect was estimated using the FIS calculation. The overall inbreeding parameter was found to be 0.0106. Furthermore, we tested the impact of the two allele frequency datasets on 101 randomly chosen STR profiles, including full and partial profiles. The 95% confidence interval estimates for the profile frequencies (pM) resulted in a tighter range when we used the new dataset compared to the previously published ones. We found that the FIS had less effect on frequency values in the 21,473 samples than the application of minimum allele frequency. No genetic substructure was detected by STRUCTURE analysis. Due to the low level of inbreeding effect and the high number of samples, the new dataset provides unbiased and precise estimates of LR for statistical interpretation of forensic casework and allows us to use lower allele frequencies. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Spectroscopic observation of SN2017gkk by NUTS (NOT Un-biased Transient Survey)

NASA Astrophysics Data System (ADS)

Onori, F.; Benetti, S.; Cappellaro, E.; Losada, Illa R.; Gafton, E.; NUTS Collaboration

2017-09-01

The Nordic Optical Telescope (NOT) Unbiased Transient Survey (NUTS; ATel #8992) reports the spectroscopic classification of supernova SN2017gkk (=MASTER OT J091344.71762842.5) in host galaxy NGC 2748.

Spectroscopic observation of ASASSN-17he by NUTS (NOT Un-biased Transient Survey)

NASA Astrophysics Data System (ADS)

Kostrzewa-Rutkowska, Z.; Benetti, S.; Dong, S.; Stritzinger, M.; Stanek, K.; Brimacombe, J.; Sagues, A.; Galindo, P.; Losada, I. Rivero

2017-10-01

The Nordic Optical Telescope (NOT) Unbiased Transient Survey (NUTS; ATel #8992) reports the spectroscopic classification of ASASSN-17he. The candidate was discovered by by the All-Sky Automated Survey for Supernovae.

A New Method for Estimating the Effective Population Size from Allele Frequency Changes

PubMed Central

Pollak, Edward

1983-01-01

A new procedure is proposed for estimating the effective population size, given that information is available on changes in frequencies of the alleles at one or more independently segregating loci and the population is observed at two or more separate times. Approximate expressions are obtained for the variances of the new statistic, as well as others, also based on allele frequency changes, that have been discussed in the literature. This analysis indicates that the new statistic will generally have a smaller variance than the others. Estimates of effective population sizes and of the standard errors of the estimates are computed for data on two fly populations that have been discussed in earlier papers. In both cases, there is evidence that the effective population size is very much smaller than the minimum census size of the population. PMID:17246147

Evaluating climate change impacts on streamflow variability based on a multisite multivariate GCM downscaling method in the Jing River of China

NASA Astrophysics Data System (ADS)

Li, Zhi; Jin, Jiming

2017-11-01

Projected hydrological variability is important for future resource and hazard management of water supplies because changes in hydrological variability can cause more disasters than changes in the mean state. However, climate change scenarios downscaled from Earth System Models (ESMs) at single sites cannot meet the requirements of distributed hydrologic models for simulating hydrological variability. This study developed multisite multivariate climate change scenarios via three steps: (i) spatial downscaling of ESMs using a transfer function method, (ii) temporal downscaling of ESMs using a single-site weather generator, and (iii) reconstruction of spatiotemporal correlations using a distribution-free shuffle procedure. Multisite precipitation and temperature change scenarios for 2011-2040 were generated from five ESMs under four representative concentration pathways to project changes in streamflow variability using the Soil and Water Assessment Tool (SWAT) for the Jing River, China. The correlation reconstruction method performed realistically for intersite and intervariable correlation reproduction and hydrological modeling. The SWAT model was found to be well calibrated with monthly streamflow with a model efficiency coefficient of 0.78. It was projected that the annual mean precipitation would not change, while the mean maximum and minimum temperatures would increase significantly by 1.6 ± 0.3 and 1.3 ± 0.2 °C; the variance ratios of 2011-2040 to 1961-2005 were 1.15 ± 0.13 for precipitation, 1.15 ± 0.14 for mean maximum temperature, and 1.04 ± 0.10 for mean minimum temperature. A warmer climate was predicted for the flood season, while the dry season was projected to become wetter and warmer; the findings indicated that the intra-annual and interannual variations in the future climate would be greater than in the current climate. The total annual streamflow was found to change insignificantly but its variance ratios of 2011-2040 to 1961-2005 increased by 1.25 ± 0.55. Streamflow variability was predicted to become greater over most months on the seasonal scale because of the increased monthly maximum streamflow and decreased monthly minimum streamflow. The increase in streamflow variability was attributed mainly to larger positive contributions from increased precipitation variances rather than negative contributions from increased mean temperatures.

Convergence of Free Energy Profile of Coumarin in Lipid Bilayer

PubMed Central

2012-01-01

Atomistic molecular dynamics (MD) simulations of druglike molecules embedded in lipid bilayers are of considerable interest as models for drug penetration and positioning in biological membranes. Here we analyze partitioning of coumarin in dioleoylphosphatidylcholine (DOPC) bilayer, based on both multiple, unbiased 3 μs MD simulations (total length) and free energy profiles along the bilayer normal calculated by biased MD simulations (∼7 μs in total). The convergences in time of free energy profiles calculated by both umbrella sampling and z-constraint techniques are thoroughly analyzed. Two sets of starting structures are also considered, one from unbiased MD simulation and the other from “pulling” coumarin along the bilayer normal. The structures obtained by pulling simulation contain water defects on the lipid bilayer surface, while those acquired from unbiased simulation have no membrane defects. The free energy profiles converge more rapidly when starting frames from unbiased simulations are used. In addition, z-constraint simulation leads to more rapid convergence than umbrella sampling, due to quicker relaxation of membrane defects. Furthermore, we show that the choice of RESP, PRODRG, or Mulliken charges considerably affects the resulting free energy profile of our model drug along the bilayer normal. We recommend using z-constraint biased MD simulations based on starting geometries acquired from unbiased MD simulations for efficient calculation of convergent free energy profiles of druglike molecules along bilayer normals. The calculation of free energy profile should start with an unbiased simulation, though the polar molecules might need a slow pulling afterward. Results obtained with the recommended simulation protocol agree well with available experimental data for two coumarin derivatives. PMID:22545027

Convergence of Free Energy Profile of Coumarin in Lipid Bilayer.

PubMed

Paloncýová, Markéta; Berka, Karel; Otyepka, Michal

2012-04-10

Atomistic molecular dynamics (MD) simulations of druglike molecules embedded in lipid bilayers are of considerable interest as models for drug penetration and positioning in biological membranes. Here we analyze partitioning of coumarin in dioleoylphosphatidylcholine (DOPC) bilayer, based on both multiple, unbiased 3 μs MD simulations (total length) and free energy profiles along the bilayer normal calculated by biased MD simulations (∼7 μs in total). The convergences in time of free energy profiles calculated by both umbrella sampling and z-constraint techniques are thoroughly analyzed. Two sets of starting structures are also considered, one from unbiased MD simulation and the other from "pulling" coumarin along the bilayer normal. The structures obtained by pulling simulation contain water defects on the lipid bilayer surface, while those acquired from unbiased simulation have no membrane defects. The free energy profiles converge more rapidly when starting frames from unbiased simulations are used. In addition, z-constraint simulation leads to more rapid convergence than umbrella sampling, due to quicker relaxation of membrane defects. Furthermore, we show that the choice of RESP, PRODRG, or Mulliken charges considerably affects the resulting free energy profile of our model drug along the bilayer normal. We recommend using z-constraint biased MD simulations based on starting geometries acquired from unbiased MD simulations for efficient calculation of convergent free energy profiles of druglike molecules along bilayer normals. The calculation of free energy profile should start with an unbiased simulation, though the polar molecules might need a slow pulling afterward. Results obtained with the recommended simulation protocol agree well with available experimental data for two coumarin derivatives.

Spectroscopic classification of Gaia18adv by NUTS (NOT Un-biased Transient Survey)

NASA Astrophysics Data System (ADS)

Gall, C.; Benetti, S.; Wyrzykowski, L.; Stritzinger, M.; Holmbo, S.; Dong, S.; Siltala, Lauri; NUTS Collaboration

2018-01-01

The Nordic Optical Telescope (NOT) Unbiased Transient Survey (NUTS; ATel #8992) collaboration reports the spectroscopic classification of Gaia18adv (SN2018hh) near the host galaxy SDSS J121341.37+282640.0.

Identification, Characterization, and Utilization of Adult Meniscal Progenitor Cells

DTIC Science & Technology

2017-11-01

approach including row scaling and Ward’s minimum variance method was chosen. This analysis revealed two groups of four samples each. For the selected...articular cartilage in an ovine model. Am J Sports Med. 2008;36(5):841-50. 7. Deshpande BR, Katz JN, Solomon DH, Yelin EH, Hunter DJ, Messier SP, et al...Miosge1,* 1Tissue Regeneration Work Group , Department of Prosthodontics, Medical Faculty, Georg-August-University, 37075 Goettingen, Germany 2Institute of

An Analysis Of The Benefits And Application Of Earned Value Management (EVM) Project Management Techniques For Dod Programs That Do Not Meet Dod Policy Thresholds

DTIC Science & Technology

2017-12-01

carefully to ensure only minimum information needed for effective management control is requested.  Requires cost-benefit analysis and PM...baseline offers metrics that highlights performance treads and program variances. This information provides Program Managers and higher levels of...The existing training philosophy is effective only if the managers using the information have well trained and experienced personnel that can

The Three-Dimensional Power Spectrum Of Galaxies from the Sloan Digital Sky Survey

DTIC Science & Technology

2004-05-10

aspects of the three-dimensional clustering of a much larger data set involving over 200,000 galaxies with redshifts. This paper is focused on measuring... papers , we will constrain galaxy bias empirically by using clustering measurements on smaller scales (e.g., I. Zehavi et al. 2004, in preparation...minimum-variance measurements in 22 k-bands of both the clustering power and its anisotropy due to redshift-space distortions, with narrow and well

Waveform-based spaceborne GNSS-R wind speed observation: Demonstration and analysis using UK TechDemoSat-1 data

NASA Astrophysics Data System (ADS)

Wang, Feng; Yang, Dongkai; Zhang, Bo; Li, Weiqiang

2018-03-01

This paper explores two types of mathematical functions to fit single- and full-frequency waveform of spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R), respectively. The metrics of the waveforms, such as the noise floor, peak magnitude, mid-point position of the leading edge, leading edge slope and trailing edge slope, can be derived from the parameters of the proposed models. Because the quality of the UK TDS-1 data is not at the level required by remote sensing mission, the waveforms buried in noise or from ice/land are removed by defining peak-to-mean ratio, cosine similarity of the waveform before wind speed are retrieved. The single-parameter retrieval models are developed by comparing the peak magnitude, leading edge slope and trailing edge slope derived from the parameters of the proposed models with in situ wind speed from the ASCAT scatterometer. To improve the retrieval accuracy, three types of multi-parameter observations based on the principle component analysis (PCA), minimum variance (MV) estimator and Back Propagation (BP) network are implemented. The results indicate that compared to the best results of the single-parameter observation, the approaches based on the principle component analysis and minimum variance could not significantly improve retrieval accuracy, however, the BP networks obtain improvement with the RMSE of 2.55 m/s and 2.53 m/s for single- and full-frequency waveform, respectively.

Fast Minimum Variance Beamforming Based on Legendre Polynomials.

PubMed

Bae, MooHo; Park, Sung Bae; Kwon, Sung Jae

2016-09-01

Currently, minimum variance beamforming (MV) is actively investigated as a method that can improve the performance of an ultrasound beamformer, in terms of the lateral and contrast resolution. However, this method has the disadvantage of excessive computational complexity since the inverse spatial covariance matrix must be calculated. Some noteworthy methods among various attempts to solve this problem include beam space adaptive beamforming methods and the fast MV method based on principal component analysis, which are similar in that the original signal in the element space is transformed to another domain using an orthonormal basis matrix and the dimension of the covariance matrix is reduced by approximating the matrix only with important components of the matrix, hence making the inversion of the matrix very simple. Recently, we proposed a new method with further reduced computational demand that uses Legendre polynomials as the basis matrix for such a transformation. In this paper, we verify the efficacy of the proposed method through Field II simulations as well as in vitro and in vivo experiments. The results show that the approximation error of this method is less than or similar to those of the above-mentioned methods and that the lateral response of point targets and the contrast-to-speckle noise in anechoic cysts are also better than or similar to those methods when the dimensionality of the covariance matrices is reduced to the same dimension.

Null steering of adaptive beamforming using linear constraint minimum variance assisted by particle swarm optimization, dynamic mutated artificial immune system, and gravitational search algorithm.

PubMed

Darzi, Soodabeh; Kiong, Tiong Sieh; Islam, Mohammad Tariqul; Ismail, Mahamod; Kibria, Salehin; Salem, Balasem

2014-01-01

Linear constraint minimum variance (LCMV) is one of the adaptive beamforming techniques that is commonly applied to cancel interfering signals and steer or produce a strong beam to the desired signal through its computed weight vectors. However, weights computed by LCMV usually are not able to form the radiation beam towards the target user precisely and not good enough to reduce the interference by placing null at the interference sources. It is difficult to improve and optimize the LCMV beamforming technique through conventional empirical approach. To provide a solution to this problem, artificial intelligence (AI) technique is explored in order to enhance the LCMV beamforming ability. In this paper, particle swarm optimization (PSO), dynamic mutated artificial immune system (DM-AIS), and gravitational search algorithm (GSA) are incorporated into the existing LCMV technique in order to improve the weights of LCMV. The simulation result demonstrates that received signal to interference and noise ratio (SINR) of target user can be significantly improved by the integration of PSO, DM-AIS, and GSA in LCMV through the suppression of interference in undesired direction. Furthermore, the proposed GSA can be applied as a more effective technique in LCMV beamforming optimization as compared to the PSO technique. The algorithms were implemented using Matlab program.

Null Steering of Adaptive Beamforming Using Linear Constraint Minimum Variance Assisted by Particle Swarm Optimization, Dynamic Mutated Artificial Immune System, and Gravitational Search Algorithm

PubMed Central

Sieh Kiong, Tiong; Tariqul Islam, Mohammad; Ismail, Mahamod; Salem, Balasem

2014-01-01

Linear constraint minimum variance (LCMV) is one of the adaptive beamforming techniques that is commonly applied to cancel interfering signals and steer or produce a strong beam to the desired signal through its computed weight vectors. However, weights computed by LCMV usually are not able to form the radiation beam towards the target user precisely and not good enough to reduce the interference by placing null at the interference sources. It is difficult to improve and optimize the LCMV beamforming technique through conventional empirical approach. To provide a solution to this problem, artificial intelligence (AI) technique is explored in order to enhance the LCMV beamforming ability. In this paper, particle swarm optimization (PSO), dynamic mutated artificial immune system (DM-AIS), and gravitational search algorithm (GSA) are incorporated into the existing LCMV technique in order to improve the weights of LCMV. The simulation result demonstrates that received signal to interference and noise ratio (SINR) of target user can be significantly improved by the integration of PSO, DM-AIS, and GSA in LCMV through the suppression of interference in undesired direction. Furthermore, the proposed GSA can be applied as a more effective technique in LCMV beamforming optimization as compared to the PSO technique. The algorithms were implemented using Matlab program. PMID:25147859

Demographics of an ornate box turtle population experiencing minimal human-induced disturbances

USGS Publications Warehouse

Converse, S.J.; Iverson, J.B.; Savidge, J.A.

2005-01-01

Human-induced disturbances may threaten the viability of many turtle populations, including populations of North American box turtles. Evaluation of the potential impacts of these disturbances can be aided by long-term studies of populations subject to minimal human activity. In such a population of ornate box turtles (Terrapene ornata ornata) in western Nebraska, we examined survival rates and population growth rates from 1981-2000 based on mark-recapture data. The average annual apparent survival rate of adult males was 0.883 (SE = 0.021) and of adult females was 0.932 (SE = 0.014). Minimum winter temperature was the best of five climate variables as a predictor of adult survival. Survival rates were highest in years with low minimum winter temperatures, suggesting that global warming may result in declining survival. We estimated an average adult population growth rate (????) of 1.006 (SE = 0.065), with an estimated temporal process variance (????2) of 0.029 (95% CI = 0.005-0.176). Stochastic simulations suggest that this mean and temporal process variance would result in a 58% probability of a population decrease over a 20-year period. This research provides evidence that, unless unknown density-dependent mechanisms are operating in the adult age class, significant human disturbances, such as commercial harvest or turtle mortality on roads, represent a potential risk to box turtle populations. ?? 2005 by the Ecological Society of America.

Spectroscopic classification of supernovae SN 2018aei and SN 2018aej by NUTS (NOT Un-biased Transient Survey)

NASA Astrophysics Data System (ADS)

Cannizzaro, G.; Kuncarayakti, H.; Fraser, M.; Hamanowicz, A.; Jonker, P.; Kankare, E.; Kostrzewa-Rutkowska, Z.; Onori, F.; Wevers, T.; Wyrzykowski, L.; Galbany, L.

2018-03-01

The NOT Unbiased Transient Survey (NUTS; ATel #8992) collaboration reports the spectroscopic classification of supernovae SN 2018aei and SN 2018aej, discovered by PanSTARSS Survey for Transients (ATel #11408).

Reconfigurable generation and measurement of mutually unbiased bases for time-bin qudits

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

Lukens, Joseph M.; Islam, Nurul T.; Lim, Charles Ci Wen

Here, we propose a method for implementing mutually unbiased generation and measurement of time-bin qudits using a cascade of electro-optic phase modulator–coded fiber Bragg grating pairs. Our approach requires only a single spatial mode and can switch rapidly between basis choices. We obtain explicit solutions for dimensions d = 2, 3, and 4 that realize all d + 1 possible mutually unbiased bases and analyze the performance of our approach in quantum key distribution. Given its practicality and compatibility with current technology, our approach provides a promising springboard for scalable processing of high-dimensional time-bin states.

Reconfigurable generation and measurement of mutually unbiased bases for time-bin qudits

DOE PAGES

Lukens, Joseph M.; Islam, Nurul T.; Lim, Charles Ci Wen; ...

2018-03-12

Here, we propose a method for implementing mutually unbiased generation and measurement of time-bin qudits using a cascade of electro-optic phase modulator–coded fiber Bragg grating pairs. Our approach requires only a single spatial mode and can switch rapidly between basis choices. We obtain explicit solutions for dimensions d = 2, 3, and 4 that realize all d + 1 possible mutually unbiased bases and analyze the performance of our approach in quantum key distribution. Given its practicality and compatibility with current technology, our approach provides a promising springboard for scalable processing of high-dimensional time-bin states.

Reconfigurable generation and measurement of mutually unbiased bases for time-bin qudits

NASA Astrophysics Data System (ADS)

Lukens, Joseph M.; Islam, Nurul T.; Lim, Charles Ci Wen; Gauthier, Daniel J.

2018-03-01

We propose a method for implementing mutually unbiased generation and measurement of time-bin qudits using a cascade of electro-optic phase modulator-coded fiber Bragg grating pairs. Our approach requires only a single spatial mode and can switch rapidly between basis choices. We obtain explicit solutions for dimensions d = 2, 3, and 4 that realize all d + 1 possible mutually unbiased bases and analyze the performance of our approach in quantum key distribution. Given its practicality and compatibility with current technology, our approach provides a promising springboard for scalable processing of high-dimensional time-bin states.

Examinations of tRNA Range of Motion Using Simulations of Cryo-EM Microscopy and X-Ray Data.

PubMed

Caulfield, Thomas R; Devkota, Batsal; Rollins, Geoffrey C

2011-01-01

We examined tRNA flexibility using a combination of steered and unbiased molecular dynamics simulations. Using Maxwell's demon algorithm, molecular dynamics was used to steer X-ray structure data toward that from an alternative state obtained from cryogenic-electron microscopy density maps. Thus, we were able to fit X-ray structures of tRNA onto cryogenic-electron microscopy density maps for hybrid states of tRNA. Additionally, we employed both Maxwell's demon molecular dynamics simulations and unbiased simulation methods to identify possible ribosome-tRNA contact areas where the ribosome may discriminate tRNAs during translation. Herein, we collected >500 ns of simulation data to assess the global range of motion for tRNAs. Biased simulations can be used to steer between known conformational stop points, while unbiased simulations allow for a general testing of conformational space previously unexplored. The unbiased molecular dynamics data describes the global conformational changes of tRNA on a sub-microsecond time scale for comparison with steered data. Additionally, the unbiased molecular dynamics data was used to identify putative contacts between tRNA and the ribosome during the accommodation step of translation. We found that the primary contact regions were H71 and H92 of the 50S subunit and ribosomal proteins L14 and L16.

Right ventrolateral prefrontal cortex mediates individual differences in conflict-driven cognitive control

PubMed Central

Egner, Tobias

2013-01-01

Conflict adaptation – a conflict-triggered improvement in the resolution of conflicting stimulus or response representations – has become a widely used probe of cognitive control processes in both healthy and clinical populations. Previous functional magnetic resonance imaging (fMRI) studies have localized activation foci associated with conflict resolution to dorsolateral prefrontal cortex (dlPFC). The traditional group-analysis approach employed in these studies highlights regions that are, on average, activated during conflict resolution, but does not necessarily reveal areas mediating individual differences in conflict resolution, because between-subject variance is treated as noise. Here, we employed a complementary approach in order to elucidate the neural bases of variability in the proficiency of conflict-driven cognitive control. We analyzed two independent fMRI data sets of face-word Stroop tasks by using individual variability in the behavioral expression of conflict adaptation as the metric against which brain activation was regressed, while controlling for individual differences in mean reaction time and Stroop interference. Across the two experiments, a replicable neural substrate of individual variation in conflict adaptation was found in ventrolateral prefrontal cortex (vlPFC), specifically, in the right inferior frontal gyrus, pars orbitalis (BA 47). Unbiased regression estimates showed that variability in activity in this region accounted for ~40% of the variance in behavioral expression of conflict adaptation across subjects, thus documenting a heretofore unsuspected key role for vlPFC in mediating conflict-driven adjustments in cognitive control. We speculate that vlPFC plays a primary role in conflict control that is supplemented by dlPFC recruitment under conditions of suboptimal performance. PMID:21568631

Maximizing the reliability of genomic selection by optimizing the calibration set of reference individuals: comparison of methods in two diverse groups of maize inbreds (Zea mays L.).

PubMed

Rincent, R; Laloë, D; Nicolas, S; Altmann, T; Brunel, D; Revilla, P; Rodríguez, V M; Moreno-Gonzalez, J; Melchinger, A; Bauer, E; Schoen, C-C; Meyer, N; Giauffret, C; Bauland, C; Jamin, P; Laborde, J; Monod, H; Flament, P; Charcosset, A; Moreau, L

2012-10-01

Genomic selection refers to the use of genotypic information for predicting breeding values of selection candidates. A prediction formula is calibrated with the genotypes and phenotypes of reference individuals constituting the calibration set. The size and the composition of this set are essential parameters affecting the prediction reliabilities. The objective of this study was to maximize reliabilities by optimizing the calibration set. Different criteria based on the diversity or on the prediction error variance (PEV) derived from the realized additive relationship matrix-best linear unbiased predictions model (RA-BLUP) were used to select the reference individuals. For the latter, we considered the mean of the PEV of the contrasts between each selection candidate and the mean of the population (PEVmean) and the mean of the expected reliabilities of the same contrasts (CDmean). These criteria were tested with phenotypic data collected on two diversity panels of maize (Zea mays L.) genotyped with a 50k SNPs array. In the two panels, samples chosen based on CDmean gave higher reliabilities than random samples for various calibration set sizes. CDmean also appeared superior to PEVmean, which can be explained by the fact that it takes into account the reduction of variance due to the relatedness between individuals. Selected samples were close to optimality for a wide range of trait heritabilities, which suggests that the strategy presented here can efficiently sample subsets in panels of inbred lines. A script to optimize reference samples based on CDmean is available on request.

Modeling effects of hydrological changes on the carbon and nitrogen balance of oak in floodplains.

PubMed

Pietsch, Stephan A; Hasenauer, Hubert; Kucera, Jiŕi; Cermák, Jan

2003-08-01

We extended the applicability of the ecosystem model BIOME-BGC to floodplain ecosystems to study effects of hydrological changes on Quercus robur L. stands. The extended model assesses floodplain peculiarities, i.e., seasonal flooding and water infiltration from the groundwater table. Our interest was the tradeoff between (a). maintaining regional applicability with respect to available model input information, (b). incorporating the necessary mechanistic detail and (c). keeping the computational effort at an acceptable level. An evaluation based on observed transpiration, timber volume, soil carbon and soil nitrogen content showed that the extended model produced unbiased results. We also investigated the impact of hydrological changes on our oak stands as a result of the completion of an artificial canal network in 1971, which has stopped regular springtime flooding. A comparison of the 11 years before versus the 11 years after 1971 demonstrated that the hydrological changes affected mainly the annual variation across years in leaf area index (LAI) and soil carbon and nitrogen sequestration, leading to stagnation of carbon and nitrogen stocks, but to an increase in the variance across years. However, carbon sequestration to timber was unaffected and exhibited no significant change in cross-year variation. Finally, we investigated how drawdown of the water table, a general problem in the region, affects modeled ecosystem behavior. We found a further amplification of cross-year LAI fluctuations, but the variance in soil carbon and nitrogen stocks decreased. Volume increment was unaffected, suggesting a stabilization of the ecosystem two decades after implementation of water management measures.

GABA editing with macromolecule suppression using an improved MEGA-SPECIAL sequence.

PubMed

Gu, Meng; Hurd, Ralph; Noeske, Ralph; Baltusis, Laima; Hancock, Roeland; Sacchet, Matthew D; Gotlib, Ian H; Chin, Frederick T; Spielman, Daniel M

2018-01-01

The most common γ-aminobutyric-acid (GABA) editing approach, MEGA-PRESS, uses J-editing to measure GABA distinct from larger overlapping metabolites, but suffers contamination from coedited macromolecules (MMs) comprising 40 to 60% of the observed signal. MEGA-SPECIAL is an alternative method with better MM suppression, but is not widely used primarily because of its relatively poor spatial localization. Our goal was to develop an improved MM-suppressed GABA editing sequence at 3 Tesla. We modified a single-voxel MEGA-SPECIAL sequence with an oscillating readout gradient for improved spatial localization, and used very selective 30-ms editing pulses for improved suppression of coedited MMs. Simulation and in vivo experiments confirmed excellent MM suppression, insensitive to the range of B 0 frequency drifts typically encountered in vivo. Both intersubject and intrasubject studies showed that MMs, when suppressed by the improved MEGA-SPECIAL method, contributed approximately 40% to the corresponding MEGA-PRESS measurements. From the intersubject study, the coefficient of variation for GABA+/Cre (MEGA-PRESS) was 11.2% versus 7% for GABA/Cre (improved MEGA-SPECIAL), demonstrating significantly reduced variance (P = 0.005), likely coming from coedited MMs. This improved MEGA-SPECIAL sequence provides unbiased GABA measurements with reduced variance as compared with conventional MEGA-PRESS. This approach is also relatively insensitive to the range of B 0 drifts typically observed in in vivo human studies. Magn Reson Med 79:41-47, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Narrow-sense heritability estimation of complex traits using identity-by-descent information.

PubMed

Evans, Luke M; Tahmasbi, Rasool; Jones, Matt; Vrieze, Scott I; Abecasis, Gonçalo R; Das, Sayantan; Bjelland, Douglas W; de Candia, Teresa R; Yang, Jian; Goddard, Michael E; Visscher, Peter M; Keller, Matthew C

2018-03-28

Heritability is a fundamental parameter in genetics. Traditional estimates based on family or twin studies can be biased due to shared environmental or non-additive genetic variance. Alternatively, those based on genotyped or imputed variants typically underestimate narrow-sense heritability contributed by rare or otherwise poorly tagged causal variants. Identical-by-descent (IBD) segments of the genome share all variants between pairs of chromosomes except new mutations that have arisen since the last common ancestor. Therefore, relating phenotypic similarity to degree of IBD sharing among classically unrelated individuals is an appealing approach to estimating the near full additive genetic variance while possibly avoiding biases that can occur when modeling close relatives. We applied an IBD-based approach (GREML-IBD) to estimate heritability in unrelated individuals using phenotypic simulation with thousands of whole-genome sequences across a range of stratification, polygenicity levels, and the minor allele frequencies of causal variants (CVs). In simulations, the IBD-based approach produced unbiased heritability estimates, even when CVs were extremely rare, although precision was low. However, population stratification and non-genetic familial environmental effects shared across generations led to strong biases in IBD-based heritability. We used data on two traits in ~120,000 people from the UK Biobank to demonstrate that, depending on the trait and possible confounding environmental effects, GREML-IBD can be applied to very large genetic datasets to infer the contribution of very rare variants lost using other methods. However, we observed apparent biases in these real data, suggesting that more work may be required to understand and mitigate factors that influence IBD-based heritability estimates.

Right ventrolateral prefrontal cortex mediates individual differences in conflict-driven cognitive control.

PubMed

Egner, Tobias

2011-12-01

Conflict adaptation--a conflict-triggered improvement in the resolution of conflicting stimulus or response representations--has become a widely used probe of cognitive control processes in both healthy and clinical populations. Previous fMRI studies have localized activation foci associated with conflict resolution to dorsolateral PFC (dlPFC). The traditional group analysis approach employed in these studies highlights regions that are, on average, activated during conflict resolution, but does not necessarily reveal areas mediating individual differences in conflict resolution, because between-subject variance is treated as noise. Here, we employed a complementary approach to elucidate the neural bases of variability in the proficiency of conflict-driven cognitive control. We analyzed two independent fMRI data sets of face-word Stroop tasks by using individual variability in the behavioral expression of conflict adaptation as the metric against which brain activation was regressed while controlling for individual differences in mean RT and Stroop interference. Across the two experiments, a replicable neural substrate of individual variation in conflict adaptation was found in ventrolateral PFC (vlPFC), specifically, in the right inferior frontal gyrus, pars orbitalis (BA 47). Unbiased regression estimates showed that variability in activity in this region accounted for ∼ 40% of the variance in behavioral expression of conflict adaptation across subjects, thus documenting a heretofore unsuspected key role for vlPFC in mediating conflict-driven adjustments in cognitive control. We speculate that vlPFC plays a primary role in conflict control that is supplemented by dlPFC recruitment under conditions of suboptimal performance.

Genome wide selection in Citrus breeding.

PubMed

Gois, I B; Borém, A; Cristofani-Yaly, M; de Resende, M D V; Azevedo, C F; Bastianel, M; Novelli, V M; Machado, M A

2016-10-17

Genome wide selection (GWS) is essential for the genetic improvement of perennial species such as Citrus because of its ability to increase gain per unit time and to enable the efficient selection of characteristics with low heritability. This study assessed GWS efficiency in a population of Citrus and compared it with selection based on phenotypic data. A total of 180 individual trees from a cross between Pera sweet orange (Citrus sinensis Osbeck) and Murcott tangor (Citrus sinensis Osbeck x Citrus reticulata Blanco) were evaluated for 10 characteristics related to fruit quality. The hybrids were genotyped using 5287 DArT_seq TM (diversity arrays technology) molecular markers and their effects on phenotypes were predicted using the random regression - best linear unbiased predictor (rr-BLUP) method. The predictive ability, prediction bias, and accuracy of GWS were estimated to verify its effectiveness for phenotype prediction. The proportion of genetic variance explained by the markers was also computed. The heritability of the traits, as determined by markers, was 16-28%. The predictive ability of these markers ranged from 0.53 to 0.64, and the regression coefficients between predicted and observed phenotypes were close to unity. Over 35% of the genetic variance was accounted for by the markers. Accuracy estimates with GWS were lower than those obtained by phenotypic analysis; however, GWS was superior in terms of genetic gain per unit time. Thus, GWS may be useful for Citrus breeding as it can predict phenotypes early and accurately, and reduce the length of the selection cycle. This study demonstrates the feasibility of genomic selection in Citrus.

Predicting Individual Differences in Placebo Analgesia: Contributions of Brain Activity during Anticipation and Pain Experience

PubMed Central

Wager, Tor D.; Atlas, Lauren Y.; Leotti, Lauren A.; Rilling, James K.

2012-01-01

Recent studies have identified brain correlates of placebo analgesia, but none have assessed how accurately patterns of brain activity can predict individual differences in placebo responses. We reanalyzed data from two fMRI studies of placebo analgesia (N = 47), using patterns of fMRI activity during the anticipation and experience of pain to predict new subjects’ scores on placebo analgesia and placebo-induced changes in pain processing. We used a cross-validated regression procedure, LASSO-PCR, which provided both unbiased estimates of predictive accuracy and interpretable maps of which regions are most important for prediction. Increased anticipatory activity in a frontoparietal network and decreases in a posterior insular/temporal network predicted placebo analgesia. Patterns of anticipatory activity across the cortex predicted a moderate amount of variance in the placebo response (~12% overall, ~40% for study 2 alone), which is substantial considering the multiple likely contributing factors. The most predictive regions were those associated with emotional appraisal, rather than cognitive control or pain processing. During pain, decreases in limbic and paralimbic regions most strongly predicted placebo analgesia. Responses within canonical pain-processing regions explained significant variance in placebo analgesia, but the pattern of effects was inconsistent with widespread decreases in nociceptive processing. Together, the findings suggest that engagement of emotional appraisal circuits drives individual variation in placebo analgesia, rather than early suppression of nociceptive processing. This approach provides a framework that will allow prediction accuracy to increase as new studies provide more precise information for future predictive models. PMID:21228154

A general reconstruction of the recent expansion history of the universe

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

Vitenti, S.D.P.; Penna-Lima, M., E-mail: dias@iap.fr, E-mail: pennal@apc.in2p3.fr

Distance measurements are currently the most powerful tool to study the expansion history of the universe without specifying its matter content nor any theory of gravitation. Assuming only an isotropic, homogeneous and flat universe, in this work we introduce a model-independent method to reconstruct directly the deceleration function via a piecewise function. Including a penalty factor, we are able to vary continuously the complexity of the deceleration function from a linear case to an arbitrary (n+1)-knots spline interpolation. We carry out a Monte Carlo (MC) analysis to determine the best penalty factor, evaluating the bias-variance trade-off, given the uncertainties ofmore » the SDSS-II and SNLS supernova combined sample (JLA), compilations of baryon acoustic oscillation (BAO) and H(z) data. The bias-variance analysis is done for three fiducial models with different features in the deceleration curve. We perform the MC analysis generating mock catalogs and computing their best-fit. For each fiducial model, we test different reconstructions using, in each case, more than 10{sup 4} catalogs in a total of about 5× 10{sup 5}. This investigation proved to be essential in determining the best reconstruction to study these data. We show that, evaluating a single fiducial model, the conclusions about the bias-variance ratio are misleading. We determine the reconstruction method in which the bias represents at most 10% of the total uncertainty. In all statistical analyses, we fit the coefficients of the deceleration function along with four nuisance parameters of the supernova astrophysical model. For the full sample, we also fit H{sub 0} and the sound horizon r{sub s}(z{sub d}) at the drag redshift. The bias-variance trade-off analysis shows that, apart from the deceleration function, all other estimators are unbiased. Finally, we apply the Ensemble Sampler Markov Chain Monte Carlo (ESMCMC) method to explore the posterior of the deceleration function up to redshift 1.3 (using only JLA) and 2.3 (JLA+BAO+H(z)). We obtain that the standard cosmological model agrees within 3σ level with the reconstructed results in the whole studied redshift intervals. Since our method is calibrated to minimize the bias, the error bars of the reconstructed functions are a good approximation for the total uncertainty.« less

Scores on Riley's stuttering severity instrument versions three and four for samples of different length and for different types of speech material.

PubMed

Todd, Helena; Mirawdeli, Avin; Costelloe, Sarah; Cavenagh, Penny; Davis, Stephen; Howell, Peter

2014-12-01

Riley stated that the minimum speech sample length necessary to compute his stuttering severity estimates was 200 syllables. This was investigated. Procedures supplied for the assessment of readers and non-readers were examined to see whether they give equivalent scores. Recordings of spontaneous speech samples from 23 young children (aged between 2 years 8 months and 6 years 3 months) and 31 older children (aged between 10 years 0 months and 14 years 7 months) were made. Riley's severity estimates were scored on extracts of different lengths. The older children provided spontaneous and read samples, which were scored for severity according to reader and non-reader procedures. Analysis of variance supported the use of 200-syllable-long samples as the minimum necessary for obtaining severity scores. There was no significant difference in SSI-3 scores for the older children when the reader and non-reader procedures were used. Samples that are 200-syllables long are the minimum that is appropriate for obtaining stable Riley's severity scores. The procedural variants provide similar severity scores.

Population genetic differentiation of height and body mass index across Europe.

PubMed

Robinson, Matthew R; Hemani, Gibran; Medina-Gomez, Carolina; Mezzavilla, Massimo; Esko, Tonu; Shakhbazov, Konstantin; Powell, Joseph E; Vinkhuyzen, Anna; Berndt, Sonja I; Gustafsson, Stefan; Justice, Anne E; Kahali, Bratati; Locke, Adam E; Pers, Tune H; Vedantam, Sailaja; Wood, Andrew R; van Rheenen, Wouter; Andreassen, Ole A; Gasparini, Paolo; Metspalu, Andres; Berg, Leonard H van den; Veldink, Jan H; Rivadeneira, Fernando; Werge, Thomas M; Abecasis, Goncalo R; Boomsma, Dorret I; Chasman, Daniel I; de Geus, Eco J C; Frayling, Timothy M; Hirschhorn, Joel N; Hottenga, Jouke Jan; Ingelsson, Erik; Loos, Ruth J F; Magnusson, Patrik K E; Martin, Nicholas G; Montgomery, Grant W; North, Kari E; Pedersen, Nancy L; Spector, Timothy D; Speliotes, Elizabeth K; Goddard, Michael E; Yang, Jian; Visscher, Peter M

2015-11-01

Across-nation differences in the mean values for complex traits are common, but the reasons for these differences are unknown. Here we find that many independent loci contribute to population genetic differences in height and body mass index (BMI) in 9,416 individuals across 14 European countries. Using discovery data on over 250,000 individuals and unbiased effect size estimates from 17,500 sibling pairs, we estimate that 24% (95% credible interval (CI) = 9%, 41%) and 8% (95% CI = 4%, 16%) of the captured additive genetic variance for height and BMI, respectively, reflect population genetic differences. Population genetic divergence differed significantly from that in a null model (height, P < 3.94 × 10(-8); BMI, P < 5.95 × 10(-4)), and we find an among-population genetic correlation for tall and slender individuals (r = -0.80, 95% CI = -0.95, -0.60), consistent with correlated selection for both phenotypes. Observed differences in height among populations reflected the predicted genetic means (r = 0.51; P < 0.001), but environmental differences across Europe masked genetic differentiation for BMI (P < 0.58).

Adjoint acceleration of Monte Carlo simulations using TORT/MCNP coupling approach: a case study on the shielding improvement for the cyclotron room of the Buddhist Tzu Chi General Hospital.

PubMed

Sheu, R J; Sheu, R D; Jiang, S H; Kao, C H

2005-01-01

Full-scale Monte Carlo simulations of the cyclotron room of the Buddhist Tzu Chi General Hospital were carried out to improve the original inadequate maze design. Variance reduction techniques are indispensable in this study to facilitate the simulations for testing a variety of configurations of shielding modification. The TORT/MCNP manual coupling approach based on the Consistent Adjoint Driven Importance Sampling (CADIS) methodology has been used throughout this study. The CADIS utilises the source and transport biasing in a consistent manner. With this method, the computational efficiency was increased significantly by more than two orders of magnitude and the statistical convergence was also improved compared to the unbiased Monte Carlo run. This paper describes the shielding problem encountered, the procedure for coupling the TORT and MCNP codes to accelerate the calculations and the calculation results for the original and improved shielding designs. In order to verify the calculation results and seek additional accelerations, sensitivity studies on the space-dependent and energy-dependent parameters were also conducted.

Comparison of algorithms to generate event times conditional on time-dependent covariates.

PubMed

Sylvestre, Marie-Pierre; Abrahamowicz, Michal

2008-06-30

The Cox proportional hazards model with time-dependent covariates (TDC) is now a part of the standard statistical analysis toolbox in medical research. As new methods involving more complex modeling of time-dependent variables are developed, simulations could often be used to systematically assess the performance of these models. Yet, generating event times conditional on TDC requires well-designed and efficient algorithms. We compare two classes of such algorithms: permutational algorithms (PAs) and algorithms based on a binomial model. We also propose a modification of the PA to incorporate a rejection sampler. We performed a simulation study to assess the accuracy, stability, and speed of these algorithms in several scenarios. Both classes of algorithms generated data sets that, once analyzed, provided virtually unbiased estimates with comparable variances. In terms of computational efficiency, the PA with the rejection sampler reduced the time necessary to generate data by more than 50 per cent relative to alternative methods. The PAs also allowed more flexibility in the specification of the marginal distributions of event times and required less calibration.

Mapping of terrain by computer clustering techniques using multispectral scanner data and using color aerial film

NASA Technical Reports Server (NTRS)

Smedes, H. W.; Linnerud, H. J.; Woolaver, L. B.; Su, M. Y.; Jayroe, R. R.

1972-01-01

Two clustering techniques were used for terrain mapping by computer of test sites in Yellowstone National Park. One test was made with multispectral scanner data using a composite technique which consists of (1) a strictly sequential statistical clustering which is a sequential variance analysis, and (2) a generalized K-means clustering. In this composite technique, the output of (1) is a first approximation of the cluster centers. This is the input to (2) which consists of steps to improve the determination of cluster centers by iterative procedures. Another test was made using the three emulsion layers of color-infrared aerial film as a three-band spectrometer. Relative film densities were analyzed using a simple clustering technique in three-color space. Important advantages of the clustering technique over conventional supervised computer programs are (1) human intervention, preparation time, and manipulation of data are reduced, (2) the computer map, gives unbiased indication of where best to select the reference ground control data, (3) use of easy to obtain inexpensive film, and (4) the geometric distortions can be easily rectified by simple standard photogrammetric techniques.

Software engineering the mixed model for genome-wide association studies on large samples.

PubMed

Zhang, Zhiwu; Buckler, Edward S; Casstevens, Terry M; Bradbury, Peter J

2009-11-01

Mixed models improve the ability to detect phenotype-genotype associations in the presence of population stratification and multiple levels of relatedness in genome-wide association studies (GWAS), but for large data sets the resource consumption becomes impractical. At the same time, the sample size and number of markers used for GWAS is increasing dramatically, resulting in greater statistical power to detect those associations. The use of mixed models with increasingly large data sets depends on the availability of software for analyzing those models. While multiple software packages implement the mixed model method, no single package provides the best combination of fast computation, ability to handle large samples, flexible modeling and ease of use. Key elements of association analysis with mixed models are reviewed, including modeling phenotype-genotype associations using mixed models, population stratification, kinship and its estimation, variance component estimation, use of best linear unbiased predictors or residuals in place of raw phenotype, improving efficiency and software-user interaction. The available software packages are evaluated, and suggestions made for future software development.

Bayesian adaptive bandit-based designs using the Gittins index for multi-armed trials with normally distributed endpoints.

PubMed

Smith, Adam L; Villar, Sofía S

2018-01-01

Adaptive designs for multi-armed clinical trials have become increasingly popular recently because of their potential to shorten development times and to increase patient response. However, developing response-adaptive designs that offer patient-benefit while ensuring the resulting trial provides a statistically rigorous and unbiased comparison of the different treatments included is highly challenging. In this paper, the theory of Multi-Armed Bandit Problems is used to define near optimal adaptive designs in the context of a clinical trial with a normally distributed endpoint with known variance. We report the operating characteristics (type I error, power, bias) and patient-benefit of these approaches and alternative designs using simulation studies based on an ongoing trial. These results are then compared to those recently published in the context of Bernoulli endpoints. Many limitations and advantages are similar in both cases but there are also important differences, specially with respect to type I error control. This paper proposes a simulation-based testing procedure to correct for the observed type I error inflation that bandit-based and adaptive rules can induce.

Variance Estimation, Design Effects, and Sample Size Calculations for Respondent-Driven Sampling

PubMed Central

2006-01-01

Hidden populations, such as injection drug users and sex workers, are central to a number of public health problems. However, because of the nature of these groups, it is difficult to collect accurate information about them, and this difficulty complicates disease prevention efforts. A recently developed statistical approach called respondent-driven sampling improves our ability to study hidden populations by allowing researchers to make unbiased estimates of the prevalence of certain traits in these populations. Yet, not enough is known about the sample-to-sample variability of these prevalence estimates. In this paper, we present a bootstrap method for constructing confidence intervals around respondent-driven sampling estimates and demonstrate in simulations that it outperforms the naive method currently in use. We also use simulations and real data to estimate the design effects for respondent-driven sampling in a number of situations. We conclude with practical advice about the power calculations that are needed to determine the appropriate sample size for a study using respondent-driven sampling. In general, we recommend a sample size twice as large as would be needed under simple random sampling. PMID:16937083

Parametric adaptive filtering and data validation in the bar GW detector AURIGA

NASA Astrophysics Data System (ADS)

Ortolan, A.; Baggio, L.; Cerdonio, M.; Prodi, G. A.; Vedovato, G.; Vitale, S.

2002-04-01

We report on our experience gained in the signal processing of the resonant GW detector AURIGA. Signal amplitude and arrival time are estimated by means of a matched-adaptive Wiener filter. The detector noise, entering in the filter set-up, is modelled as a parametric ARMA process; to account for slow non-stationarity of the noise, the ARMA parameters are estimated on an hourly basis. A requirement of the set-up of an unbiased Wiener filter is the separation of time spans with 'almost Gaussian' noise from non-Gaussian and/or strongly non-stationary time spans. The separation algorithm consists basically of a variance estimate with the Chauvenet convergence method and a threshold on the Curtosis index. The subsequent validation of data is strictly connected with the separation procedure: in fact, by injecting a large number of artificial GW signals into the 'almost Gaussian' part of the AURIGA data stream, we have demonstrated that the effective probability distributions of the signal-to-noise ratio χ2 and the time of arrival are those that are expected.

Spectroscopic observation of SN 2017jzp and SN 2018bf by NUTS (NOT Un-biased Transient Survey)

NASA Astrophysics Data System (ADS)

Kuncarayakti, H.; Mattila, S.; Kotak, R.; Harmanen, J.; Reynolds, T.; Wyrzykowski, L.; Stritzinger, M.; Onori, F.; Somero, A.; Kangas, T.; Lundqvist, P.; Taddia, F.; Ergon, M.

2018-01-01

The Nordic Optical Telescope (NOT) Unbiased Transient Survey (NUTS; ATel #8992) reports the spectroscopic classification of SNe 2017jzp and 2018bf in host galaxies KUG 1326+679 and SDSS J225746.53+253833.5, respectively.

Spectroscopic observation of ASASSN-17nb and CSS170922:172546+342249 by NUTS (NOT Un-biased Transient Survey)

NASA Astrophysics Data System (ADS)

Harmanen, J.; Mattila, S.; Kuncarayakti, H.; Reynolds, T.; Somero, A.; Kangas, T.; Lundqvist, P.; Taddia, F.; Ergon, M.; Dong, S.; Pastorello, A.; Pursimo, T.; NUTS Collaboration

2017-10-01

The Nordic Optical Telescope (NOT) Unbiased Transient Survey (NUTS; ATel #8992) reports the spectroscopic classification of ASASSN-17nb in MCG+06-17-007 and CSS170922:172546+342249 in an unknown host galaxy.

Losing the rose tinted glasses: neural substrates of unbiased belief updating in depression

PubMed Central

Garrett, Neil; Sharot, Tali; Faulkner, Paul; Korn, Christoph W.; Roiser, Jonathan P.; Dolan, Raymond J.

2014-01-01

Recent evidence suggests that a state of good mental health is associated with biased processing of information that supports a positively skewed view of the future. Depression, on the other hand, is associated with unbiased processing of such information. Here, we use brain imaging in conjunction with a belief update task administered to clinically depressed patients and healthy controls to characterize brain activity that supports unbiased belief updating in clinically depressed individuals. Our results reveal that unbiased belief updating in depression is mediated by strong neural coding of estimation errors in response to both good news (in left inferior frontal gyrus and bilateral superior frontal gyrus) and bad news (in right inferior parietal lobule and right inferior frontal gyrus) regarding the future. In contrast, intact mental health was linked to a relatively attenuated neural coding of bad news about the future. These findings identify a neural substrate mediating the breakdown of biased updating in major depression disorder, which may be essential for mental health. PMID:25221492

Allowable SEM noise for unbiased LER measurement

NASA Astrophysics Data System (ADS)

Papavieros, George; Constantoudis, Vassilios; Gogolides, Evangelos

2018-03-01

Recently, a novel method for the calculation of unbiased Line Edge Roughness based on Power Spectral Density analysis has been proposed. In this paper first an alternative method is discussed and investigated, utilizing the Height-Height Correlation Function (HHCF) of edges. The HHCF-based method enables the unbiased determination of the whole triplet of LER parameters including besides rms the correlation length and roughness exponent. The key of both methods is the sensitivity of PSD and HHCF on noise at high frequencies and short distance respectively. Secondly, we elaborate a testbed of synthesized SEM images with controlled LER and noise to justify the effectiveness of the proposed unbiased methods. Our main objective is to find out the boundaries of the method in respect to noise levels and roughness characteristics, for which the method remains reliable, i.e the maximum amount of noise allowed, for which the output results cope with the controllable known inputs. At the same time, we will also set the extremes of roughness parameters for which the methods hold their accuracy.

The potential for increased power from combining P-values testing the same hypothesis.

PubMed

Ganju, Jitendra; Julie Ma, Guoguang

2017-02-01

The conventional approach to hypothesis testing for formal inference is to prespecify a single test statistic thought to be optimal. However, we usually have more than one test statistic in mind for testing the null hypothesis of no treatment effect but we do not know which one is the most powerful. Rather than relying on a single p-value, combining p-values from prespecified multiple test statistics can be used for inference. Combining functions include Fisher's combination test and the minimum p-value. Using randomization-based tests, the increase in power can be remarkable when compared with a single test and Simes's method. The versatility of the method is that it also applies when the number of covariates exceeds the number of observations. The increase in power is large enough to prefer combined p-values over a single p-value. The limitation is that the method does not provide an unbiased estimator of the treatment effect and does not apply to situations when the model includes treatment by covariate interaction.

Nonlinear vs. linear biasing in Trp-cage folding simulations

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

Spiwok, Vojtěch, E-mail: spiwokv@vscht.cz; Oborský, Pavel; Králová, Blanka

2015-03-21

Biased simulations have great potential for the study of slow processes, including protein folding. Atomic motions in molecules are nonlinear, which suggests that simulations with enhanced sampling of collective motions traced by nonlinear dimensionality reduction methods may perform better than linear ones. In this study, we compare an unbiased folding simulation of the Trp-cage miniprotein with metadynamics simulations using both linear (principle component analysis) and nonlinear (Isomap) low dimensional embeddings as collective variables. Folding of the mini-protein was successfully simulated in 200 ns simulation with linear biasing and non-linear motion biasing. The folded state was correctly predicted as the free energymore » minimum in both simulations. We found that the advantage of linear motion biasing is that it can sample a larger conformational space, whereas the advantage of nonlinear motion biasing lies in slightly better resolution of the resulting free energy surface. In terms of sampling efficiency, both methods are comparable.« less

The use of clinical CCT images in the forensic examination of closed head injuries.

PubMed

Bauer, M; Polzin, S; Patzelt, D

2004-04-01

The forensic evaluation of clinical cranial computed tomographies (CCT) frequently is the only reliable source of morphological evidence in head injuries when the injured individual survives or when death is delayed and autopsy findings are characterized by secondary changes. We have reviewed 21 cases where clinical CCT examinations were used to establish a medico-legal diagnosis. In 18 cases falls (n = 13) could be distinguished from blows (n = 5) due to the presence and/or absence of coup and contrecoup lesions and linear or depressed skull fractures. In two cases the striking object could be identified by digital superimposition. The minimum number of blows could be determined in 1 case. Only in 3 remaining cases the results were inconclusive. In our experience, CCT scans provide an important source of information for the forensic expert. To have unbiased access to these information, it is useful to evaluate the CT scans personally which requires a basic knowledge of traumatic changes found on radiographs.

Comparing least-squares and quantile regression approaches to analyzing median hospital charges.

PubMed

Olsen, Cody S; Clark, Amy E; Thomas, Andrea M; Cook, Lawrence J

2012-07-01

Emergency department (ED) and hospital charges obtained from administrative data sets are useful descriptors of injury severity and the burden to EDs and the health care system. However, charges are typically positively skewed due to costly procedures, long hospital stays, and complicated or prolonged treatment for few patients. The median is not affected by extreme observations and is useful in describing and comparing distributions of hospital charges. A least-squares analysis employing a log transformation is one approach for estimating median hospital charges, corresponding confidence intervals (CIs), and differences between groups; however, this method requires certain distributional properties. An alternate method is quantile regression, which allows estimation and inference related to the median without making distributional assumptions. The objective was to compare the log-transformation least-squares method to the quantile regression approach for estimating median hospital charges, differences in median charges between groups, and associated CIs. The authors performed simulations using repeated sampling of observed statewide ED and hospital charges and charges randomly generated from a hypothetical lognormal distribution. The median and 95% CI and the multiplicative difference between the median charges of two groups were estimated using both least-squares and quantile regression methods. Performance of the two methods was evaluated. In contrast to least squares, quantile regression produced estimates that were unbiased and had smaller mean square errors in simulations of observed ED and hospital charges. Both methods performed well in simulations of hypothetical charges that met least-squares method assumptions. When the data did not follow the assumed distribution, least-squares estimates were often biased, and the associated CIs had lower than expected coverage as sample size increased. Quantile regression analyses of hospital charges provide unbiased estimates even when lognormal and equal variance assumptions are violated. These methods may be particularly useful in describing and analyzing hospital charges from administrative data sets. © 2012 by the Society for Academic Emergency Medicine.

Accessing evidence to inform public health policy: a study to enhance advocacy.

PubMed

Tabak, R G; Eyler, A A; Dodson, E A; Brownson, R C

2015-06-01

Improving population health often involves policy changes that are the result of complex advocacy efforts. Information exchanges among researchers, advocates, and policymakers is paramount to policy interventions to improve health outcomes. This information may include evidence on what works well for whom and cost-effective strategies to improve outcomes of interest. However, this information is not always readily available or easily communicated. The purposes of this paper are to describe ways advocates seek information for health policy advocacy and to compare advocate demographics. Cross-sectional telephone survey. Seventy-seven state-level advocates were asked about the desirable characteristics of policy-relevant information including methods of obtaining information, what makes it useful, and what sources make evidence most reliable/trustworthy. Responses were explored for the full sample and variety of subsamples (i.e. gender, age, and position on social and fiscal issues). Differences between groups were tested using t-tests and one-way analysis of variance. On average, advocates rated frequency of seeking research information as 4.3 out of five. Overall, advocates rated the Internet as the top source, rated unbiased research and research with relevancy to their organization as the most important characteristics, and considered information from their organization as most reliable/believable. When ratings were examined by subgroup, the two characteristics most important for each question in the total sample (listed above) emerged as most important for nearly all subgroups. Advocates are a resource to policymakers on health topics in the policy process. This study, among the first of its kind, found that advocates seek research information, but have a need for evidence that is unbiased and relevant to their organizations and report that university-based information is reliable. Researchers and advocates should partner so research is useful in advocating for evidence-based policy change. Copyright © 2015 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

A spatially explicit capture-recapture estimator for single-catch traps.

PubMed

Distiller, Greg; Borchers, David L

2015-11-01

Single-catch traps are frequently used in live-trapping studies of small mammals. Thus far, a likelihood for single-catch traps has proven elusive and usually the likelihood for multicatch traps is used for spatially explicit capture-recapture (SECR) analyses of such data. Previous work found the multicatch likelihood to provide a robust estimator of average density. We build on a recently developed continuous-time model for SECR to derive a likelihood for single-catch traps. We use this to develop an estimator based on observed capture times and compare its performance by simulation to that of the multicatch estimator for various scenarios with nonconstant density surfaces. While the multicatch estimator is found to be a surprisingly robust estimator of average density, its performance deteriorates with high trap saturation and increasing density gradients. Moreover, it is found to be a poor estimator of the height of the detection function. By contrast, the single-catch estimators of density, distribution, and detection function parameters are found to be unbiased or nearly unbiased in all scenarios considered. This gain comes at the cost of higher variance. If there is no interest in interpreting the detection function parameters themselves, and if density is expected to be fairly constant over the survey region, then the multicatch estimator performs well with single-catch traps. However if accurate estimation of the detection function is of interest, or if density is expected to vary substantially in space, then there is merit in using the single-catch estimator when trap saturation is above about 60%. The estimator's performance is improved if care is taken to place traps so as to span the range of variables that affect animal distribution. As a single-catch likelihood with unknown capture times remains intractable for now, researchers using single-catch traps should aim to incorporate timing devices with their traps.

Optimal portfolio strategy with cross-correlation matrix composed by DCCA coefficients: Evidence from the Chinese stock market

NASA Astrophysics Data System (ADS)

Sun, Xuelian; Liu, Zixian

2016-02-01

In this paper, a new estimator of correlation matrix is proposed, which is composed of the detrended cross-correlation coefficients (DCCA coefficients), to improve portfolio optimization. In contrast to Pearson's correlation coefficients (PCC), DCCA coefficients acquired by the detrended cross-correlation analysis (DCCA) method can describe the nonlinear correlation between assets, and can be decomposed in different time scales. These properties of DCCA make it possible to improve the investment effect and more valuable to investigate the scale behaviors of portfolios. The minimum variance portfolio (MVP) model and the Mean-Variance (MV) model are used to evaluate the effectiveness of this improvement. Stability analysis shows the effect of two kinds of correlation matrices on the estimation error of portfolio weights. The observed scale behaviors are significant to risk management and could be used to optimize the portfolio selection.

Demodulation of messages received with low signal to noise ratio

NASA Astrophysics Data System (ADS)

Marguinaud, A.; Quignon, T.; Romann, B.

The implementation of this all-digital demodulator is derived from maximum likelihood considerations applied to an analytical representation of the received signal. Traditional adapted filters and phase lock loops are replaced by minimum variance estimators and hypothesis tests. These statistical tests become very simple when working on phase signal. These methods, combined with rigorous control data representation allow significant computation savings as compared to conventional realizations. Nominal operation has been verified down to energetic signal over noise of -3 dB upon a QPSK demodulator.

An adaptive technique for estimating the atmospheric density profile during the AE mission

NASA Technical Reports Server (NTRS)

Argentiero, P.

1973-01-01

A technique is presented for processing accelerometer data obtained during the AE missions in order to estimate the atmospheric density profile. A minimum variance, adaptive filter is utilized. The trajectory of the probe and probe parameters are in a consider mode where their estimates are unimproved but their associated uncertainties are permitted an impact on filter behavior. Simulations indicate that the technique is effective in estimating a density profile to within a few percentage points.

Real-time performance assessment and adaptive control for a water chiller unit in an HVAC system

NASA Astrophysics Data System (ADS)

Bai, Jianbo; Li, Yang; Chen, Jianhao

2018-02-01

The paper proposes an adaptive control method for a water chiller unit in a HVAC system. Based on the minimum variance evaluation, the adaptive control method was used to realize better control of the water chiller unit. To verify the performance of the adaptive control method, the proposed method was compared with an a conventional PID controller, the simulation results showed that adaptive control method had superior control performance to that of the conventional PID controller.

Optimization of data analysis for the in vivo neutron activation analysis of aluminum in bone.

PubMed

Mohseni, H K; Matysiak, W; Chettle, D R; Byun, S H; Priest, N; Atanackovic, J; Prestwich, W V

2016-10-01

An existing system at McMaster University has been used for the in vivo measurement of aluminum in human bone. Precise and detailed analysis approaches are necessary to determine the aluminum concentration because of the low levels of aluminum found in the bone and the challenges associated with its detection. Phantoms resembling the composition of the human hand with varying concentrations of aluminum were made for testing the system prior to the application to human studies. A spectral decomposition model and a photopeak fitting model involving the inverse-variance weighted mean and a time-dependent analysis were explored to analyze the results and determine the model with the best performance and lowest minimum detection limit. The results showed that the spectral decomposition and the photopeak fitting model with the inverse-variance weighted mean both provided better results compared to the other methods tested. The spectral decomposition method resulted in a marginally lower detection limit (5μg Al/g Ca) compared to the inverse-variance weighted mean (5.2μg Al/g Ca), rendering both equally applicable to human measurements. Copyright © 2016 Elsevier Ltd. All rights reserved.

Uncertainty in Population Estimates for Endangered Animals and Improving the Recovery Process.

PubMed

Haines, Aaron M; Zak, Matthew; Hammond, Katie; Scott, J Michael; Goble, Dale D; Rachlow, Janet L

2013-08-13

United States recovery plans contain biological information for a species listed under the Endangered Species Act and specify recovery criteria to provide basis for species recovery. The objective of our study was to evaluate whether recovery plans provide uncertainty (e.g., variance) with estimates of population size. We reviewed all finalized recovery plans for listed terrestrial vertebrate species to record the following data: (1) if a current population size was given, (2) if a measure of uncertainty or variance was associated with current estimates of population size and (3) if population size was stipulated for recovery. We found that 59% of completed recovery plans specified a current population size, 14.5% specified a variance for the current population size estimate and 43% specified population size as a recovery criterion. More recent recovery plans reported more estimates of current population size, uncertainty and population size as a recovery criterion. Also, bird and mammal recovery plans reported more estimates of population size and uncertainty compared to reptiles and amphibians. We suggest the use of calculating minimum detectable differences to improve confidence when delisting endangered animals and we identified incentives for individuals to get involved in recovery planning to improve access to quantitative data.

Lekking without a paradox in the buff-breasted sandpiper

USGS Publications Warehouse

Lanctot, Richard B.; Scribner, Kim T.; Kempenaers, Bart; Weatherhead, Patrick J.

1997-01-01

Females in lek‐breeding species appear to copulate with a small subset of the available males. Such strong directional selection is predicted to decrease additive genetic variance in the preferred male traits, yet females continue to mate selectively, thus generating the lek paradox. In a study of buff‐breasted sandpipers (Tryngites subruficollis), we combine detailed behavioral observations with paternity analyses using single‐locus minisatellite DNA probes to provide the first evidence from a lek‐breeding species that the variance in male reproductive success is much lower than expected. In 17 and 30 broods sampled in two consecutive years, a minimum of 20 and 39 males, respectively, sired offspring. This low variance in male reproductive success resulted from effective use of alternative reproductive tactics by males, females mating with solitary males off leks, and multiple mating by females. Thus, the results of this study suggests that sexual selection through female choice is weak in buff‐breasted sandpipers. The behavior of other lek‐breeding birds is sufficiently similar to that of buff‐breasted sandpipers that paternity studies of those species should be conducted to determine whether leks generally are less paradoxical than they appear.

Aircrew coordination and decisionmaking: Peer ratings of video tapes made during a full mission simulation

NASA Technical Reports Server (NTRS)

Murphy, M. R.; Awe, C. A.

1986-01-01

Six professionally active, retired captains rated the coordination and decisionmaking performances of sixteen aircrews while viewing videotapes of a simulated commercial air transport operation. The scenario featured a required diversion and a probable minimum fuel situation. Seven point Likert-type scales were used in rating variables on the basis of a model of crew coordination and decisionmaking. The variables were based on concepts of, for example, decision difficulty, efficiency, and outcome quality; and leader-subordin ate concepts such as person and task-oriented leader behavior, and competency motivation of subordinate crewmembers. Five-front-end variables of the model were in turn dependent variables for a hierarchical regression procedure. The variance in safety performance was explained 46%, by decision efficiency, command reversal, and decision quality. The variance of decision quality, an alternative substantive dependent variable to safety performance, was explained 60% by decision efficiency and the captain's quality of within-crew communications. The variance of decision efficiency, crew coordination, and command reversal were in turn explained 78%, 80%, and 60% by small numbers of preceding independent variables. A principle component, varimax factor analysis supported the model structure suggested by regression analyses.

Signal-dependent noise determines motor planning

NASA Astrophysics Data System (ADS)

Harris, Christopher M.; Wolpert, Daniel M.

1998-08-01

When we make saccadic eye movements or goal-directed arm movements, there is an infinite number of possible trajectories that the eye or arm could take to reach the target,. However, humans show highly stereotyped trajectories in which velocity profiles of both the eye and hand are smooth and symmetric for brief movements,. Here we present a unifying theory of eye and arm movements based on the single physiological assumption that the neural control signals are corrupted by noise whose variance increases with the size of the control signal. We propose that in the presence of such signal-dependent noise, the shape of a trajectory is selected to minimize the variance of the final eye or arm position. This minimum-variance theory accurately predicts the trajectories of both saccades and arm movements and the speed-accuracy trade-off described by Fitt's law. These profiles are robust to changes in the dynamics of the eye or arm, as found empirically,. Moreover, the relation between path curvature and hand velocity during drawing movements reproduces the empirical `two-thirds power law',. This theory provides a simple and powerful unifying perspective for both eye and arm movement control.

Block-circulant matrices with circulant blocks, Weil sums, and mutually unbiased bases. II. The prime power case

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

Combescure, Monique

2009-03-15

In our previous paper [Combescure, M., 'Circulant matrices, Gauss sums and the mutually unbiased bases. I. The prime number case', Cubo A Mathematical Journal (unpublished)] we have shown that the theory of circulant matrices allows to recover the result that there exists p+1 mutually unbiased bases in dimension p, p being an arbitrary prime number. Two orthonormal bases B, B{sup '} of C{sup d} are said mutually unbiased if for all b(set-membership sign)B, for all b{sup '}(set-membership sign)B{sup '} one has that |b{center_dot}b{sup '}|=1/{radical}(d) (b{center_dot}b{sup '} Hermitian scalar product in C{sup d}). In this paper we show that the theorymore » of block-circulant matrices with circulant blocks allows to show very simply the known result that if d=p{sup n} (p a prime number and n any integer) there exists d+1 mutually unbiased bases in C{sup d}. Our result relies heavily on an idea of Klimov et al. [''Geometrical approach to the discrete Wigner function,'' J. Phys. A 39, 14471 (2006)]. As a subproduct we recover properties of quadratic Weil sums for p{>=}3, which generalizes the fact that in the prime case the quadratic Gauss sum properties follow from our results.« less

Examinations of tRNA Range of Motion Using Simulations of Cryo-EM Microscopy and X-Ray Data

PubMed Central

Caulfield, Thomas R.; Devkota, Batsal; Rollins, Geoffrey C.

2011-01-01

We examined tRNA flexibility using a combination of steered and unbiased molecular dynamics simulations. Using Maxwell's demon algorithm, molecular dynamics was used to steer X-ray structure data toward that from an alternative state obtained from cryogenic-electron microscopy density maps. Thus, we were able to fit X-ray structures of tRNA onto cryogenic-electron microscopy density maps for hybrid states of tRNA. Additionally, we employed both Maxwell's demon molecular dynamics simulations and unbiased simulation methods to identify possible ribosome-tRNA contact areas where the ribosome may discriminate tRNAs during translation. Herein, we collected >500 ns of simulation data to assess the global range of motion for tRNAs. Biased simulations can be used to steer between known conformational stop points, while unbiased simulations allow for a general testing of conformational space previously unexplored. The unbiased molecular dynamics data describes the global conformational changes of tRNA on a sub-microsecond time scale for comparison with steered data. Additionally, the unbiased molecular dynamics data was used to identify putative contacts between tRNA and the ribosome during the accommodation step of translation. We found that the primary contact regions were H71 and H92 of the 50S subunit and ribosomal proteins L14 and L16. PMID:21716650

Definition and Measurement of Selection Bias: From Constant Ratio to Constant Difference

ERIC Educational Resources Information Center

Cahan, Sorel; Gamliel, Eyal

2006-01-01

Despite its intuitive appeal and popularity, Thorndike's constant ratio (CR) model for unbiased selection is inherently inconsistent in "n"-free selection. Satisfaction of the condition for unbiased selection, when formulated in terms of success/acceptance probabilities, usually precludes satisfaction by the converse probabilities of…

Spectroscopic observation of Gaia17dht and Gaia17diu by NUTS (NOT Un-biased Transient Survey)

NASA Astrophysics Data System (ADS)

Fraser, M.; Dyrbye, S.; Cappella, E.

2017-12-01

The Nordic Optical Telescope (NOT) Unbiased Transient Survey (NUTS; ATel #8992) reports the spectroscopic classification of Gaia17dht/SN2017izz and Gaia17diu/SN2017jdb (in host galaxies SDSS J145121.24+283521.6 and LEDA 2753585 respectively).

Generalized approach for using unbiased symmetric metrics with negative values: normalized mean bias factor and normalized mean absolute error factor

EPA Science Inventory

Unbiased symmetric metrics provide a useful measure to quickly compare two datasets, with similar interpretations for both under and overestimations. Two examples include the normalized mean bias factor and normalized mean absolute error factor. However, the original formulations...

A method for energy window optimization for quantitative tasks that includes the effects of model-mismatch on bias: application to Y-90 bremsstrahlung SPECT imaging.

PubMed

Rong, Xing; Du, Yong; Frey, Eric C

2012-06-21

Quantitative Yttrium-90 ((90)Y) bremsstrahlung single photon emission computed tomography (SPECT) imaging has shown great potential to provide reliable estimates of (90)Y activity distribution for targeted radionuclide therapy dosimetry applications. One factor that potentially affects the reliability of the activity estimates is the choice of the acquisition energy window. In contrast to imaging conventional gamma photon emitters where the acquisition energy windows are usually placed around photopeaks, there has been great variation in the choice of the acquisition energy window for (90)Y imaging due to the continuous and broad energy distribution of the bremsstrahlung photons. In quantitative imaging of conventional gamma photon emitters, previous methods for optimizing the acquisition energy window assumed unbiased estimators and used the variance in the estimates as a figure of merit (FOM). However, for situations, such as (90)Y imaging, where there are errors in the modeling of the image formation process used in the reconstruction there will be bias in the activity estimates. In (90)Y bremsstrahlung imaging this will be especially important due to the high levels of scatter, multiple scatter, and collimator septal penetration and scatter. Thus variance will not be a complete measure of reliability of the estimates and thus is not a complete FOM. To address this, we first aimed to develop a new method to optimize the energy window that accounts for both the bias due to model-mismatch and the variance of the activity estimates. We applied this method to optimize the acquisition energy window for quantitative (90)Y bremsstrahlung SPECT imaging in microsphere brachytherapy. Since absorbed dose is defined as the absorbed energy from the radiation per unit mass of tissues in this new method we proposed a mass-weighted root mean squared error of the volume of interest (VOI) activity estimates as the FOM. To calculate this FOM, two analytical expressions were derived for calculating the bias due to model-mismatch and the variance of the VOI activity estimates, respectively. To obtain the optimal acquisition energy window for general situations of interest in clinical (90)Y microsphere imaging, we generated phantoms with multiple tumors of various sizes and various tumor-to-normal activity concentration ratios using a digital phantom that realistically simulates human anatomy, simulated (90)Y microsphere imaging with a clinical SPECT system and typical imaging parameters using a previously validated Monte Carlo simulation code, and used a previously proposed method for modeling the image degrading effects in quantitative SPECT reconstruction. The obtained optimal acquisition energy window was 100-160 keV. The values of the proposed FOM were much larger than the FOM taking into account only the variance of the activity estimates, thus demonstrating in our experiment that the bias of the activity estimates due to model-mismatch was a more important factor than the variance in terms of limiting the reliability of activity estimates.

3D facial landmarks: Inter-operator variability of manual annotation

PubMed Central

2014-01-01

Background Manual annotation of landmarks is a known source of variance, which exist in all fields of medical imaging, influencing the accuracy and interpretation of the results. However, the variability of human facial landmarks is only sparsely addressed in the current literature as opposed to e.g. the research fields of orthodontics and cephalometrics. We present a full facial 3D annotation procedure and a sparse set of manually annotated landmarks, in effort to reduce operator time and minimize the variance. Method Facial scans from 36 voluntary unrelated blood donors from the Danish Blood Donor Study was randomly chosen. Six operators twice manually annotated 73 anatomical and pseudo-landmarks, using a three-step scheme producing a dense point correspondence map. We analyzed both the intra- and inter-operator variability, using mixed-model ANOVA. We then compared four sparse sets of landmarks in order to construct a dense correspondence map of the 3D scans with a minimum point variance. Results The anatomical landmarks of the eye were associated with the lowest variance, particularly the center of the pupils. Whereas points of the jaw and eyebrows have the highest variation. We see marginal variability in regards to intra-operator and portraits. Using a sparse set of landmarks (n=14), that capture the whole face, the dense point mean variance was reduced from 1.92 to 0.54 mm. Conclusion The inter-operator variability was primarily associated with particular landmarks, where more leniently landmarks had the highest variability. The variables embedded in the portray and the reliability of a trained operator did only have marginal influence on the variability. Further, using 14 of the annotated landmarks we were able to reduced the variability and create a dense correspondences mesh to capture all facial features. PMID:25306436

How good is crude MDL for solving the bias-variance dilemma? An empirical investigation based on Bayesian networks.

PubMed

Cruz-Ramírez, Nicandro; Acosta-Mesa, Héctor Gabriel; Mezura-Montes, Efrén; Guerra-Hernández, Alejandro; Hoyos-Rivera, Guillermo de Jesús; Barrientos-Martínez, Rocío Erandi; Gutiérrez-Fragoso, Karina; Nava-Fernández, Luis Alonso; González-Gaspar, Patricia; Novoa-del-Toro, Elva María; Aguilera-Rueda, Vicente Josué; Ameca-Alducin, María Yaneli

2014-01-01

The bias-variance dilemma is a well-known and important problem in Machine Learning. It basically relates the generalization capability (goodness of fit) of a learning method to its corresponding complexity. When we have enough data at hand, it is possible to use these data in such a way so as to minimize overfitting (the risk of selecting a complex model that generalizes poorly). Unfortunately, there are many situations where we simply do not have this required amount of data. Thus, we need to find methods capable of efficiently exploiting the available data while avoiding overfitting. Different metrics have been proposed to achieve this goal: the Minimum Description Length principle (MDL), Akaike's Information Criterion (AIC) and Bayesian Information Criterion (BIC), among others. In this paper, we focus on crude MDL and empirically evaluate its performance in selecting models with a good balance between goodness of fit and complexity: the so-called bias-variance dilemma, decomposition or tradeoff. Although the graphical interaction between these dimensions (bias and variance) is ubiquitous in the Machine Learning literature, few works present experimental evidence to recover such interaction. In our experiments, we argue that the resulting graphs allow us to gain insights that are difficult to unveil otherwise: that crude MDL naturally selects balanced models in terms of bias-variance, which not necessarily need be the gold-standard ones. We carry out these experiments using a specific model: a Bayesian network. In spite of these motivating results, we also should not overlook three other components that may significantly affect the final model selection: the search procedure, the noise rate and the sample size.

How Good Is Crude MDL for Solving the Bias-Variance Dilemma? An Empirical Investigation Based on Bayesian Networks

PubMed Central

Cruz-Ramírez, Nicandro; Acosta-Mesa, Héctor Gabriel; Mezura-Montes, Efrén; Guerra-Hernández, Alejandro; Hoyos-Rivera, Guillermo de Jesús; Barrientos-Martínez, Rocío Erandi; Gutiérrez-Fragoso, Karina; Nava-Fernández, Luis Alonso; González-Gaspar, Patricia; Novoa-del-Toro, Elva María; Aguilera-Rueda, Vicente Josué; Ameca-Alducin, María Yaneli

2014-01-01

The bias-variance dilemma is a well-known and important problem in Machine Learning. It basically relates the generalization capability (goodness of fit) of a learning method to its corresponding complexity. When we have enough data at hand, it is possible to use these data in such a way so as to minimize overfitting (the risk of selecting a complex model that generalizes poorly). Unfortunately, there are many situations where we simply do not have this required amount of data. Thus, we need to find methods capable of efficiently exploiting the available data while avoiding overfitting. Different metrics have been proposed to achieve this goal: the Minimum Description Length principle (MDL), Akaike’s Information Criterion (AIC) and Bayesian Information Criterion (BIC), among others. In this paper, we focus on crude MDL and empirically evaluate its performance in selecting models with a good balance between goodness of fit and complexity: the so-called bias-variance dilemma, decomposition or tradeoff. Although the graphical interaction between these dimensions (bias and variance) is ubiquitous in the Machine Learning literature, few works present experimental evidence to recover such interaction. In our experiments, we argue that the resulting graphs allow us to gain insights that are difficult to unveil otherwise: that crude MDL naturally selects balanced models in terms of bias-variance, which not necessarily need be the gold-standard ones. We carry out these experiments using a specific model: a Bayesian network. In spite of these motivating results, we also should not overlook three other components that may significantly affect the final model selection: the search procedure, the noise rate and the sample size. PMID:24671204

Estimation of stable boundary-layer height using variance processing of backscatter lidar data

NASA Astrophysics Data System (ADS)

Saeed, Umar; Rocadenbosch, Francesc

2017-04-01

Stable boundary layer (SBL) is one of the most complex and less understood topics in atmospheric science. The type and height of the SBL is an important parameter for several applications such as understanding the formation of haze fog, and accuracy of chemical and pollutant dispersion models, etc. [1]. This work addresses nocturnal Stable Boundary-Layer Height (SBLH) estimation by using variance processing and attenuated backscatter lidar measurements, its principles and limitations. It is shown that temporal and spatial variance profiles of the attenuated backscatter signal are related to the stratification of aerosols in the SBL. A minimum variance SBLH estimator using local minima in the variance profiles of backscatter lidar signals is introduced. The method is validated using data from HD(CP)2 Observational Prototype Experiment (HOPE) campaign at Jülich, Germany [2], under different atmospheric conditions. This work has received funding from the European Union Seventh Framework Programme, FP7 People, ITN Marie Curie Actions Programme (2012-2016) in the frame of ITaRS project (GA 289923), H2020 programme under ACTRIS-2 project (GA 654109), the Spanish Ministry of Economy and Competitiveness - European Regional Development Funds under TEC2015-63832-P project, and from the Generalitat de Catalunya (Grup de Recerca Consolidat) 2014-SGR-583. [1] R. B. Stull, An Introduction to Boundary Layer Meteorology, chapter 12, Stable Boundary Layer, pp. 499-543, Springer, Netherlands, 1988. [2] U. Löhnert, J. H. Schween, C. Acquistapace, K. Ebell, M. Maahn, M. Barrera-Verdejo, A. Hirsikko, B. Bohn, A. Knaps, E. O'Connor, C. Simmer, A. Wahner, and S. Crewell, "JOYCE: Jülich Observatory for Cloud Evolution," Bull. Amer. Meteor. Soc., vol. 96, no. 7, pp. 1157-1174, 2015.

Mutually unbiased bases in six dimensions: The four most distant bases

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

Raynal, Philippe; Lue Xin; Englert, Berthold-Georg

2011-06-15

We consider the average distance between four bases in six dimensions. The distance between two orthonormal bases vanishes when the bases are the same, and the distance reaches its maximal value of unity when the bases are unbiased. We perform a numerical search for the maximum average distance and find it to be strictly smaller than unity. This is strong evidence that no four mutually unbiased bases exist in six dimensions. We also provide a two-parameter family of three bases which, together with the canonical basis, reach the numerically found maximum of the average distance, and we conduct a detailedmore » study of the structure of the extremal set of bases.« less

Automatic quantification of mammary glands on non-contrast x-ray CT by using a novel segmentation approach

NASA Astrophysics Data System (ADS)

Zhou, Xiangrong; Kano, Takuya; Cai, Yunliang; Li, Shuo; Zhou, Xinxin; Hara, Takeshi; Yokoyama, Ryujiro; Fujita, Hiroshi

2016-03-01

This paper describes a brand new automatic segmentation method for quantifying volume and density of mammary gland regions on non-contrast CT images. The proposed method uses two processing steps: (1) breast region localization, and (2) breast region decomposition to accomplish a robust mammary gland segmentation task on CT images. The first step detects two minimum bounding boxes of left and right breast regions, respectively, based on a machine-learning approach that adapts to a large variance of the breast appearances on different age levels. The second step divides the whole breast region in each side into mammary gland, fat tissue, and other regions by using spectral clustering technique that focuses on intra-region similarities of each patient and aims to overcome the image variance caused by different scan-parameters. The whole approach is designed as a simple structure with very minimum number of parameters to gain a superior robustness and computational efficiency for real clinical setting. We applied this approach to a dataset of 300 CT scans, which are sampled with the equal number from 30 to 50 years-old-women. Comparing to human annotations, the proposed approach can measure volume and quantify distributions of the CT numbers of mammary gland regions successfully. The experimental results demonstrated that the proposed approach achieves results consistent with manual annotations. Through our proposed framework, an efficient and effective low cost clinical screening scheme may be easily implemented to predict breast cancer risk, especially on those already acquired scans.

Minimum Required Attention: A Human-Centered Approach to Driver Inattention.

PubMed

Kircher, Katja; Ahlstrom, Christer

2017-05-01

To propose a driver attention theory based on the notion of driving as a satisficing and partially self-paced task and, within this framework, present a definition for driver inattention. Many definitions of driver inattention and distraction have been proposed, but they are difficult to operationalize, and they are either unreasonably strict and inflexible or suffer from hindsight bias. Existing definitions of driver distraction are reviewed and their shortcomings identified. We then present the minimum required attention (MiRA) theory to overcome these shortcomings. Suggestions on how to operationalize MiRA are also presented. MiRA describes which role the attention of the driver plays in the shared "situation awareness of the traffic system." A driver is considered attentive when sampling sufficient information to meet the demands of the system, namely, that he or she fulfills the preconditions to be able to form and maintain a good enough mental representation of the situation. A driver should only be considered inattentive when information sampling is not sufficient, regardless of whether the driver is concurrently executing an additional task or not. The MiRA theory builds on well-established driver attention theories. It goes beyond available driver distraction definitions by first defining what a driver needs to be attentive to, being free from hindsight bias, and allowing the driver to adapt to the current demands of the traffic situation through satisficing and self-pacing. MiRA has the potential to provide the stepping stone for unbiased and operationalizable inattention detection and classification.

Influence of Layer Thickness, Raster Angle, Deformation Temperature and Recovery Temperature on the Shape-Memory Effect of 3D-Printed Polylactic Acid Samples

PubMed Central

Wu, Wenzheng; Ye, Wenli; Wu, Zichao; Geng, Peng; Wang, Yulei; Zhao, Ji

2017-01-01

The success of the 3D-printing process depends upon the proper selection of process parameters. However, the majority of current related studies focus on the influence of process parameters on the mechanical properties of the parts. The influence of process parameters on the shape-memory effect has been little studied. This study used the orthogonal experimental design method to evaluate the influence of the layer thickness H, raster angle θ, deformation temperature Td and recovery temperature Tr on the shape-recovery ratio Rr and maximum shape-recovery rate Vm of 3D-printed polylactic acid (PLA). The order and contribution of every experimental factor on the target index were determined by range analysis and ANOVA, respectively. The experimental results indicated that the recovery temperature exerted the greatest effect with a variance ratio of 416.10, whereas the layer thickness exerted the smallest effect on the shape-recovery ratio with a variance ratio of 4.902. The recovery temperature exerted the most significant effect on the maximum shape-recovery rate with the highest variance ratio of 1049.50, whereas the raster angle exerted the minimum effect with a variance ratio of 27.163. The results showed that the shape-memory effect of 3D-printed PLA parts depended strongly on recovery temperature, and depended more weakly on the deformation temperature and 3D-printing parameters. PMID:28825617

Genetic parameters of legendre polynomials for first parity lactation curves.

PubMed

Pool, M H; Janss, L L; Meuwissen, T H

2000-11-01

Variance components of the covariance function coefficients in a random regression test-day model were estimated by Legendre polynomials up to a fifth order for first-parity records of Dutch dairy cows using Gibbs sampling. Two Legendre polynomials of equal order were used to model the random part of the lactation curve, one for the genetic component and one for permanent environment. Test-day records from cows registered between 1990 to 1996 and collected by regular milk recording were available. For the data set, 23,700 complete lactations were selected from 475 herds sired by 262 sires. Because the application of a random regression model is limited by computing capacity, we investigated the minimum order needed to fit the variance structure in the data sufficiently. Predictions of genetic and permanent environmental variance structures were compared with bivariate estimates on 30-d intervals. A third-order or higher polynomial modeled the shape of variance curves over DIM with sufficient accuracy for the genetic and permanent environment part. Also, the genetic correlation structure was fitted with sufficient accuracy by a third-order polynomial, but, for the permanent environmental component, a fourth order was needed. Because equal orders are suggested in the literature, a fourth-order Legendre polynomial is recommended in this study. However, a rank of three for the genetic covariance matrix and of four for permanent environment allows a simpler covariance function with a reduced number of parameters based on the eigenvalues and eigenvectors.

Estimating Unbiased Treatment Effects in Education Using a Regression Discontinuity Design

ERIC Educational Resources Information Center

Smith, William C.

2014-01-01

The ability of regression discontinuity (RD) designs to provide an unbiased treatment effect while overcoming the ethical concerns plagued by Random Control Trials (RCTs) make it a valuable and useful approach in education evaluation. RD is the only explicitly recognized quasi-experimental approach identified by the Institute of Education…

Rare Event Simulation in Radiation Transport

NASA Astrophysics Data System (ADS)

Kollman, Craig

This dissertation studies methods for estimating extremely small probabilities by Monte Carlo simulation. Problems in radiation transport typically involve estimating very rare events or the expected value of a random variable which is with overwhelming probability equal to zero. These problems often have high dimensional state spaces and irregular geometries so that analytic solutions are not possible. Monte Carlo simulation must be used to estimate the radiation dosage being transported to a particular location. If the area is well shielded the probability of any one particular particle getting through is very small. Because of the large number of particles involved, even a tiny fraction penetrating the shield may represent an unacceptable level of radiation. It therefore becomes critical to be able to accurately estimate this extremely small probability. Importance sampling is a well known technique for improving the efficiency of rare event calculations. Here, a new set of probabilities is used in the simulation runs. The results are multiplied by the likelihood ratio between the true and simulated probabilities so as to keep our estimator unbiased. The variance of the resulting estimator is very sensitive to which new set of transition probabilities are chosen. It is shown that a zero variance estimator does exist, but that its computation requires exact knowledge of the solution. A simple random walk with an associated killing model for the scatter of neutrons is introduced. Large deviation results for optimal importance sampling in random walks are extended to the case where killing is present. An adaptive "learning" algorithm for implementing importance sampling is given for more general Markov chain models of neutron scatter. For finite state spaces this algorithm is shown to give, with probability one, a sequence of estimates converging exponentially fast to the true solution. In the final chapter, an attempt to generalize this algorithm to a continuous state space is made. This involves partitioning the space into a finite number of cells. There is a tradeoff between additional computation per iteration and variance reduction per iteration that arises in determining the optimal grid size. All versions of this algorithm can be thought of as a compromise between deterministic and Monte Carlo methods, capturing advantages of both techniques.

Two is better than one: joint statistics of density and velocity in concentric spheres as a cosmological probe

NASA Astrophysics Data System (ADS)

Uhlemann, C.; Codis, S.; Hahn, O.; Pichon, C.; Bernardeau, F.

2017-08-01

The analytical formalism to obtain the probability distribution functions (PDFs) of spherically averaged cosmic densities and velocity divergences in the mildly non-linear regime is presented. A large-deviation principle is applied to those cosmic fields assuming their most likely dynamics in spheres is set by the spherical collapse model. We validate our analytical results using state-of-the-art dark matter simulations with a phase-space resolved velocity field finding a 2 per cent level agreement for a wide range of velocity divergences and densities in the mildly non-linear regime (˜10 Mpc h-1 at redshift zero), usually inaccessible to perturbation theory. From the joint PDF of densities and velocity divergences measured in two concentric spheres, we extract with the same accuracy velocity profiles and conditional velocity PDF subject to a given over/underdensity that are of interest to understand the non-linear evolution of velocity flows. Both PDFs are used to build a simple but accurate maximum likelihood estimator for the redshift evolution of the variance of both the density and velocity divergence fields, which have smaller relative errors than their sample variances when non-linearities appear. Given the dependence of the velocity divergence on the growth rate, there is a significant gain in using the full knowledge of both PDFs to derive constraints on the equation of state-of-dark energy. Thanks to the insensitivity of the velocity divergence to bias, its PDF can be used to obtain unbiased constraints on the growth of structures (σ8, f) or it can be combined with the galaxy density PDF to extract bias parameters.

Nucleotide polymorphisms in a pine ortholog of the Arabidopsis degrading enzyme cellulase KORRIGAN are associated with early growth performance in Pinus pinaster.

PubMed

Cabezas, José Antonio; González-Martínez, Santiago C; Collada, Carmen; Guevara, María Angeles; Boury, Christophe; de María, Nuria; Eveno, Emmanuelle; Aranda, Ismael; Garnier-Géré, Pauline H; Brach, Jean; Alía, Ricardo; Plomion, Christophe; Cervera, María Teresa

2015-09-01

We have carried out a candidate-gene-based association genetic study in Pinus pinaster Aiton and evaluated the predictive performance for genetic merit gain of the most significantly associated genes and single nucleotide polymorphisms (SNPs). We used a second generation 384-SNP array enriched with candidate genes for growth and wood properties to genotype mother trees collected in 20 natural populations covering most of the European distribution of the species. Phenotypic data for total height, polycyclism, root-collar diameter and biomass were obtained from a replicated provenance-progeny trial located in two sites with contrasting environments (Atlantic vs Mediterranean climate). General linear models identified strong associations between growth traits (total height and polycyclism) and four SNPs from the korrigan candidate gene, after multiple testing corrections using false discovery rate. The combined genomic breeding value predictions assessed for the four associated korrigan SNPs by ridge regression-best linear unbiased prediction (RR-BLUP) and cross-validation accounted for up to 8 and 15% of the phenotypic variance for height and polycyclic growth, respectively, and did not improve adding SNPs from other growth-related candidate genes. For root-collar diameter and total biomass, they accounted for 1.6 and 1.1% of the phenotypic variance, respectively, but increased to 15 and 4.1% when other SNPs from lp3.1, lp3.3 and cad were included in RR-BLUP models. These results point towards a desirable integration of candidate-gene studies as a means to pre-select relevant markers, and aid genomic selection in maritime pine breeding programs. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Genetic Diversity and Population Structure of F3:6 Nebraska Winter Wheat Genotypes Using Genotyping-By-Sequencing.

PubMed

Eltaher, Shamseldeen; Sallam, Ahmed; Belamkar, Vikas; Emara, Hamdy A; Nower, Ahmed A; Salem, Khaled F M; Poland, Jesse; Baenziger, Peter S

2018-01-01

The availability of information on the genetic diversity and population structure in wheat ( Triticum aestivum L.) breeding lines will help wheat breeders to better use their genetic resources and manage genetic variation in their breeding program. The recent advances in sequencing technology provide the opportunity to identify tens or hundreds of thousands of single nucleotide polymorphism (SNPs) in large genome species (e.g., wheat). These SNPs can be utilized for understanding genetic diversity and performing genome wide association studies (GWAS) for complex traits. In this study, the genetic diversity and population structure were investigated in a set of 230 genotypes (F 3:6 ) derived from various crosses as a prerequisite for GWAS and genomic selection. Genotyping-by-sequencing provided 25,566 high-quality SNPs. The polymorphism information content (PIC) across chromosomes ranged from 0.09 to 0.37 with an average of 0.23. The distribution of SNPs markers on the 21 chromosomes ranged from 319 on chromosome 3D to 2,370 on chromosome 3B. The analysis of population structure revealed three subpopulations (G1, G2, and G3). Analysis of molecular variance identified 8% variance among and 92% within subpopulations. Of the three subpopulations, G2 had the highest level of genetic diversity based on three genetic diversity indices: Shannon's information index ( I ) = 0.494, diversity index ( h ) = 0.328 and unbiased diversity index (uh) = 0.331, while G3 had lowest level of genetic diversity ( I = 0.348, h = 0.226 and uh = 0.236). This high genetic diversity identified among the subpopulations can be used to develop new wheat cultivars.

TU-H-CAMPUS-IeP1-01: Bias and Computational Efficiency of Variance Reduction Methods for the Monte Carlo Simulation of Imaging Detectors

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

Sharma, D; Badano, A; Sempau, J

Purpose: Variance reduction techniques (VRTs) are employed in Monte Carlo simulations to obtain estimates with reduced statistical uncertainty for a given simulation time. In this work, we study the bias and efficiency of a VRT for estimating the response of imaging detectors. Methods: We implemented Directed Sampling (DS), preferentially directing a fraction of emitted optical photons directly towards the detector by altering the isotropic model. The weight of each optical photon is appropriately modified to maintain simulation estimates unbiased. We use a Monte Carlo tool called fastDETECT2 (part of the hybridMANTIS open-source package) for optical transport, modified for VRT. Themore » weight of each photon is calculated as the ratio of original probability (no VRT) and the new probability for a particular direction. For our analysis of bias and efficiency, we use pulse height spectra, point response functions, and Swank factors. We obtain results for a variety of cases including analog (no VRT, isotropic distribution), and DS with 0.2 and 0.8 optical photons directed towards the sensor plane. We used 10,000, 25-keV primaries. Results: The Swank factor for all cases in our simplified model converged fast (within the first 100 primaries) to a stable value of 0.9. The root mean square error per pixel for DS VRT for the point response function between analog and VRT cases was approximately 5e-4. Conclusion: Our preliminary results suggest that DS VRT does not affect the estimate of the mean for the Swank factor. Our findings indicate that it may be possible to design VRTs for imaging detector simulations to increase computational efficiency without introducing bias.« less

Estimating riparian understory vegetation cover with beta regression and copula models

USGS Publications Warehouse

Eskelson, Bianca N.I.; Madsen, Lisa; Hagar, Joan C.; Temesgen, Hailemariam

2011-01-01

Understory vegetation communities are critical components of forest ecosystems. As a result, the importance of modeling understory vegetation characteristics in forested landscapes has become more apparent. Abundance measures such as shrub cover are bounded between 0 and 1, exhibit heteroscedastic error variance, and are often subject to spatial dependence. These distributional features tend to be ignored when shrub cover data are analyzed. The beta distribution has been used successfully to describe the frequency distribution of vegetation cover. Beta regression models ignoring spatial dependence (BR) and accounting for spatial dependence (BRdep) were used to estimate percent shrub cover as a function of topographic conditions and overstory vegetation structure in riparian zones in western Oregon. The BR models showed poor explanatory power (pseudo-R2 ≤ 0.34) but outperformed ordinary least-squares (OLS) and generalized least-squares (GLS) regression models with logit-transformed response in terms of mean square prediction error and absolute bias. We introduce a copula (COP) model that is based on the beta distribution and accounts for spatial dependence. A simulation study was designed to illustrate the effects of incorrectly assuming normality, equal variance, and spatial independence. It showed that BR, BRdep, and COP models provide unbiased parameter estimates, whereas OLS and GLS models result in slightly biased estimates for two of the three parameters. On the basis of the simulation study, 93–97% of the GLS, BRdep, and COP confidence intervals covered the true parameters, whereas OLS and BR only resulted in 84–88% coverage, which demonstrated the superiority of GLS, BRdep, and COP over OLS and BR models in providing standard errors for the parameter estimates in the presence of spatial dependence.

Mixed Model Methods for Genomic Prediction and Variance Component Estimation of Additive and Dominance Effects Using SNP Markers

PubMed Central

Da, Yang; Wang, Chunkao; Wang, Shengwen; Hu, Guo

2014-01-01

We established a genomic model of quantitative trait with genomic additive and dominance relationships that parallels the traditional quantitative genetics model, which partitions a genotypic value as breeding value plus dominance deviation and calculates additive and dominance relationships using pedigree information. Based on this genomic model, two sets of computationally complementary but mathematically identical mixed model methods were developed for genomic best linear unbiased prediction (GBLUP) and genomic restricted maximum likelihood estimation (GREML) of additive and dominance effects using SNP markers. These two sets are referred to as the CE and QM sets, where the CE set was designed for large numbers of markers and the QM set was designed for large numbers of individuals. GBLUP and associated accuracy formulations for individuals in training and validation data sets were derived for breeding values, dominance deviations and genotypic values. Simulation study showed that GREML and GBLUP generally were able to capture small additive and dominance effects that each accounted for 0.00005–0.0003 of the phenotypic variance and GREML was able to differentiate true additive and dominance heritability levels. GBLUP of the total genetic value as the summation of additive and dominance effects had higher prediction accuracy than either additive or dominance GBLUP, causal variants had the highest accuracy of GREML and GBLUP, and predicted accuracies were in agreement with observed accuracies. Genomic additive and dominance relationship matrices using SNP markers were consistent with theoretical expectations. The GREML and GBLUP methods can be an effective tool for assessing the type and magnitude of genetic effects affecting a phenotype and for predicting the total genetic value at the whole genome level. PMID:24498162

Mixed model methods for genomic prediction and variance component estimation of additive and dominance effects using SNP markers.

PubMed

Da, Yang; Wang, Chunkao; Wang, Shengwen; Hu, Guo

2014-01-01

We established a genomic model of quantitative trait with genomic additive and dominance relationships that parallels the traditional quantitative genetics model, which partitions a genotypic value as breeding value plus dominance deviation and calculates additive and dominance relationships using pedigree information. Based on this genomic model, two sets of computationally complementary but mathematically identical mixed model methods were developed for genomic best linear unbiased prediction (GBLUP) and genomic restricted maximum likelihood estimation (GREML) of additive and dominance effects using SNP markers. These two sets are referred to as the CE and QM sets, where the CE set was designed for large numbers of markers and the QM set was designed for large numbers of individuals. GBLUP and associated accuracy formulations for individuals in training and validation data sets were derived for breeding values, dominance deviations and genotypic values. Simulation study showed that GREML and GBLUP generally were able to capture small additive and dominance effects that each accounted for 0.00005-0.0003 of the phenotypic variance and GREML was able to differentiate true additive and dominance heritability levels. GBLUP of the total genetic value as the summation of additive and dominance effects had higher prediction accuracy than either additive or dominance GBLUP, causal variants had the highest accuracy of GREML and GBLUP, and predicted accuracies were in agreement with observed accuracies. Genomic additive and dominance relationship matrices using SNP markers were consistent with theoretical expectations. The GREML and GBLUP methods can be an effective tool for assessing the type and magnitude of genetic effects affecting a phenotype and for predicting the total genetic value at the whole genome level.

Genetic Diversity and Population Structure of F3:6 Nebraska Winter Wheat Genotypes Using Genotyping-By-Sequencing

PubMed Central

Eltaher, Shamseldeen; Sallam, Ahmed; Belamkar, Vikas; Emara, Hamdy A.; Nower, Ahmed A.; Salem, Khaled F. M.; Poland, Jesse; Baenziger, Peter S.

2018-01-01

The availability of information on the genetic diversity and population structure in wheat (Triticum aestivum L.) breeding lines will help wheat breeders to better use their genetic resources and manage genetic variation in their breeding program. The recent advances in sequencing technology provide the opportunity to identify tens or hundreds of thousands of single nucleotide polymorphism (SNPs) in large genome species (e.g., wheat). These SNPs can be utilized for understanding genetic diversity and performing genome wide association studies (GWAS) for complex traits. In this study, the genetic diversity and population structure were investigated in a set of 230 genotypes (F3:6) derived from various crosses as a prerequisite for GWAS and genomic selection. Genotyping-by-sequencing provided 25,566 high-quality SNPs. The polymorphism information content (PIC) across chromosomes ranged from 0.09 to 0.37 with an average of 0.23. The distribution of SNPs markers on the 21 chromosomes ranged from 319 on chromosome 3D to 2,370 on chromosome 3B. The analysis of population structure revealed three subpopulations (G1, G2, and G3). Analysis of molecular variance identified 8% variance among and 92% within subpopulations. Of the three subpopulations, G2 had the highest level of genetic diversity based on three genetic diversity indices: Shannon’s information index (I) = 0.494, diversity index (h) = 0.328 and unbiased diversity index (uh) = 0.331, while G3 had lowest level of genetic diversity (I = 0.348, h = 0.226 and uh = 0.236). This high genetic diversity identified among the subpopulations can be used to develop new wheat cultivars. PMID:29593779

Rare event simulation in radiation transport

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

Kollman, Craig

1993-10-01

This dissertation studies methods for estimating extremely small probabilities by Monte Carlo simulation. Problems in radiation transport typically involve estimating very rare events or the expected value of a random variable which is with overwhelming probability equal to zero. These problems often have high dimensional state spaces and irregular geometries so that analytic solutions are not possible. Monte Carlo simulation must be used to estimate the radiation dosage being transported to a particular location. If the area is well shielded the probability of any one particular particle getting through is very small. Because of the large number of particles involved,more » even a tiny fraction penetrating the shield may represent an unacceptable level of radiation. It therefore becomes critical to be able to accurately estimate this extremely small probability. Importance sampling is a well known technique for improving the efficiency of rare event calculations. Here, a new set of probabilities is used in the simulation runs. The results are multiple by the likelihood ratio between the true and simulated probabilities so as to keep the estimator unbiased. The variance of the resulting estimator is very sensitive to which new set of transition probabilities are chosen. It is shown that a zero variance estimator does exist, but that its computation requires exact knowledge of the solution. A simple random walk with an associated killing model for the scatter of neutrons is introduced. Large deviation results for optimal importance sampling in random walks are extended to the case where killing is present. An adaptive ``learning`` algorithm for implementing importance sampling is given for more general Markov chain models of neutron scatter. For finite state spaces this algorithm is shown to give with probability one, a sequence of estimates converging exponentially fast to the true solution.« less

Variation in cassava germplasm for tolerance to post-harvest physiological deterioration.

PubMed

Venturini, M T; Santos, L R; Vildoso, C I A; Santos, V S; Oliveira, E J

2016-05-06

Tolerant varieties can effectively control post-harvest physiological deterioration (PPD) of cassava, although knowledge on the genetic variability and inheritance of this trait is needed. The objective of this study was to estimate genetic parameters and identify sources of tolerance to PPD and their stability in cassava accessions. Roots from 418 cassava accessions, grown in four independent experiments, were evaluated for PPD tolerance 0, 2, 5, and 10 days post-harvest. Data were transformed into area under the PPD-progress curve (AUP-PPD) to quantify tolerance. Genetic parameters, stability (Si), adaptability (Ai), and the joint analysis of stability and adaptability (Zi) were obtained via residual maximum likelihood (REML) and best linear unbiased prediction (BLUP) methods. Variance in the genotype (G) x environment (E) interaction and genotypic variance were important for PPD tolerance. Individual broad-sense heritability (hg(2)= 0.38 ± 0.04) and average heritability in accessions (hmg(2)= 0.52) showed high genetic control of PPD tolerance. Genotypic correlation of AUP-PPD in different experiments was of medium magnitude (ȓgA = 0.42), indicating significant G x E interaction. The predicted genotypic values o f G x E free of interaction (û + ĝi) showed high variation. Of the 30 accessions with high Zi, 19 were common to û + ĝi, Si, and Ai parameters. The genetic gain with selection of these 19 cassava accessions was -55.94, -466.86, -397.72, and -444.03% for û + ĝi, Si, Ai, and Zi, respectively, compared with the overall mean for each parameter. These results demonstrate the variability and potential of cassava germplasm to introduce PPD tolerance in commercial varieties.

Genomic-Enabled Prediction in Maize Using Kernel Models with Genotype × Environment Interaction

PubMed Central

Bandeira e Sousa, Massaine; Cuevas, Jaime; de Oliveira Couto, Evellyn Giselly; Pérez-Rodríguez, Paulino; Jarquín, Diego; Fritsche-Neto, Roberto; Burgueño, Juan; Crossa, Jose

2017-01-01

Multi-environment trials are routinely conducted in plant breeding to select candidates for the next selection cycle. In this study, we compare the prediction accuracy of four developed genomic-enabled prediction models: (1) single-environment, main genotypic effect model (SM); (2) multi-environment, main genotypic effects model (MM); (3) multi-environment, single variance G×E deviation model (MDs); and (4) multi-environment, environment-specific variance G×E deviation model (MDe). Each of these four models were fitted using two kernel methods: a linear kernel Genomic Best Linear Unbiased Predictor, GBLUP (GB), and a nonlinear kernel Gaussian kernel (GK). The eight model-method combinations were applied to two extensive Brazilian maize data sets (HEL and USP data sets), having different numbers of maize hybrids evaluated in different environments for grain yield (GY), plant height (PH), and ear height (EH). Results show that the MDe and the MDs models fitted with the Gaussian kernel (MDe-GK, and MDs-GK) had the highest prediction accuracy. For GY in the HEL data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 9 to 32%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 9 to 49%. For GY in the USP data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 0 to 7%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 34 to 70%. For traits PH and EH, gains in prediction accuracy of models with GK compared to models with GB were smaller than those achieved in GY. Also, these gains in prediction accuracy decreased when a more difficult prediction problem was studied. PMID:28455415

Genomic-Enabled Prediction in Maize Using Kernel Models with Genotype × Environment Interaction.

PubMed

Bandeira E Sousa, Massaine; Cuevas, Jaime; de Oliveira Couto, Evellyn Giselly; Pérez-Rodríguez, Paulino; Jarquín, Diego; Fritsche-Neto, Roberto; Burgueño, Juan; Crossa, Jose

2017-06-07

Multi-environment trials are routinely conducted in plant breeding to select candidates for the next selection cycle. In this study, we compare the prediction accuracy of four developed genomic-enabled prediction models: (1) single-environment, main genotypic effect model (SM); (2) multi-environment, main genotypic effects model (MM); (3) multi-environment, single variance G×E deviation model (MDs); and (4) multi-environment, environment-specific variance G×E deviation model (MDe). Each of these four models were fitted using two kernel methods: a linear kernel Genomic Best Linear Unbiased Predictor, GBLUP (GB), and a nonlinear kernel Gaussian kernel (GK). The eight model-method combinations were applied to two extensive Brazilian maize data sets (HEL and USP data sets), having different numbers of maize hybrids evaluated in different environments for grain yield (GY), plant height (PH), and ear height (EH). Results show that the MDe and the MDs models fitted with the Gaussian kernel (MDe-GK, and MDs-GK) had the highest prediction accuracy. For GY in the HEL data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 9 to 32%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 9 to 49%. For GY in the USP data set, the increase in prediction accuracy of SM-GK over SM-GB ranged from 0 to 7%. For the MM, MDs, and MDe models, the increase in prediction accuracy of GK over GB ranged from 34 to 70%. For traits PH and EH, gains in prediction accuracy of models with GK compared to models with GB were smaller than those achieved in GY. Also, these gains in prediction accuracy decreased when a more difficult prediction problem was studied. Copyright © 2017 Bandeira e Sousa et al.

Foraminifera Record the Good Years More than the Bad

NASA Astrophysics Data System (ADS)

Hull, P. M.

2014-12-01

Past ocean conditions are primarily discerned from geochemical and community-based analyses of fossilized taxa, each of which have unique environmental niches and dynamics. A key requirement of such paleoceanographic studies is that some unbiased or well-constrained record of the living ecosystem and climate is deposited on the sea floor and preserved through the post-depositional processes that act to distort them. It is widely known that foraminiferal species exhibit varying seasonal preferences and that seasonality is a key variable to account for in paleoceanographic reconstructions. However, on longer time scales (> year), it is generally assumed that species record the 'average' environmental conditions or typical variance (e.g., El Nino intensity) that existed in a given, time-averaged sediment sample. Here I examine planktonic foraminiferal population dynamics on yearly and longer time scales, in order to quantify their effect on paleoceanographic reconstructions. Using a previously published record of >250 years of population dynamics in the Santa Barbara Basin sediments, I find that the majority of individuals in a given species lived during a small subset of the total years (~15- 37% of years depending on the species). Populations of shallow, mixed layer species primarily represent the warmest, youngest years, while thermocline species primarily represent the cooler, older years. Importantly, the seasonality of species does not always predict their interannual dynamics. The general importance of long time-scale population dynamics on paleoceanographic reconstructions will also be considered in a theoretical model parameterized with temporally explicit species co-variances and temperature variability. Such modeling is needed to constrain the relative impact that a very good year can have on our interpretation of the 'average' of hundreds to thousands of years.

Five instruments for measuring tree height: an evaluation

Treesearch

Michael S. Williams; William A. Bechtold; V.J. LaBau

1994-01-01

Five instruments were tested for reliability in measuring tree heights under realistic conditions. Four linear models were used to determine if tree height can be measured unbiasedly over all tree sizes and if any of the instruments were more efficient in estimating tree height. The laser height finder was the only instrument to produce unbiased estimates of the true...

Spectroscopic observations of ATLAS17lcs (SN 2017guv) and ASASSN-17mq (AT 2017gvo) by NUTS (NOT Un-biased Transient Survey)

NASA Astrophysics Data System (ADS)

Dong, Subo; Bose, Subhash; Stritzinger, M.; Holmbo, S.; Fraser, M.; Fedorets, G.

2017-10-01

The Nordic Optical Telescope (NOT) Unbiased Transient Survey (NUTS; ATel #8992) reports the spectroscopic classification of ATLAS17lcs (SN 2017guv) and ASASSN-17mq (AT 2017gvo) in host galaxies 2MASX J19132225-1648031 and CGCG 225-050, respectively.

Spectroscopic observations of ASASSN-17io and ATLAS17hpt (SN 2017faf) by NUTS (NOT Un-biased Transient Survey)

NASA Astrophysics Data System (ADS)

Pastorello, Andrea; Benetti, Stefano; Cappellaro, Enrico; Terreran, Giacomo; Tomasella, Lina; Fedorets, Grigori; NUTS Collaboration

2017-07-01

The Nordic Optical Telescope (NOT) Unbiased Transient Survey (NUTS; ATel #8992) reports the spectroscopic classification of ASASSN-17io in the galaxy CGCG 316-010, along with the re classification of ATLAS17hpt (SN 2017faf), which was previously classified as a SLSN-I (ATel #10549).

Contextual classification of multispectral image data: An unbiased estimator for the context distribution

NASA Technical Reports Server (NTRS)

Tilton, J. C.; Swain, P. H. (Principal Investigator); Vardeman, S. B.

1981-01-01

A key input to a statistical classification algorithm, which exploits the tendency of certain ground cover classes to occur more frequently in some spatial context than in others, is a statistical characterization of the context: the context distribution. An unbiased estimator of the context distribution is discussed which, besides having the advantage of statistical unbiasedness, has the additional advantage over other estimation techniques of being amenable to an adaptive implementation in which the context distribution estimate varies according to local contextual information. Results from applying the unbiased estimator to the contextual classification of three real LANDSAT data sets are presented and contrasted with results from non-contextual classifications and from contextual classifications utilizing other context distribution estimation techniques.

VizieR Online Data Catalog: AGNs in submm-selected Lockman Hole galaxies (Serjeant+, 2010)

NASA Astrophysics Data System (ADS)

Serjeant, S.; Negrello, M.; Pearson, C.; Mortier, A.; Austermann, J.; Aretxaga, I.; Clements, D.; Chapman, S.; Dye, S.; Dunlop, J.; Dunne, L.; Farrah, D.; Hughes, D.; Lee, H. M.; Matsuhara, H.; Ibar, E.; Im, M.; Jeong, W.-S.; Kim, S.; Oyabu, S.; Takagi, T.; Wada, T.; Wilson, G.; Vaccari, M.; Yun, M.

2013-11-01

We present a comparison of the SCUBA half degree extragalactic survey (SHADES) at 450μm, 850μm and 1100μm with deep guaranteed time 15μm AKARI FU-HYU survey data and Spitzer guaranteed time data at 3.6-24μm in the Lockman hole east. The AKARI data was analysed using bespoke software based in part on the drizzling and minimum-variance matched filtering developed for SHADES, and was cross-calibrated against ISO fluxes. (2 data files).

Statistical analysis of nonlinearly reconstructed near-infrared tomographic images: Part I--Theory and simulations.

PubMed

Pogue, Brian W; Song, Xiaomei; Tosteson, Tor D; McBride, Troy O; Jiang, Shudong; Paulsen, Keith D

2002-07-01

Near-infrared (NIR) diffuse tomography is an emerging method for imaging the interior of tissues to quantify concentrations of hemoglobin and exogenous chromophores non-invasively in vivo. It often exploits an optical diffusion model-based image reconstruction algorithm to estimate spatial property values from measurements of the light flux at the surface of the tissue. In this study, mean-squared error (MSE) over the image is used to evaluate methods for regularizing the ill-posed inverse image reconstruction problem in NIR tomography. Estimates of image bias and image standard deviation were calculated based upon 100 repeated reconstructions of a test image with randomly distributed noise added to the light flux measurements. It was observed that the bias error dominates at high regularization parameter values while variance dominates as the algorithm is allowed to approach the optimal solution. This optimum does not necessarily correspond to the minimum projection error solution, but typically requires further iteration with a decreasing regularization parameter to reach the lowest image error. Increasing measurement noise causes a need to constrain the minimum regularization parameter to higher values in order to achieve a minimum in the overall image MSE.

Global-scale high-resolution ( 1 km) modelling of mean, maximum and minimum annual streamflow

NASA Astrophysics Data System (ADS)

Barbarossa, Valerio; Huijbregts, Mark; Hendriks, Jan; Beusen, Arthur; Clavreul, Julie; King, Henry; Schipper, Aafke

2017-04-01

Quantifying mean, maximum and minimum annual flow (AF) of rivers at ungauged sites is essential for a number of applications, including assessments of global water supply, ecosystem integrity and water footprints. AF metrics can be quantified with spatially explicit process-based models, which might be overly time-consuming and data-intensive for this purpose, or with empirical regression models that predict AF metrics based on climate and catchment characteristics. Yet, so far, regression models have mostly been developed at a regional scale and the extent to which they can be extrapolated to other regions is not known. We developed global-scale regression models that quantify mean, maximum and minimum AF as function of catchment area and catchment-averaged slope, elevation, and mean, maximum and minimum annual precipitation and air temperature. We then used these models to obtain global 30 arc-seconds (˜ 1 km) maps of mean, maximum and minimum AF for each year from 1960 through 2015, based on a newly developed hydrologically conditioned digital elevation model. We calibrated our regression models based on observations of discharge and catchment characteristics from about 4,000 catchments worldwide, ranging from 100 to 106 km2 in size, and validated them against independent measurements as well as the output of a number of process-based global hydrological models (GHMs). The variance explained by our regression models ranged up to 90% and the performance of the models compared well with the performance of existing GHMs. Yet, our AF maps provide a level of spatial detail that cannot yet be achieved by current GHMs.

Law of the Minimum paradoxes.

PubMed

Gorban, Alexander N; Pokidysheva, Lyudmila I; Smirnova, Elena V; Tyukina, Tatiana A

2011-09-01

The "Law of the Minimum" states that growth is controlled by the scarcest resource (limiting factor). This concept was originally applied to plant or crop growth (Justus von Liebig, 1840, Salisbury, Plant physiology, 4th edn., Wadsworth, Belmont, 1992) and quantitatively supported by many experiments. Some generalizations based on more complicated "dose-response" curves were proposed. Violations of this law in natural and experimental ecosystems were also reported. We study models of adaptation in ensembles of similar organisms under load of environmental factors and prove that violation of Liebig's law follows from adaptation effects. If the fitness of an organism in a fixed environment satisfies the Law of the Minimum then adaptation equalizes the pressure of essential factors and, therefore, acts against the Liebig's law. This is the the Law of the Minimum paradox: if for a randomly chosen pair "organism-environment" the Law of the Minimum typically holds, then in a well-adapted system, we have to expect violations of this law.For the opposite interaction of factors (a synergistic system of factors which amplify each other), adaptation leads from factor equivalence to limitations by a smaller number of factors.For analysis of adaptation, we develop a system of models based on Selye's idea of the universal adaptation resource (adaptation energy). These models predict that under the load of an environmental factor a population separates into two groups (phases): a less correlated, well adapted group and a highly correlated group with a larger variance of attributes, which experiences problems with adaptation. Some empirical data are presented and evidences of interdisciplinary applications to econometrics are discussed. © Society for Mathematical Biology 2010

MOnthly TEmperature DAtabase of Spain 1951-2010: MOTEDAS (2): The Correlation Decay Distance (CDD) and the spatial variability of maximum and minimum monthly temperature in Spain during (1981-2010).

NASA Astrophysics Data System (ADS)

Cortesi, Nicola; Peña-Angulo, Dhais; Simolo, Claudia; Stepanek, Peter; Brunetti, Michele; Gonzalez-Hidalgo, José Carlos

2014-05-01

One of the key point in the develop of the MOTEDAS dataset (see Poster 1 MOTEDAS) in the framework of the HIDROCAES Project (Impactos Hidrológicos del Calentamiento Global en España, Spanish Ministery of Research CGL2011-27574-C02-01) is the reference series for which no generalized metadata exist. In this poster we present an analysis of spatial variability of monthly minimum and maximum temperatures in the conterminous land of Spain (Iberian Peninsula, IP), by using the Correlation Decay Distance function (CDD), with the aim of evaluating, at sub-regional level, the optimal threshold distance between neighbouring stations for producing the set of reference series used in the quality control (see MOTEDAS Poster 1) and the reconstruction (see MOREDAS Poster 3). The CDD analysis for Tmax and Tmin was performed calculating a correlation matrix at monthly scale between 1981-2010 among monthly mean values of maximum (Tmax) and minimum (Tmin) temperature series (with at least 90% of data), free of anomalous data and homogenized (see MOTEDAS Poster 1), obtained from AEMEt archives (National Spanish Meteorological Agency). Monthly anomalies (difference between data and mean 1981-2010) were used to prevent the dominant effect of annual cycle in the CDD annual estimation. For each station, and time scale, the common variance r2 (using the square of Pearson's correlation coefficient) was calculated between all neighbouring temperature series and the relation between r2 and distance was modelled according to the following equation (1): Log (r2ij) = b*°dij (1) being Log(rij2) the common variance between target (i) and neighbouring series (j), dij the distance between them and b the slope of the ordinary least-squares linear regression model applied taking into account only the surrounding stations within a starting radius of 50 km and with a minimum of 5 stations required. Finally, monthly, seasonal and annual CDD values were interpolated using the Ordinary Kriging with a spherical variogram over conterminous land of Spain, and converted on a regular 10 km2 grid (resolution similar to the mean distance between stations) to map the results. In the conterminous land of Spain the distance at which couples of stations have a common variance in temperature (both maximum Tmax, and minimum Tmin) above the selected threshold (50%, r Pearson ~0.70) on average does not exceed 400 km, with relevant spatial and temporal differences. The spatial distribution of the CDD shows a clear coastland-to-inland gradient at annual, seasonal and monthly scale, with highest spatial variability along the coastland areas and lower variability inland. The highest spatial variability coincide particularly with coastland areas surrounded by mountain chains and suggests that the orography is one of the most driving factor causing higher interstation variability. Moreover, there are some differences between the behaviour of Tmax and Tmin, being Tmin spatially more homogeneous than Tmax, but its lower CDD values indicate that night-time temperature is more variable than diurnal one. The results suggest that in general local factors affects the spatial variability of monthly Tmin more than Tmax and then higher network density would be necessary to capture the higher spatial variability highlighted for Tmin respect to Tmax. The results suggest that in general local factors affects the spatial variability of Tmin more than Tmax and then higher network density would be necessary to capture the higher spatial variability highlighted for minimum temperature respect to maximum temperature. A conservative distance for reference series could be evaluated in 200 km, that we propose for continental land of Spain and use in the development of MOTEDAS.

Minimum variance optimal rate allocation for multiplexed H.264/AVC bitstreams.

PubMed

Tagliasacchi, Marco; Valenzise, Giuseppe; Tubaro, Stefano

2008-07-01

Consider the problem of transmitting multiple video streams to fulfill a constant bandwidth constraint. The available bit budget needs to be distributed across the sequences in order to meet some optimality criteria. For example, one might want to minimize the average distortion or, alternatively, minimize the distortion variance, in order to keep almost constant quality among the encoded sequences. By working in the rho-domain, we propose a low-delay rate allocation scheme that, at each time instant, provides a closed form solution for either the aforementioned problems. We show that minimizing the distortion variance instead of the average distortion leads, for each of the multiplexed sequences, to a coding penalty less than 0.5 dB, in terms of average PSNR. In addition, our analysis provides an explicit relationship between model parameters and this loss. In order to smooth the distortion also along time, we accommodate a shared encoder buffer to compensate for rate fluctuations. Although the proposed scheme is general, and it can be adopted for any video and image coding standard, we provide experimental evidence by transcoding bitstreams encoded using the state-of-the-art H.264/AVC standard. The results of our simulations reveal that is it possible to achieve distortion smoothing both in time and across the sequences, without sacrificing coding efficiency.

Maximal Unbiased Benchmarking Data Sets for Human Chemokine Receptors and Comparative Analysis.

PubMed

Xia, Jie; Reid, Terry-Elinor; Wu, Song; Zhang, Liangren; Wang, Xiang Simon

2018-05-29

Chemokine receptors (CRs) have long been druggable targets for the treatment of inflammatory diseases and HIV-1 infection. As a powerful technique, virtual screening (VS) has been widely applied to identifying small molecule leads for modern drug targets including CRs. For rational selection of a wide variety of VS approaches, ligand enrichment assessment based on a benchmarking data set has become an indispensable practice. However, the lack of versatile benchmarking sets for the whole CRs family that are able to unbiasedly evaluate every single approach including both structure- and ligand-based VS somewhat hinders modern drug discovery efforts. To address this issue, we constructed Maximal Unbiased Benchmarking Data sets for human Chemokine Receptors (MUBD-hCRs) using our recently developed tools of MUBD-DecoyMaker. The MUBD-hCRs encompasses 13 subtypes out of 20 chemokine receptors, composed of 404 ligands and 15756 decoys so far and is readily expandable in the future. It had been thoroughly validated that MUBD-hCRs ligands are chemically diverse while its decoys are maximal unbiased in terms of "artificial enrichment", "analogue bias". In addition, we studied the performance of MUBD-hCRs, in particular CXCR4 and CCR5 data sets, in ligand enrichment assessments of both structure- and ligand-based VS approaches in comparison with other benchmarking data sets available in the public domain and demonstrated that MUBD-hCRs is very capable of designating the optimal VS approach. MUBD-hCRs is a unique and maximal unbiased benchmarking set that covers major CRs subtypes so far.

Critical point relascope sampling for unbiased volume estimation of downed coarse woody debris

Treesearch

Jeffrey H. Gove; Michael S. Williams; Mark J. Ducey; Mark J. Ducey

2005-01-01

Critical point relascope sampling is developed and shown to be design-unbiased for the estimation of log volume when used with point relascope sampling for downed coarse woody debris. The method is closely related to critical height sampling for standing trees when trees are first sampled with a wedge prism. Three alternative protocols for determining the critical...

Retransformation bias in a stem profile model

Treesearch

Raymond L. Czaplewski; David Bruce

1990-01-01

An unbiased profile model, fit to diameter divided by diameter at breast height, overestimated volume of 5.3-m log sections by 0.5 to 3.5%. Another unbiased profile model, fit to squared diameter divided by squared diameter at breast height, underestimated bole diameters by 0.2 to 2.1%. These biases are caused by retransformation of the predicted dependent variable;...

Unbiased nonorthogonal bases for tomographic reconstruction

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

Sainz, Isabel; Klimov, Andrei B.; Roa, Luis

2010-05-15

We have developed a general method for constructing a set of nonorthogonal bases with equal separations between all different basis states in prime dimensions. The results are that the corresponding biorthogonal counterparts are pairwise unbiased with the components of the original bases. Using these bases, we derive an explicit expression for the optimal tomography in nonorthogonal bases. A special two-dimensional case is analyzed separately.

Simultaneous unbiased estimates of multiple downed wood attributes in perpendicular distance sampling

Treesearch

Mark J. Ducey; Jeffrey H. Gove; Harry T. Valentine

2008-01-01

Perpendicular distance sampling (PDS) is a fast probability-proportional-to-size method for inventory of downed wood. However, previous development of PDS had limited the method to estimating only one variable (such as volume per hectare, or surface area per hectare) at a time. Here, we develop a general design-unbiased estimator for PDS. We then show how that...

Additive-Multiplicative Approximation of Genotype-Environment Interaction

PubMed Central

Gimelfarb, A.

1994-01-01

A model of genotype-environment interaction in quantitative traits is considered. The model represents an expansion of the traditional additive (first degree polynomial) approximation of genotypic and environmental effects to a second degree polynomial incorporating a multiplicative term besides the additive terms. An experimental evaluation of the model is suggested and applied to a trait in Drosophila melanogaster. The environmental variance of a genotype in the model is shown to be a function of the genotypic value: it is a convex parabola. The broad sense heritability in a population depends not only on the genotypic and environmental variances, but also on the position of the genotypic mean in the population relative to the minimum of the parabola. It is demonstrated, using the model, that GXE interaction rectional may cause a substantial non-linearity in offspring-parent regression and a reversed response to directional selection. It is also shown that directional selection may be accompanied by an increase in the heritability. PMID:7896113

Combinatorics of least-squares trees.

PubMed

Mihaescu, Radu; Pachter, Lior

2008-09-09

A recurring theme in the least-squares approach to phylogenetics has been the discovery of elegant combinatorial formulas for the least-squares estimates of edge lengths. These formulas have proved useful for the development of efficient algorithms, and have also been important for understanding connections among popular phylogeny algorithms. For example, the selection criterion of the neighbor-joining algorithm is now understood in terms of the combinatorial formulas of Pauplin for estimating tree length. We highlight a phylogenetically desirable property that weighted least-squares methods should satisfy, and provide a complete characterization of methods that satisfy the property. The necessary and sufficient condition is a multiplicative four-point condition that the variance matrix needs to satisfy. The proof is based on the observation that the Lagrange multipliers in the proof of the Gauss-Markov theorem are tree-additive. Our results generalize and complete previous work on ordinary least squares, balanced minimum evolution, and the taxon-weighted variance model. They also provide a time-optimal algorithm for computation.

A Quantitative Microscopy Technique for Determining the Number of Specific Proteins in Cellular Compartments

PubMed Central

Mutch, Sarah A.; Gadd, Jennifer C.; Fujimoto, Bryant S.; Kensel-Hammes, Patricia; Schiro, Perry G.; Bajjalieh, Sandra M.; Chiu, Daniel T.

2013-01-01

This protocol describes a method to determine both the average number and variance of proteins in the few to tens of copies in isolated cellular compartments, such as organelles and protein complexes. Other currently available protein quantification techniques either provide an average number but lack information on the variance or are not suitable for reliably counting proteins present in the few to tens of copies. This protocol entails labeling the cellular compartment with fluorescent primary-secondary antibody complexes, TIRF (total internal reflection fluorescence) microscopy imaging of the cellular compartment, digital image analysis, and deconvolution of the fluorescence intensity data. A minimum of 2.5 days is required to complete the labeling, imaging, and analysis of a set of samples. As an illustrative example, we describe in detail the procedure used to determine the copy number of proteins in synaptic vesicles. The same procedure can be applied to other organelles or signaling complexes. PMID:22094731

Apparatus bias and place conditioning with ethanol in mice.

PubMed

Cunningham, Christopher L; Ferree, Nikole K; Howard, MacKenzie A

2003-12-01

Although the distinction between "biased" and "unbiased" is generally recognized as an important methodological issue in place conditioning, previous studies have not adequately addressed the distinction between a biased/unbiased apparatus and a biased/unbiased stimulus assignment procedure. Moreover, a review of the recent literature indicates that many reports (70% of 76 papers published in 2001) fail to provide adequate information about apparatus bias. This issue is important because the mechanisms underlying a drug's effect in the place-conditioning procedure may differ depending on whether the apparatus is biased or unbiased. The present studies were designed to assess the impact of apparatus bias and stimulus assignment procedure on ethanol-induced place conditioning in mice (DBA/2 J). A secondary goal was to compare various dependent variables commonly used to index conditioned place preference. Apparatus bias was manipulated by varying the combination of tactile (floor) cues available during preference tests. Experiment 1 used an unbiased apparatus in which the stimulus alternatives were equally preferred during a pre-test as indicated by the group average. Experiment 2 used a biased apparatus in which one of the stimuli was strongly preferred by most mice (mean % time on cue = 67%) during the pre-test. In both studies, the stimulus paired with drug (CS+) was assigned randomly (i.e., an "unbiased" stimulus assignment procedure). Experimental mice received four pairings of CS+ with ethanol (2 g/kg, i.p.) and four pairings of the alternative stimulus (CS-) with saline; control mice received saline on both types of trial. Each experiment concluded with a 60-min choice test. With the unbiased apparatus (experiment 1), significant place conditioning was obtained regardless of whether drug was paired with the subject's initially preferred or non-preferred stimulus. However, with the biased apparatus (experiment 2), place conditioning was apparent only when ethanol was paired with the initially non-preferred cue, and not when it was paired with the initially preferred cue. These conclusions held regardless of which dependent variable was used to index place conditioning, but only if the counterbalancing factor was included in statistical analyses. These studies indicate that apparatus bias plays a major role in determining whether biased assignment of an ethanol-paired stimulus affects ability to demonstrate conditioned place preference. Ethanol's ability to produce conditioned place preference in an unbiased apparatus, regardless of the direction of the initial cue bias, supports previous studies that interpret such findings as evidence of a primary rewarding drug effect. Moreover, these studies suggest that the asymmetrical outcome observed in the biased apparatus is most likely due to a measurement problem (e.g., ceiling effect) rather than to an interaction between the drug's effect and an unconditioned motivational response (e.g., "anxiety") to the initially non-preferred stimulus. More generally, these findings illustrate the importance of providing clear information on apparatus bias in all place-conditioning studies.

Testing for gene-environment interaction under exposure misspecification.

PubMed

Sun, Ryan; Carroll, Raymond J; Christiani, David C; Lin, Xihong

2017-11-09

Complex interplay between genetic and environmental factors characterizes the etiology of many diseases. Modeling gene-environment (GxE) interactions is often challenged by the unknown functional form of the environment term in the true data-generating mechanism. We study the impact of misspecification of the environmental exposure effect on inference for the GxE interaction term in linear and logistic regression models. We first examine the asymptotic bias of the GxE interaction regression coefficient, allowing for confounders as well as arbitrary misspecification of the exposure and confounder effects. For linear regression, we show that under gene-environment independence and some confounder-dependent conditions, when the environment effect is misspecified, the regression coefficient of the GxE interaction can be unbiased. However, inference on the GxE interaction is still often incorrect. In logistic regression, we show that the regression coefficient is generally biased if the genetic factor is associated with the outcome directly or indirectly. Further, we show that the standard robust sandwich variance estimator for the GxE interaction does not perform well in practical GxE studies, and we provide an alternative testing procedure that has better finite sample properties. © 2017, The International Biometric Society.

Extending large-scale forest inventories to assess urban forests.

PubMed

Corona, Piermaria; Agrimi, Mariagrazia; Baffetta, Federica; Barbati, Anna; Chiriacò, Maria Vincenza; Fattorini, Lorenzo; Pompei, Enrico; Valentini, Riccardo; Mattioli, Walter

2012-03-01

Urban areas are continuously expanding today, extending their influence on an increasingly large proportion of woods and trees located in or nearby urban and urbanizing areas, the so-called urban forests. Although these forests have the potential for significantly improving the quality the urban environment and the well-being of the urban population, data to quantify the extent and characteristics of urban forests are still lacking or fragmentary on a large scale. In this regard, an expansion of the domain of multipurpose forest inventories like National Forest Inventories (NFIs) towards urban forests would be required. To this end, it would be convenient to exploit the same sampling scheme applied in NFIs to assess the basic features of urban forests. This paper considers approximately unbiased estimators of abundance and coverage of urban forests, together with estimators of the corresponding variances, which can be achieved from the first phase of most large-scale forest inventories. A simulation study is carried out in order to check the performance of the considered estimators under various situations involving the spatial distribution of the urban forests over the study area. An application is worked out on the data from the Italian NFI.

Statistical and sampling issues when using multiple particle tracking

NASA Astrophysics Data System (ADS)

Savin, Thierry; Doyle, Patrick S.

2007-08-01

Video microscopy can be used to simultaneously track several microparticles embedded in a complex material. The trajectories are used to extract a sample of displacements at random locations in the material. From this sample, averaged quantities characterizing the dynamics of the probes are calculated to evaluate structural and/or mechanical properties of the assessed material. However, the sampling of measured displacements in heterogeneous systems is singular because the volume of observation with video microscopy is finite. By carefully characterizing the sampling design in the experimental output of the multiple particle tracking technique, we derive estimators for the mean and variance of the probes’ dynamics that are independent of the peculiar statistical characteristics. We expose stringent tests of these estimators using simulated and experimental complex systems with a known heterogeneous structure. Up to a certain fundamental limitation, which we characterize through a material degree of sampling by the embedded probe tracking, these estimators can be applied to quantify the heterogeneity of a material, providing an original and intelligible kind of information on complex fluid properties. More generally, we show that the precise assessment of the statistics in the multiple particle tracking output sample of observations is essential in order to provide accurate unbiased measurements.

Mixed-Poisson Point Process with Partially-Observed Covariates: Ecological Momentary Assessment of Smoking.

PubMed

Neustifter, Benjamin; Rathbun, Stephen L; Shiffman, Saul

2012-01-01

Ecological Momentary Assessment is an emerging method of data collection in behavioral research that may be used to capture the times of repeated behavioral events on electronic devices, and information on subjects' psychological states through the electronic administration of questionnaires at times selected from a probability-based design as well as the event times. A method for fitting a mixed Poisson point process model is proposed for the impact of partially-observed, time-varying covariates on the timing of repeated behavioral events. A random frailty is included in the point-process intensity to describe variation among subjects in baseline rates of event occurrence. Covariate coefficients are estimated using estimating equations constructed by replacing the integrated intensity in the Poisson score equations with a design-unbiased estimator. An estimator is also proposed for the variance of the random frailties. Our estimators are robust in the sense that no model assumptions are made regarding the distribution of the time-varying covariates or the distribution of the random effects. However, subject effects are estimated under gamma frailties using an approximate hierarchical likelihood. The proposed approach is illustrated using smoking data.

A Multiphase Model for the Intracluster Medium

NASA Technical Reports Server (NTRS)

Nagai, Daisuke; Sulkanen, Martin E.; Evrard, August E.

1999-01-01

Constraints on the clustered mass density of the universe derived from the observed population mean intracluster gas fraction of x-ray clusters may be biased by reliance on a single-phase assumption for the thermodynamic structure of the intracluster medium (ICM). We propose a descriptive model for multiphase structure in which a spherically symmetric ICM contains isobaric density perturbations with a radially dependent variance. Fixing the x-ray emission and emission weighted temperature, we explore two independently observable signatures of the model in the parameter space. For bremsstrahlung dominated emission, the central Sunyaev-Zel'dovich (SZ) decrement in the multiphase case is increased over the single-phase case and multiphase x-ray spectra in the range 0.1-20 keV are flatter in the continuum and exhibit stronger low energy emission lines than their single-phase counterpart. We quantify these effects for a fiducial 10e8 K cluster and demonstrate how the combination of SZ and x-ray spectroscopy can be used to identify a preferred location in the plane of the model parameter space. From these parameters the correct value of mean intracluster gas fraction in the multiphase model results, allowing an unbiased estimate of clustered mass density to he recovered.

Multiscale measurement error models for aggregated small area health data.

PubMed

Aregay, Mehreteab; Lawson, Andrew B; Faes, Christel; Kirby, Russell S; Carroll, Rachel; Watjou, Kevin

2016-08-01

Spatial data are often aggregated from a finer (smaller) to a coarser (larger) geographical level. The process of data aggregation induces a scaling effect which smoothes the variation in the data. To address the scaling problem, multiscale models that link the convolution models at different scale levels via the shared random effect have been proposed. One of the main goals in aggregated health data is to investigate the relationship between predictors and an outcome at different geographical levels. In this paper, we extend multiscale models to examine whether a predictor effect at a finer level hold true at a coarser level. To adjust for predictor uncertainty due to aggregation, we applied measurement error models in the framework of multiscale approach. To assess the benefit of using multiscale measurement error models, we compare the performance of multiscale models with and without measurement error in both real and simulated data. We found that ignoring the measurement error in multiscale models underestimates the regression coefficient, while it overestimates the variance of the spatially structured random effect. On the other hand, accounting for the measurement error in multiscale models provides a better model fit and unbiased parameter estimates. © The Author(s) 2016.

[An ADAA model and its analysis method for agronomic traits based on the double-cross mating design].

PubMed

Xu, Z C; Zhu, J

2000-01-01

According to the double-cross mating design and using principles of Cockerham's general genetic model, a genetic model with additive, dominance and epistatic effects (ADAA model) was proposed for the analysis of agronomic traits. Components of genetic effects were derived for different generations. Monte Carlo simulation was conducted for analyzing the ADAA model and its reduced AD model by using different generations. It was indicated that genetic variance components could be estimated without bias by MINQUE(1) method and genetic effects could be predicted effectively by AUP method; at least three generations (including parent, F1 of single cross and F1 of double-cross) were necessary for analyzing the ADAA model and only two generations (including parent and F1 of double-cross) were enough for the reduced AD model. When epistatic effects were taken into account, a new approach for predicting the heterosis of agronomic traits of double-crosses was given on the basis of unbiased prediction of genotypic merits of parents and their crosses. In addition, genotype x environment interaction effects and interaction heterosis due to G x E interaction were discussed briefly.

Vector space methods of photometric analysis. II - Refinement of the MK grid for B stars. III - The two components of ultraviolet reddening

NASA Technical Reports Server (NTRS)

Massa, D.

1980-01-01

This paper discusses systematic errors which arise from exclusive use of the MK system to determine reddening. It is found that implementation of uvby, beta photometry to refine the qualitative MK grid substantially reduces stellar mismatch error. A working definition of 'identical' ubvy, beta types is investigated and the relationship of uvby to B-V color excesses is determined. A comparison is also made of the hydrogen based uvby, beta types with the MK system based on He and metal lines. A small core correlated effective temperature luminosity error in the MK system for the early B stars is observed along with a breakdown of the MK luminosity criteria for the late B stars. The second part investigates the wavelength dependence of interstellar extinction in the ultraviolet wavelength range observed with the TD-1 satellite. In this study the sets of identical stars employed to find reddening are determined more precisely than in previous studies and consist only of normal, nonsupergiant stars. A multivariate analysis of variance techniques in an unbiased coordinate system is used for determining the wavelength dependence of reddening.

Random regression models using Legendre orthogonal polynomials to evaluate the milk production of Alpine goats.

PubMed

Silva, F G; Torres, R A; Brito, L F; Euclydes, R F; Melo, A L P; Souza, N O; Ribeiro, J I; Rodrigues, M T

2013-12-11

The objective of this study was to identify the best random regression model using Legendre orthogonal polynomials to evaluate Alpine goats genetically and to estimate the parameters for test day milk yield. On the test day, we analyzed 20,710 records of milk yield of 667 goats from the Goat Sector of the Universidade Federal de Viçosa. The evaluated models had combinations of distinct fitting orders for polynomials (2-5), random genetic (1-7), and permanent environmental (1-7) fixed curves and a number of classes for residual variance (2, 4, 5, and 6). WOMBAT software was used for all genetic analyses. A random regression model using the best Legendre orthogonal polynomial for genetic evaluation of milk yield on the test day of Alpine goats considered a fixed curve of order 4, curve of genetic additive effects of order 2, curve of permanent environmental effects of order 7, and a minimum of 5 classes of residual variance because it was the most economical model among those that were equivalent to the complete model by the likelihood ratio test. Phenotypic variance and heritability were higher at the end of the lactation period, indicating that the length of lactation has more genetic components in relation to the production peak and persistence. It is very important that the evaluation utilizes the best combination of fixed, genetic additive and permanent environmental regressions, and number of classes of heterogeneous residual variance for genetic evaluation using random regression models, thereby enhancing the precision and accuracy of the estimates of parameters and prediction of genetic values.

A training image evaluation and selection method based on minimum data event distance for multiple-point geostatistics

NASA Astrophysics Data System (ADS)

Feng, Wenjie; Wu, Shenghe; Yin, Yanshu; Zhang, Jiajia; Zhang, Ke

2017-07-01

A training image (TI) can be regarded as a database of spatial structures and their low to higher order statistics used in multiple-point geostatistics (MPS) simulation. Presently, there are a number of methods to construct a series of candidate TIs (CTIs) for MPS simulation based on a modeler's subjective criteria. The spatial structures of TIs are often various, meaning that the compatibilities of different CTIs with the conditioning data are different. Therefore, evaluation and optimal selection of CTIs before MPS simulation is essential. This paper proposes a CTI evaluation and optimal selection method based on minimum data event distance (MDevD). In the proposed method, a set of MDevD properties are established through calculation of the MDevD of conditioning data events in each CTI. Then, CTIs are evaluated and ranked according to the mean value and variance of the MDevD properties. The smaller the mean value and variance of an MDevD property are, the more compatible the corresponding CTI is with the conditioning data. In addition, data events with low compatibility in the conditioning data grid can be located to help modelers select a set of complementary CTIs for MPS simulation. The MDevD property can also help to narrow the range of the distance threshold for MPS simulation. The proposed method was evaluated using three examples: a 2D categorical example, a 2D continuous example, and an actual 3D oil reservoir case study. To illustrate the method, a C++ implementation of the method is attached to the paper.

Cortical neuron activation induced by electromagnetic stimulation: a quantitative analysis via modelling and simulation.

PubMed

Wu, Tiecheng; Fan, Jie; Lee, Kim Seng; Li, Xiaoping

2016-02-01

Previous simulation works concerned with the mechanism of non-invasive neuromodulation has isolated many of the factors that can influence stimulation potency, but an inclusive account of the interplay between these factors on realistic neurons is still lacking. To give a comprehensive investigation on the stimulation-evoked neuronal activation, we developed a simulation scheme which incorporates highly detailed physiological and morphological properties of pyramidal cells. The model was implemented on a multitude of neurons; their thresholds and corresponding activation points with respect to various field directions and pulse waveforms were recorded. The results showed that the simulated thresholds had a minor anisotropy and reached minimum when the field direction was parallel to the dendritic-somatic axis; the layer 5 pyramidal cells always had lower thresholds but substantial variances were also observed within layers; reducing pulse length could magnify the threshold values as well as the variance; tortuosity and arborization of axonal segments could obstruct action potential initiation. The dependence of the initiation sites on both the orientation and the duration of the stimulus implies that the cellular excitability might represent the result of the competition between various firing-capable axonal components, each with a unique susceptibility determined by the local geometry. Moreover, the measurements obtained in simulation intimately resemble recordings in physiological and clinical studies, which seems to suggest that, with minimum simplification of the neuron model, the cable theory-based simulation approach can have sufficient verisimilitude to give quantitatively accurate evaluation of cell activities in response to the externally applied field.

Local finite element enrichment strategies for 2D contact computations and a corresponding post-processing scheme

NASA Astrophysics Data System (ADS)

Sauer, Roger A.

2013-08-01

Recently an enriched contact finite element formulation has been developed that substantially increases the accuracy of contact computations while keeping the additional numerical effort at a minimum reported by Sauer (Int J Numer Meth Eng, 87: 593-616, 2011). Two enrich-ment strategies were proposed, one based on local p-refinement using Lagrange interpolation and one based on Hermite interpolation that produces C 1-smoothness on the contact surface. Both classes, which were initially considered for the frictionless Signorini problem, are extended here to friction and contact between deformable bodies. For this, a symmetric contact formulation is used that allows the unbiased treatment of both contact partners. This paper also proposes a post-processing scheme for contact quantities like the contact pressure. The scheme, which provides a more accurate representation than the raw data, is based on an averaging procedure that is inspired by mortar formulations. The properties of the enrichment strategies and the corresponding post-processing scheme are illustrated by several numerical examples considering sliding and peeling contact in the presence of large deformations.

A Smoluchowski model of crystallization dynamics of small colloidal clusters

NASA Astrophysics Data System (ADS)

Beltran-Villegas, Daniel J.; Sehgal, Ray M.; Maroudas, Dimitrios; Ford, David M.; Bevan, Michael A.

2011-10-01

We investigate the dynamics of colloidal crystallization in a 32-particle system at a fixed value of interparticle depletion attraction that produces coexisting fluid and solid phases. Free energy landscapes (FELs) and diffusivity landscapes (DLs) are obtained as coefficients of 1D Smoluchowski equations using as order parameters either the radius of gyration or the average crystallinity. FELs and DLs are estimated by fitting the Smoluchowski equations to Brownian dynamics (BD) simulations using either linear fits to locally initiated trajectories or global fits to unbiased trajectories using Bayesian inference. The resulting FELs are compared to Monte Carlo Umbrella Sampling results. The accuracy of the FELs and DLs for modeling colloidal crystallization dynamics is evaluated by comparing mean first-passage times from BD simulations with analytical predictions using the FEL and DL models. While the 1D models accurately capture dynamics near the free energy minimum fluid and crystal configurations, predictions near the transition region are not quantitatively accurate. A preliminary investigation of ensemble averaged 2D order parameter trajectories suggests that 2D models are required to capture crystallization dynamics in the transition region.

Unbiased All-Optical Random-Number Generator

NASA Astrophysics Data System (ADS)

Steinle, Tobias; Greiner, Johannes N.; Wrachtrup, Jörg; Giessen, Harald; Gerhardt, Ilja

2017-10-01

The generation of random bits is of enormous importance in modern information science. Cryptographic security is based on random numbers which require a physical process for their generation. This is commonly performed by hardware random-number generators. These often exhibit a number of problems, namely experimental bias, memory in the system, and other technical subtleties, which reduce the reliability in the entropy estimation. Further, the generated outcome has to be postprocessed to "iron out" such spurious effects. Here, we present a purely optical randomness generator, based on the bistable output of an optical parametric oscillator. Detector noise plays no role and postprocessing is reduced to a minimum. Upon entering the bistable regime, initially the resulting output phase depends on vacuum fluctuations. Later, the phase is rigidly locked and can be well determined versus a pulse train, which is derived from the pump laser. This delivers an ambiguity-free output, which is reliably detected and associated with a binary outcome. The resulting random bit stream resembles a perfect coin toss and passes all relevant randomness measures. The random nature of the generated binary outcome is furthermore confirmed by an analysis of resulting conditional entropies.

Estimation of joint stiffness with a compliant load.

PubMed

Ludvig, Daniel; Kearney, Robert E

2009-01-01

Joint stiffness defines the dynamic relationship between the position of the joint and the torque acting about it. It consists of two components: intrinsic and reflex stiffness. Many previous studies have investigated joint stiffness in an open-loop environment, because the current algorithm in use is an open-loop algorithm. This paper explores issues related to the estimation of joint stiffness when subjects interact with compliant loads. First, we show analytically how the bias in closed-loop estimates of joint stiffness depends on the properties of the load, the noise power, and length of the estimated impulse response functions (IRF). We then demonstrate with simulations that the open-loop analysis will fail completely for an elastic load but may succeed for an inertial load. We further show that the open-loop analysis can yield unbiased results with an inertial load and document IRF length, signal-to-noise ratio needed, and minimum inertia needed for the analysis to succeed. Thus, by using a load with a properly selected inertia, open-loop analysis can be used under closed-loop conditions.

Reply to ''Comment on 'Mutually unbiased bases, orthogonal Latin squares, and hidden-variable models'''

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

Paterek, Tomasz; Dakic, Borivoje; Brukner, Caslav

In this Reply to the preceding Comment by Hall and Rao [Phys. Rev. A 83, 036101 (2011)], we motivate terminology of our original paper and point out that further research is needed in order to (dis)prove the claimed link between every orthogonal Latin square of order being a power of a prime and a mutually unbiased basis.

Generation and evaluation of an ultra-high-field atlas with applications in DBS planning

NASA Astrophysics Data System (ADS)

Wang, Brian T.; Poirier, Stefan; Guo, Ting; Parrent, Andrew G.; Peters, Terry M.; Khan, Ali R.

2016-03-01

Purpose Deep brain stimulation (DBS) is a common treatment for Parkinson's disease (PD) and involves the use of brain atlases or intrinsic landmarks to estimate the location of target deep brain structures, such as the subthalamic nucleus (STN) and the globus pallidus pars interna (GPi). However, these structures can be difficult to localize with conventional clinical magnetic resonance imaging (MRI), and thus targeting can be prone to error. Ultra-high-field imaging at 7T has the ability to clearly resolve these structures and thus atlases built with these data have the potential to improve targeting accuracy. Methods T1 and T2-weighted images of 12 healthy control subjects were acquired using a 7T MR scanner. These images were then used with groupwise registration to generate an unbiased average template with T1w and T2w contrast. Deep brain structures were manually labelled in each subject by two raters and rater reliability was assessed. We compared the use of this unbiased atlas with two other methods of atlas-based segmentation (single-template and multi-template) for subthalamic nucleus (STN) segmentation on 7T MRI data. We also applied this atlas to clinical DBS data acquired at 1.5T to evaluate its efficacy for DBS target localization as compared to using a standard atlas. Results The unbiased templates provide superb detail of subcortical structures. Through one-way ANOVA tests, the unbiased template is significantly (p <0.05) more accurate than a single-template in atlas-based segmentation and DBS target localization tasks. Conclusion The generated unbiased averaged templates provide better visualization of deep brain nuclei and an increase in accuracy over single-template and lower field strength atlases.

Extending unbiased stereology of brain ultrastructure to three-dimensional volumes

NASA Technical Reports Server (NTRS)

Fiala, J. C.; Harris, K. M.; Koslow, S. H. (Principal Investigator)

2001-01-01

OBJECTIVE: Analysis of brain ultrastructure is needed to reveal how neurons communicate with one another via synapses and how disease processes alter this communication. In the past, such analyses have usually been based on single or paired sections obtained by electron microscopy. Reconstruction from multiple serial sections provides a much needed, richer representation of the three-dimensional organization of the brain. This paper introduces a new reconstruction system and new methods for analyzing in three dimensions the location and ultrastructure of neuronal components, such as synapses, which are distributed non-randomly throughout the brain. DESIGN AND MEASUREMENTS: Volumes are reconstructed by defining transformations that align the entire area of adjacent sections. Whole-field alignment requires rotation, translation, skew, scaling, and second-order nonlinear deformations. Such transformations are implemented by a linear combination of bivariate polynomials. Computer software for generating transformations based on user input is described. Stereological techniques for assessing structural distributions in reconstructed volumes are the unbiased bricking, disector, unbiased ratio, and per-length counting techniques. A new general method, the fractional counter, is also described. This unbiased technique relies on the counting of fractions of objects contained in a test volume. A volume of brain tissue from stratum radiatum of hippocampal area CA1 is reconstructed and analyzed for synaptic density to demonstrate and compare the techniques. RESULTS AND CONCLUSIONS: Reconstruction makes practicable volume-oriented analysis of ultrastructure using such techniques as the unbiased bricking and fractional counter methods. These analysis methods are less sensitive to the section-to-section variations in counts and section thickness, factors that contribute to the inaccuracy of other stereological methods. In addition, volume reconstruction facilitates visualization and modeling of structures and analysis of three-dimensional relationships such as synaptic connectivity.

An Unbiased Method To Build Benchmarking Sets for Ligand-Based Virtual Screening and its Application To GPCRs

PubMed Central

2015-01-01

Benchmarking data sets have become common in recent years for the purpose of virtual screening, though the main focus had been placed on the structure-based virtual screening (SBVS) approaches. Due to the lack of crystal structures, there is great need for unbiased benchmarking sets to evaluate various ligand-based virtual screening (LBVS) methods for important drug targets such as G protein-coupled receptors (GPCRs). To date these ready-to-apply data sets for LBVS are fairly limited, and the direct usage of benchmarking sets designed for SBVS could bring the biases to the evaluation of LBVS. Herein, we propose an unbiased method to build benchmarking sets for LBVS and validate it on a multitude of GPCRs targets. To be more specific, our methods can (1) ensure chemical diversity of ligands, (2) maintain the physicochemical similarity between ligands and decoys, (3) make the decoys dissimilar in chemical topology to all ligands to avoid false negatives, and (4) maximize spatial random distribution of ligands and decoys. We evaluated the quality of our Unbiased Ligand Set (ULS) and Unbiased Decoy Set (UDS) using three common LBVS approaches, with Leave-One-Out (LOO) Cross-Validation (CV) and a metric of average AUC of the ROC curves. Our method has greatly reduced the “artificial enrichment” and “analogue bias” of a published GPCRs benchmarking set, i.e., GPCR Ligand Library (GLL)/GPCR Decoy Database (GDD). In addition, we addressed an important issue about the ratio of decoys per ligand and found that for a range of 30 to 100 it does not affect the quality of the benchmarking set, so we kept the original ratio of 39 from the GLL/GDD. PMID:24749745

An unbiased method to build benchmarking sets for ligand-based virtual screening and its application to GPCRs.

PubMed

Xia, Jie; Jin, Hongwei; Liu, Zhenming; Zhang, Liangren; Wang, Xiang Simon

2014-05-27

Benchmarking data sets have become common in recent years for the purpose of virtual screening, though the main focus had been placed on the structure-based virtual screening (SBVS) approaches. Due to the lack of crystal structures, there is great need for unbiased benchmarking sets to evaluate various ligand-based virtual screening (LBVS) methods for important drug targets such as G protein-coupled receptors (GPCRs). To date these ready-to-apply data sets for LBVS are fairly limited, and the direct usage of benchmarking sets designed for SBVS could bring the biases to the evaluation of LBVS. Herein, we propose an unbiased method to build benchmarking sets for LBVS and validate it on a multitude of GPCRs targets. To be more specific, our methods can (1) ensure chemical diversity of ligands, (2) maintain the physicochemical similarity between ligands and decoys, (3) make the decoys dissimilar in chemical topology to all ligands to avoid false negatives, and (4) maximize spatial random distribution of ligands and decoys. We evaluated the quality of our Unbiased Ligand Set (ULS) and Unbiased Decoy Set (UDS) using three common LBVS approaches, with Leave-One-Out (LOO) Cross-Validation (CV) and a metric of average AUC of the ROC curves. Our method has greatly reduced the "artificial enrichment" and "analogue bias" of a published GPCRs benchmarking set, i.e., GPCR Ligand Library (GLL)/GPCR Decoy Database (GDD). In addition, we addressed an important issue about the ratio of decoys per ligand and found that for a range of 30 to 100 it does not affect the quality of the benchmarking set, so we kept the original ratio of 39 from the GLL/GDD.

An Example of an Improvable Rao-Blackwell Improvement, Inefficient Maximum Likelihood Estimator, and Unbiased Generalized Bayes Estimator.

PubMed

Galili, Tal; Meilijson, Isaac

2016-01-02

The Rao-Blackwell theorem offers a procedure for converting a crude unbiased estimator of a parameter θ into a "better" one, in fact unique and optimal if the improvement is based on a minimal sufficient statistic that is complete. In contrast, behind every minimal sufficient statistic that is not complete, there is an improvable Rao-Blackwell improvement. This is illustrated via a simple example based on the uniform distribution, in which a rather natural Rao-Blackwell improvement is uniformly improvable. Furthermore, in this example the maximum likelihood estimator is inefficient, and an unbiased generalized Bayes estimator performs exceptionally well. Counterexamples of this sort can be useful didactic tools for explaining the true nature of a methodology and possible consequences when some of the assumptions are violated. [Received December 2014. Revised September 2015.].

Quantum key distribution for composite dimensional finite systems

NASA Astrophysics Data System (ADS)

Shalaby, Mohamed; Kamal, Yasser

2017-06-01

The application of quantum mechanics contributes to the field of cryptography with very important advantage as it offers a mechanism for detecting the eavesdropper. The pioneering work of quantum key distribution uses mutually unbiased bases (MUBs) to prepare and measure qubits (or qudits). Weak mutually unbiased bases (WMUBs) have weaker properties than MUBs properties, however, unlike MUBs, a complete set of WMUBs can be constructed for systems with composite dimensions. In this paper, we study the use of weak mutually unbiased bases (WMUBs) in quantum key distribution for composite dimensional finite systems. We prove that the security analysis of using a complete set of WMUBs to prepare and measure the quantum states in the generalized BB84 protocol, gives better results than using the maximum number of MUBs that can be constructed, when they are analyzed against the intercept and resend attack.

Unbiased Estimation of Refractive State of Aberrated Eyes

PubMed Central

Martin, Jesson; Vasudevan, Balamurali; Himebaugh, Nikole; Bradley, Arthur; Thibos, Larry

2011-01-01

To identify unbiased methods for estimating the target vergence required to maximize visual acuity based on wavefront aberration measurements. Experiments were designed to minimize the impact of confounding factors that have hampered previous research. Objective wavefront refractions and subjective acuity refractions were obtained for the same monochromatic wavelength. Accommodation and pupil fluctuations were eliminated by cycloplegia. Unbiased subjective refractions that maximize visual acuity for high contrast letters were performed with a computer controlled forced choice staircase procedure, using 0.125 diopter steps of defocus. All experiments were performed for two pupil diameters (3mm and 6mm). As reported in the literature, subjective refractive error does not change appreciably when the pupil dilates. For 3 mm pupils most metrics yielded objective refractions that were about 0.1D more hyperopic than subjective acuity refractions. When pupil diameter increased to 6 mm, this bias changed in the myopic direction and the variability between metrics also increased. These inaccuracies were small compared to the precision of the measurements, which implies that most metrics provided unbiased estimates of refractive state for medium and large pupils. A variety of image quality metrics may be used to determine ocular refractive state for monochromatic (635nm) light, thereby achieving accurate results without the need for empirical correction factors. PMID:21777601

Circulating tumor cell detection: A direct comparison between negative and unbiased enrichment in lung cancer.

PubMed

Xu, Yan; Liu, Biao; Ding, Fengan; Zhou, Xiaodie; Tu, Pin; Yu, Bo; He, Yan; Huang, Peilin

2017-06-01

Circulating tumor cells (CTCs), isolated as a 'liquid biopsy', may provide important diagnostic and prognostic information. Therefore, rapid, reliable and unbiased detection of CTCs are required for routine clinical analyses. It was demonstrated that negative enrichment, an epithelial marker-independent technique for isolating CTCs, exhibits a better efficiency in the detection of CTCs compared with positive enrichment techniques that only use specific anti-epithelial cell adhesion molecules. However, negative enrichment techniques incur significant cell loss during the isolation procedure, and as it is a method that uses only one type of antibody, it is inherently biased. The detection procedure and identification of cell types also relies on skilled and experienced technicians. In the present study, the detection sensitivity of using negative enrichment and a previously described unbiased detection method was compared. The results revealed that unbiased detection methods may efficiently detect >90% of cancer cells in blood samples containing CTCs. By contrast, only 40-60% of CTCs were detected by negative enrichment. Additionally, CTCs were identified in >65% of patients with stage I/II lung cancer. This simple yet efficient approach may achieve a high level of sensitivity. It demonstrates a potential for the large-scale clinical implementation of CTC-based diagnostic and prognostic strategies.

Building unbiased estimators from non-gaussian likelihoods with application to shear estimation

DOE PAGES

Madhavacheril, Mathew S.; McDonald, Patrick; Sehgal, Neelima; ...

2015-01-15

We develop a general framework for generating estimators of a given quantity which are unbiased to a given order in the difference between the true value of the underlying quantity and the fiducial position in theory space around which we expand the likelihood. We apply this formalism to rederive the optimal quadratic estimator and show how the replacement of the second derivative matrix with the Fisher matrix is a generic way of creating an unbiased estimator (assuming choice of the fiducial model is independent of data). Next we apply the approach to estimation of shear lensing, closely following the workmore » of Bernstein and Armstrong (2014). Our first order estimator reduces to their estimator in the limit of zero shear, but it also naturally allows for the case of non-constant shear and the easy calculation of correlation functions or power spectra using standard methods. Both our first-order estimator and Bernstein and Armstrong’s estimator exhibit a bias which is quadratic in true shear. Our third-order estimator is, at least in the realm of the toy problem of Bernstein and Armstrong, unbiased to 0.1% in relative shear errors Δg/g for shears up to |g| = 0.2.« less

Building unbiased estimators from non-Gaussian likelihoods with application to shear estimation

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

Madhavacheril, Mathew S.; Sehgal, Neelima; McDonald, Patrick

2015-01-01

We develop a general framework for generating estimators of a given quantity which are unbiased to a given order in the difference between the true value of the underlying quantity and the fiducial position in theory space around which we expand the likelihood. We apply this formalism to rederive the optimal quadratic estimator and show how the replacement of the second derivative matrix with the Fisher matrix is a generic way of creating an unbiased estimator (assuming choice of the fiducial model is independent of data). Next we apply the approach to estimation of shear lensing, closely following the workmore » of Bernstein and Armstrong (2014). Our first order estimator reduces to their estimator in the limit of zero shear, but it also naturally allows for the case of non-constant shear and the easy calculation of correlation functions or power spectra using standard methods. Both our first-order estimator and Bernstein and Armstrong's estimator exhibit a bias which is quadratic in true shear. Our third-order estimator is, at least in the realm of the toy problem of Bernstein and Armstrong, unbiased to 0.1% in relative shear errors Δg/g for shears up to |g|=0.2.« less

Potential Seasonal Terrestrial Water Storage Monitoring from GPS Vertical Displacements: A Case Study in the Lower Three-Rivers Headwater Region, China.

PubMed

Zhang, Bao; Yao, Yibin; Fok, Hok Sum; Hu, Yufeng; Chen, Qiang

2016-09-19

This study uses the observed vertical displacements of Global Positioning System (GPS) time series obtained from the Crustal Movement Observation Network of China (CMONOC) with careful pre- and post-processing to estimate the seasonal crustal deformation in response to the hydrological loading in lower three-rivers headwater region of southwest China, followed by inferring the annual EWH changes through geodetic inversion methods. The Helmert Variance Component Estimation (HVCE) and the Minimum Mean Square Error (MMSE) criterion were successfully employed. The GPS inferred EWH changes agree well qualitatively with the Gravity Recovery and Climate Experiment (GRACE)-inferred and the Global Land Data Assimilation System (GLDAS)-inferred EWH changes, with a discrepancy of 3.2-3.9 cm and 4.8-5.2 cm, respectively. In the research areas, the EWH changes in the Lancang basin is larger than in the other regions, with a maximum of 21.8-24.7 cm and a minimum of 3.1-6.9 cm.

Development of an Empirical Model for Optimization of Machining Parameters to Minimize Power Consumption

NASA Astrophysics Data System (ADS)

Kant Garg, Girish; Garg, Suman; Sangwan, K. S.

2018-04-01

The manufacturing sector consumes huge energy demand and the machine tools used in this sector have very less energy efficiency. Selection of the optimum machining parameters for machine tools is significant for energy saving and for reduction of environmental emission. In this work an empirical model is developed to minimize the power consumption using response surface methodology. The experiments are performed on a lathe machine tool during the turning of AISI 6061 Aluminum with coated tungsten inserts. The relationship between the power consumption and machining parameters is adequately modeled. This model is used for formulation of minimum power consumption criterion as a function of optimal machining parameters using desirability function approach. The influence of machining parameters on the energy consumption has been found using the analysis of variance. The validation of the developed empirical model is proved using the confirmation experiments. The results indicate that the developed model is effective and has potential to be adopted by the industry for minimum power consumption of machine tools.

Blind Channel Equalization with Colored Source Based on Constrained Optimization Methods

NASA Astrophysics Data System (ADS)

Wang, Yunhua; DeBrunner, Linda; DeBrunner, Victor; Zhou, Dayong

2008-12-01

Tsatsanis and Xu have applied the constrained minimum output variance (CMOV) principle to directly blind equalize a linear channel—a technique that has proven effective with white inputs. It is generally assumed in the literature that their CMOV method can also effectively equalize a linear channel with a colored source. In this paper, we prove that colored inputs will cause the equalizer to incorrectly converge due to inadequate constraints. We also introduce a new blind channel equalizer algorithm that is based on the CMOV principle, but with a different constraint that will correctly handle colored sources. Our proposed algorithm works for channels with either white or colored inputs and performs equivalently to the trained minimum mean-square error (MMSE) equalizer under high SNR. Thus, our proposed algorithm may be regarded as an extension of the CMOV algorithm proposed by Tsatsanis and Xu. We also introduce several methods to improve the performance of our introduced algorithm in the low SNR condition. Simulation results show the superior performance of our proposed methods.

Comparative efficacy of storage bags, storability and damage potential of bruchid beetle.

PubMed

Harish, G; Nataraja, M V; Ajay, B C; Holajjer, Prasanna; Savaliya, S D; Gedia, M V

2014-12-01

Groundnut during storage is attacked by number of stored grain pests and management of these insect pests particularly bruchid beetle, Caryedon serratus (Oliver) is of prime importance as they directly damage the pod and kernels. In this regard different storage bags that could be used and duration up to which we can store groundnut has been studied. Super grain bag recorded minimum number of eggs laid and less damage and minimum weight loss in pods and kernels in comparison to other storage bags. Analysis of variance for multiple regression models were found to be significant in all bags for variables viz, number of eggs laid, damage in pods and kernels, weight loss in pods and kernels throughout the season. Multiple comparison results showed that there was a high probability of eggs laid and pod damage in lino bag, fertilizer bag and gunny bag, whereas super grain bag was found to be more effective in managing the C. serratus owing to very low air circulation.

A Robust Statistics Approach to Minimum Variance Portfolio Optimization

NASA Astrophysics Data System (ADS)

Yang, Liusha; Couillet, Romain; McKay, Matthew R.

2015-12-01

We study the design of portfolios under a minimum risk criterion. The performance of the optimized portfolio relies on the accuracy of the estimated covariance matrix of the portfolio asset returns. For large portfolios, the number of available market returns is often of similar order to the number of assets, so that the sample covariance matrix performs poorly as a covariance estimator. Additionally, financial market data often contain outliers which, if not correctly handled, may further corrupt the covariance estimation. We address these shortcomings by studying the performance of a hybrid covariance matrix estimator based on Tyler's robust M-estimator and on Ledoit-Wolf's shrinkage estimator while assuming samples with heavy-tailed distribution. Employing recent results from random matrix theory, we develop a consistent estimator of (a scaled version of) the realized portfolio risk, which is minimized by optimizing online the shrinkage intensity. Our portfolio optimization method is shown via simulations to outperform existing methods both for synthetic and real market data.

Noise sensitivity of portfolio selection in constant conditional correlation GARCH models

NASA Astrophysics Data System (ADS)

Varga-Haszonits, I.; Kondor, I.

2007-11-01

This paper investigates the efficiency of minimum variance portfolio optimization for stock price movements following the Constant Conditional Correlation GARCH process proposed by Bollerslev. Simulations show that the quality of portfolio selection can be improved substantially by computing optimal portfolio weights from conditional covariances instead of unconditional ones. Measurement noise can be further reduced by applying some filtering method on the conditional correlation matrix (such as Random Matrix Theory based filtering). As an empirical support for the simulation results, the analysis is also carried out for a time series of S&P500 stock prices.

Statistical indicators of collective behavior and functional clusters in gene networks of yeast

NASA Astrophysics Data System (ADS)

Živković, J.; Tadić, B.; Wick, N.; Thurner, S.

2006-03-01

We analyze gene expression time-series data of yeast (S. cerevisiae) measured along two full cell-cycles. We quantify these data by using q-exponentials, gene expression ranking and a temporal mean-variance analysis. We construct gene interaction networks based on correlation coefficients and study the formation of the corresponding giant components and minimum spanning trees. By coloring genes according to their cell function we find functional clusters in the correlation networks and functional branches in the associated trees. Our results suggest that a percolation point of functional clusters can be identified on these gene expression correlation networks.

Gravity anomalies, compensation mechanisms, and the geodynamics of western Ishtar Terra, Venus

NASA Technical Reports Server (NTRS)

Grimm, Robert E.; Phillips, Roger J.

1991-01-01

Pioneer Venus line-of-sight orbital accelerations were utilized to calculate the geoid and vertical gravity anomalies for western Ishtar Terra on various planes of altitude z sub 0. The apparent depth of isostatic compensation at z sub 0 = 1400 km is 180 + or - 20 km based on the usual method of minimum variance in the isostatic anomaly. An attempt is made here to explain this observation, as well as the regional elevation, peripheral mountain belts, and inferred age of western Ishtar Terra, in terms of one or three broad geodynamic models.

Minimal Model of Prey Localization through the Lateral-Line System

NASA Astrophysics Data System (ADS)

Franosch, Jan-Moritz P.; Sobotka, Marion C.; Elepfandt, Andreas; van Hemmen, J. Leo

2003-10-01

The clawed frog Xenopus is an aquatic predator catching prey at night by detecting water movements caused by its prey. We present a general method, a “minimal model” based on a minimum-variance estimator, to explain prey detection through the frog's many lateral-line organs, even in case several of them are defunct. We show how waveform reconstruction allows Xenopus' neuronal system to determine both the direction and the character of the prey and even to distinguish two simultaneous wave sources. The results can be applied to many aquatic amphibians, fish, or reptiles such as crocodilians.

Beamforming approaches for untethered, ultrasonic neural dust motes for cortical recording: a simulation study.

PubMed

Bertrand, Alexander; Seo, Dongjin; Maksimovic, Filip; Carmena, Jose M; Maharbiz, Michel M; Alon, Elad; Rabaey, Jan M

2014-01-01

In this paper, we examine the use of beamforming techniques to interrogate a multitude of neural implants in a distributed, ultrasound-based intra-cortical recording platform known as Neural Dust. We propose a general framework to analyze system design tradeoffs in the ultrasonic beamformer that extracts neural signals from modulated ultrasound waves that are backscattered by free-floating neural dust (ND) motes. Simulations indicate that high-resolution linearly-constrained minimum variance beamforming sufficiently suppresses interference from unselected ND motes and can be incorporated into the ND-based cortical recording system.

Statistical evaluation of metal fill widths for emulated metal fill in parasitic extraction methodology

NASA Astrophysics Data System (ADS)

J-Me, Teh; Noh, Norlaili Mohd.; Aziz, Zalina Abdul

2015-05-01

In the chip industry today, the key goal of a chip development organization is to develop and market chips within a short time frame to gain foothold on market share. This paper proposes a design flow around the area of parasitic extraction to improve the design cycle time. The proposed design flow utilizes the usage of metal fill emulation as opposed to the current flow which performs metal fill insertion directly. By replacing metal fill structures with an emulation methodology in earlier iterations of the design flow, this is targeted to help reduce runtime in fill insertion stage. Statistical design of experiments methodology utilizing the randomized complete block design was used to select an appropriate emulated metal fill width to improve emulation accuracy. The experiment was conducted on test cases of different sizes, ranging from 1000 gates to 21000 gates. The metal width was varied from 1 x minimum metal width to 6 x minimum metal width. Two-way analysis of variance and Fisher's least significant difference test were used to analyze the interconnect net capacitance values of the different test cases. This paper presents the results of the statistical analysis for the 45 nm process technology. The recommended emulated metal fill width was found to be 4 x the minimum metal width.

Claw length recommendations for dairy cow foot trimming

PubMed Central

Archer, S. C.; Newsome, R.; Dibble, H.; Sturrock, C. J.; Chagunda, M. G. G.; Mason, C. S.; Huxley, J. N.

2015-01-01

The aim was to describe variation in length of the dorsal hoof wall in contact with the dermis for cows on a single farm, and hence, derive minimum appropriate claw lengths for routine foot trimming. The hind feet of 68 Holstein-Friesian dairy cows were collected post mortem, and the internal structures were visualised using x-ray µCT. The internal distance from the proximal limit of the wall horn to the distal tip of the dermis was measured from cross-sectional sagittal images. A constant was added to allow for a minimum sole thickness of 5 mm and an average wall thickness of 8 mm. Data were evaluated using descriptive statistics and two-level linear regression models with claw nested within cow. Based on 219 claws, the recommended dorsal wall length from the proximal limit of hoof horn was up to 90 mm for 96 per cent of claws, and the median value was 83 mm. Dorsal wall length increased by 1 mm per year of age, yet 85 per cent of the null model variance remained unexplained. Overtrimming can have severe consequences; the authors propose that the minimum recommended claw length stated in training materials for all Holstein-Friesian cows should be increased to 90 mm. PMID:26220848

Estimating multilevel logistic regression models when the number of clusters is low: a comparison of different statistical software procedures.

PubMed

Austin, Peter C

2010-04-22

Multilevel logistic regression models are increasingly being used to analyze clustered data in medical, public health, epidemiological, and educational research. Procedures for estimating the parameters of such models are available in many statistical software packages. There is currently little evidence on the minimum number of clusters necessary to reliably fit multilevel regression models. We conducted a Monte Carlo study to compare the performance of different statistical software procedures for estimating multilevel logistic regression models when the number of clusters was low. We examined procedures available in BUGS, HLM, R, SAS, and Stata. We found that there were qualitative differences in the performance of different software procedures for estimating multilevel logistic models when the number of clusters was low. Among the likelihood-based procedures, estimation methods based on adaptive Gauss-Hermite approximations to the likelihood (glmer in R and xtlogit in Stata) or adaptive Gaussian quadrature (Proc NLMIXED in SAS) tended to have superior performance for estimating variance components when the number of clusters was small, compared to software procedures based on penalized quasi-likelihood. However, only Bayesian estimation with BUGS allowed for accurate estimation of variance components when there were fewer than 10 clusters. For all statistical software procedures, estimation of variance components tended to be poor when there were only five subjects per cluster, regardless of the number of clusters.

Developing Novel Therapeutics Targeting Undifferentiated and Castration-Resistant Prostate Cancer Stem Cells

DTIC Science & Technology

2016-10-01

identify PCSC- specific homing peptides ; and 2) To perform unbiased drug library screening to identify novel PCSC-targeting chemicals. In the past...display library (PDL) screening in PSA-/lo PCa cells to identify PCSC- specific homing peptides ; and 2) To perform unbiased drug library screening to...Goals of the Project (SOW): Aim 1: To perform phage display library (PDL) screening in PSA-/lo PCa cells to identify PCSC- specific homing peptides

What Do We Know about Using Value-Added to Compare Teachers Who Work in Different Schools? What We Know Series: Value-Added Methods and Applications. Knowledge Brief 10

ERIC Educational Resources Information Center

Raudenbush, Stephen

2013-01-01

This brief considers the problem of using value-added scores to compare teachers who work in different schools. The author focuses on whether such comparisons can be regarded as fair, or, in statistical language, "unbiased." An unbiased measure does not systematically favor teachers because of the backgrounds of the students they are…

Long Term Follow up of the Delayed Effects of Acute Radiation Exposure in Primates

DTIC Science & Technology

2017-10-01

66 of 94 We will then use shRNAs and/or CRISPR constructs targeting the gene of interest to knock down its expression in stem cells prior to...DLBCLs Mutational profiling identifies 150 driver genes Gene expression identifies sub- groups including cell of origin Unbiased CRISPR screen...Exome sequencing in 1,001 DLBCL patients comprehensively identifies 150 driver genes d Unbiased CRISPR screen in DLBCL cell lines identifies essential

Four photon parametric amplification. [in unbiased Josephson junction

NASA Technical Reports Server (NTRS)

Parrish, P. T.; Feldman, M. J.; Ohta, H.; Chiao, R. Y.

1974-01-01

An analysis is presented describing four-photon parametric amplification in an unbiased Josephson junction. Central to the theory is the model of the Josephson effect as a nonlinear inductance. Linear, small signal analysis is applied to the two-fluid model of the Josephson junction. The gain, gain-bandwidth product, high frequency limit, and effective noise temperature are calculated for a cavity reflection amplifier. The analysis is extended to multiple (series-connected) junctions and subharmonic pumping.

A Simple Joint Estimation Method of Residual Frequency Offset and Sampling Frequency Offset for DVB Systems

NASA Astrophysics Data System (ADS)

Kwon, Ki-Won; Cho, Yongsoo

This letter presents a simple joint estimation method for residual frequency offset (RFO) and sampling frequency offset (STO) in OFDM-based digital video broadcasting (DVB) systems. The proposed method selects a continual pilot (CP) subset from an unsymmetrically and non-uniformly distributed CP set to obtain an unbiased estimator. Simulation results show that the proposed method using a properly selected CP subset is unbiased and performs robustly.

Test of mutually unbiased bases for six-dimensional photonic quantum systems

PubMed Central

D'Ambrosio, Vincenzo; Cardano, Filippo; Karimi, Ebrahim; Nagali, Eleonora; Santamato, Enrico; Marrucci, Lorenzo; Sciarrino, Fabio

2013-01-01

In quantum information, complementarity of quantum mechanical observables plays a key role. The eigenstates of two complementary observables form a pair of mutually unbiased bases (MUBs). More generally, a set of MUBs consists of bases that are all pairwise unbiased. Except for specific dimensions of the Hilbert space, the maximal sets of MUBs are unknown in general. Even for a dimension as low as six, the identification of a maximal set of MUBs remains an open problem, although there is strong numerical evidence that no more than three simultaneous MUBs do exist. Here, by exploiting a newly developed holographic technique, we implement and test different sets of three MUBs for a single photon six-dimensional quantum state (a “qusix”), encoded exploiting polarization and orbital angular momentum of photons. A close agreement is observed between theory and experiments. Our results can find applications in state tomography, quantitative wave-particle duality, quantum key distribution. PMID:24067548

Test of mutually unbiased bases for six-dimensional photonic quantum systems.

PubMed

D'Ambrosio, Vincenzo; Cardano, Filippo; Karimi, Ebrahim; Nagali, Eleonora; Santamato, Enrico; Marrucci, Lorenzo; Sciarrino, Fabio

2013-09-25

In quantum information, complementarity of quantum mechanical observables plays a key role. The eigenstates of two complementary observables form a pair of mutually unbiased bases (MUBs). More generally, a set of MUBs consists of bases that are all pairwise unbiased. Except for specific dimensions of the Hilbert space, the maximal sets of MUBs are unknown in general. Even for a dimension as low as six, the identification of a maximal set of MUBs remains an open problem, although there is strong numerical evidence that no more than three simultaneous MUBs do exist. Here, by exploiting a newly developed holographic technique, we implement and test different sets of three MUBs for a single photon six-dimensional quantum state (a "qusix"), encoded exploiting polarization and orbital angular momentum of photons. A close agreement is observed between theory and experiments. Our results can find applications in state tomography, quantitative wave-particle duality, quantum key distribution.

Graph-state formalism for mutually unbiased bases

NASA Astrophysics Data System (ADS)

Spengler, Christoph; Kraus, Barbara

2013-11-01

A pair of orthonormal bases is called mutually unbiased if all mutual overlaps between any element of one basis and an arbitrary element of the other basis coincide. In case the dimension, d, of the considered Hilbert space is a power of a prime number, complete sets of d+1 mutually unbiased bases (MUBs) exist. Here we present a method based on the graph-state formalism to construct such sets of MUBs. We show that for n p-level systems, with p being prime, one particular graph suffices to easily construct a set of pn+1 MUBs. In fact, we show that a single n-dimensional vector, which is associated with this graph, can be used to generate a complete set of MUBs and demonstrate that this vector can be easily determined. Finally, we discuss some advantages of our formalism regarding the analysis of entanglement structures in MUBs, as well as experimental realizations.

Mutually unbiased coarse-grained measurements of two or more phase-space variables

NASA Astrophysics Data System (ADS)

Paul, E. C.; Walborn, S. P.; Tasca, D. S.; Rudnicki, Łukasz

2018-05-01

Mutual unbiasedness of the eigenstates of phase-space operators—such as position and momentum, or their standard coarse-grained versions—exists only in the limiting case of infinite squeezing. In Phys. Rev. Lett. 120, 040403 (2018), 10.1103/PhysRevLett.120.040403, it was shown that mutual unbiasedness can be recovered for periodic coarse graining of these two operators. Here we investigate mutual unbiasedness of coarse-grained measurements for more than two phase-space variables. We show that mutual unbiasedness can be recovered between periodic coarse graining of any two nonparallel phase-space operators. We illustrate these results through optics experiments, using the fractional Fourier transform to prepare and measure mutually unbiased phase-space variables. The differences between two and three mutually unbiased measurements is discussed. Our results contribute to bridging the gap between continuous and discrete quantum mechanics, and they could be useful in quantum-information protocols.

Nature of Fluctuations on Directional Discontinuities Inside a Solar Ejection: Wind and IMP 8 Observations

NASA Technical Reports Server (NTRS)

Vasquez, Bernard J.; Farrugia, Charles J.; Markovskii, Sergei A.; Hollweg, Joseph V.; Richardson, Ian G.; Ogilvie, Keith W.; Lepping, Ronald P.; Lin, Robert P.; Larson, Davin; White, Nicholas E. (Technical Monitor)

2001-01-01

A solar ejection passed the Wind spacecraft between December 23 and 26, 1996. On closer examination, we find a sequence of ejecta material, as identified by abnormally low proton temperatures, separated by plasmas with typical solar wind temperatures at 1 AU. Large and abrupt changes in field and plasma properties occurred near the separation boundaries of these regions. At the one boundary we examine here, a series of directional discontinuities was observed. We argue that Alfvenic fluctuations in the immediate vicinity of these discontinuities distort minimum variance normals, introducing uncertainty into the identification of the discontinuities as either rotational or tangential. Carrying out a series of tests on plasma and field data including minimum variance, velocity and magnetic field correlations, and jump conditions, we conclude that the discontinuities are tangential. Furthermore, we find waves superposed on these tangential discontinuities (TDs). The presence of discontinuities allows the existence of both surface waves and ducted body waves. Both probably form in the solar atmosphere where many transverse nonuniformities exist and where theoretically they have been expected. We add to prior speculation that waves on discontinuities may in fact be a common occurrence. In the solar wind, these waves can attain large amplitudes and low frequencies. We argue that such waves can generate dynamical changes at TDs through advection or forced reconnection. The dynamics might so extensively alter the internal structure that the discontinuity would no longer be identified as tangential. Such processes could help explain why the occurrence frequency of TDs observed throughout the solar wind falls off with increasing heliocentric distance. The presence of waves may also alter the nature of the interactions of TDs with the Earth's bow shock in so-called hot flow anomalies.

Cross-Layer Resource Allocation for Wireless Visual Sensor Networks and Mobile Ad Hoc Networks

DTIC Science & Technology

2014-10-01

MMD), minimizes the maximum dis- tortion among all nodes of the network, promoting a rather unbiased treatment of the nodes. We employed the Particle...achieve the ideal tradeoff between the transmitted video quality and energy consumption. Each sensor node has a bit rate that can be used for both...Distortion (MMD), minimizes the maximum distortion among all nodes of the network, promoting a rather unbiased treatment of the nodes. For both criteria

Unbiased estimators for spatial distribution functions of classical fluids

NASA Astrophysics Data System (ADS)

Adib, Artur B.; Jarzynski, Christopher

2005-01-01

We use a statistical-mechanical identity closely related to the familiar virial theorem, to derive unbiased estimators for spatial distribution functions of classical fluids. In particular, we obtain estimators for both the fluid density ρ(r) in the vicinity of a fixed solute and the pair correlation g(r) of a homogeneous classical fluid. We illustrate the utility of our estimators with numerical examples, which reveal advantages over traditional histogram-based methods of computing such distributions.

Brillouin Frequency Shift of Fiber Distributed Sensors Extracted from Noisy Signals by Quadratic Fitting.

PubMed

Zheng, Hanrong; Fang, Zujie; Wang, Zhaoyong; Lu, Bin; Cao, Yulong; Ye, Qing; Qu, Ronghui; Cai, Haiwen

2018-01-31

It is a basic task in Brillouin distributed fiber sensors to extract the peak frequency of the scattering spectrum, since the peak frequency shift gives information on the fiber temperature and strain changes. Because of high-level noise, quadratic fitting is often used in the data processing. Formulas of the dependence of the minimum detectable Brillouin frequency shift (BFS) on the signal-to-noise ratio (SNR) and frequency step have been presented in publications, but in different expressions. A detailed deduction of new formulas of BFS variance and its average is given in this paper, showing especially their dependences on the data range used in fitting, including its length and its center respective to the real spectral peak. The theoretical analyses are experimentally verified. It is shown that the center of the data range has a direct impact on the accuracy of the extracted BFS. We propose and demonstrate an iterative fitting method to mitigate such effects and improve the accuracy of BFS measurement. The different expressions of BFS variances presented in previous papers are explained and discussed.

Paradigms for parasite conservation.

PubMed

Dougherty, Eric R; Carlson, Colin J; Bueno, Veronica M; Burgio, Kevin R; Cizauskas, Carrie A; Clements, Christopher F; Seidel, Dana P; Harris, Nyeema C

2016-08-01

Parasitic species, which depend directly on host species for their survival, represent a major regulatory force in ecosystems and a significant component of Earth's biodiversity. Yet the negative impacts of parasites observed at the host level have motivated a conservation paradigm of eradication, moving us farther from attainment of taxonomically unbiased conservation goals. Despite a growing body of literature highlighting the importance of parasite-inclusive conservation, most parasite species remain understudied, underfunded, and underappreciated. We argue the protection of parasitic biodiversity requires a paradigm shift in the perception and valuation of their role as consumer species, similar to that of apex predators in the mid-20th century. Beyond recognizing parasites as vital trophic regulators, existing tools available to conservation practitioners should explicitly account for the unique threats facing dependent species. We built upon concepts from epidemiology and economics (e.g., host-density threshold and cost-benefit analysis) to devise novel metrics of margin of error and minimum investment for parasite conservation. We define margin of error as the risk of accidental host extinction from misestimating equilibrium population sizes and predicted oscillations, while minimum investment represents the cost associated with conserving the additional hosts required to maintain viable parasite populations. This framework will aid in the identification of readily conserved parasites that present minimal health risks. To establish parasite conservation, we propose an extension of population viability analysis for host-parasite assemblages to assess extinction risk. In the direst cases, ex situ breeding programs for parasites should be evaluated to maximize success without undermining host protection. Though parasitic species pose a considerable conservation challenge, adaptations to conservation tools will help protect parasite biodiversity in the face of an uncertain environmental future. © 2015 Society for Conservation Biology.

Measurement Uncertainty Relations for Discrete Observables: Relative Entropy Formulation

NASA Astrophysics Data System (ADS)

Barchielli, Alberto; Gregoratti, Matteo; Toigo, Alessandro

2018-02-01

We introduce a new information-theoretic formulation of quantum measurement uncertainty relations, based on the notion of relative entropy between measurement probabilities. In the case of a finite-dimensional system and for any approximate joint measurement of two target discrete observables, we define the entropic divergence as the maximal total loss of information occurring in the approximation at hand. For fixed target observables, we study the joint measurements minimizing the entropic divergence, and we prove the general properties of its minimum value. Such a minimum is our uncertainty lower bound: the total information lost by replacing the target observables with their optimal approximations, evaluated at the worst possible state. The bound turns out to be also an entropic incompatibility degree, that is, a good information-theoretic measure of incompatibility: indeed, it vanishes if and only if the target observables are compatible, it is state-independent, and it enjoys all the invariance properties which are desirable for such a measure. In this context, we point out the difference between general approximate joint measurements and sequential approximate joint measurements; to do this, we introduce a separate index for the tradeoff between the error of the first measurement and the disturbance of the second one. By exploiting the symmetry properties of the target observables, exact values, lower bounds and optimal approximations are evaluated in two different concrete examples: (1) a couple of spin-1/2 components (not necessarily orthogonal); (2) two Fourier conjugate mutually unbiased bases in prime power dimension. Finally, the entropic incompatibility degree straightforwardly generalizes to the case of many observables, still maintaining all its relevant properties; we explicitly compute it for three orthogonal spin-1/2 components.

Sleep and nutritional deprivation and performance of house officers.

PubMed

Hawkins, M R; Vichick, D A; Silsby, H D; Kruzich, D J; Butler, R

1985-07-01

A study was conducted by the authors to compare cognitive functioning in acutely and chronically sleep-deprived house officers. A multivariate analysis of variance revealed significant deficits in primary mental tasks involving basic rote memory, language, and numeric skills as well as in tasks requiring high-order cognitive functioning and traditional intellective abilities. These deficits existed only for the acutely sleep-deprived group. The finding of deficits in individuals who reported five hours or less of sleep in a 24-hour period suggests that the minimum standard of four hours that has been considered by some to be adequate for satisfactory performance may be insufficient for more complex cognitive functioning.

A Multipath Mitigation Algorithm for vehicle with Smart Antenna

NASA Astrophysics Data System (ADS)

Ji, Jing; Zhang, Jiantong; Chen, Wei; Su, Deliang

2018-01-01

In this paper, the antenna array adaptive method is used to eliminate the multipath interference in the environment of GPS L1 frequency. Combined with the power inversion (PI) algorithm and the minimum variance no distortion response (MVDR) algorithm, the anti-Simulation and verification of the antenna array, and the program into the FPGA, the actual test on the CBD road, the theoretical analysis of the LCMV criteria and PI and MVDR algorithm principles and characteristics of MVDR algorithm to verify anti-multipath interference performance is better than PI algorithm, The satellite navigation in the field of vehicle engineering practice has some guidance and reference.

What determines the direction of minimum variance of the magnetic field fluctuations in the solar wind?

NASA Technical Reports Server (NTRS)

Grappin, R.; Velli, M.

1995-01-01

The solar wind is not an isotropic medium; two symmetry axis are provided, first the radial direction (because the mean wind is radial) and second the spiral direction of the mean magnetic field, which depends on heliocentric distance. Observations show very different anisotropy directions, depending on the frequency waveband; while the large-scale velocity fluctuations are essentially radial, the smaller scale magnetic field fluctuations are mostly perpendicular to the mean field direction, which is not the expected linear (WkB) result. We attempt to explain how these properties are related, with the help of numerical simulations.

GenoMatrix: A Software Package for Pedigree-Based and Genomic Prediction Analyses on Complex Traits.

PubMed

Nazarian, Alireza; Gezan, Salvador Alejandro

2016-07-01

Genomic and pedigree-based best linear unbiased prediction methodologies (G-BLUP and P-BLUP) have proven themselves efficient for partitioning the phenotypic variance of complex traits into its components, estimating the individuals' genetic merits, and predicting unobserved (or yet-to-be observed) phenotypes in many species and fields of study. The GenoMatrix software, presented here, is a user-friendly package to facilitate the process of using genome-wide marker data and parentage information for G-BLUP and P-BLUP analyses on complex traits. It provides users with a collection of applications which help them on a set of tasks from performing quality control on data to constructing and manipulating the genomic and pedigree-based relationship matrices and obtaining their inverses. Such matrices will be then used in downstream analyses by other statistical packages. The package also enables users to obtain predicted values for unobserved individuals based on the genetic values of observed related individuals. GenoMatrix is available to the research community as a Windows 64bit executable and can be downloaded free of charge at: http://compbio.ufl.edu/software/genomatrix/. © The American Genetic Association. 2016. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

High-Dimensional Multivariate Repeated Measures Analysis with Unequal Covariance Matrices.

PubMed

Harrar, Solomon W; Kong, Xiaoli

2015-03-01

In this paper, test statistics for repeated measures design are introduced when the dimension is large. By large dimension is meant the number of repeated measures and the total sample size grow together but either one could be larger than the other. Asymptotic distribution of the statistics are derived for the equal as well as unequal covariance cases in the balanced as well as unbalanced cases. The asymptotic framework considered requires proportional growth of the sample sizes and the dimension of the repeated measures in the unequal covariance case. In the equal covariance case, one can grow at much faster rate than the other. The derivations of the asymptotic distributions mimic that of Central Limit Theorem with some important peculiarities addressed with sufficient rigor. Consistent and unbiased estimators of the asymptotic variances, which make efficient use of all the observations, are also derived. Simulation study provides favorable evidence for the accuracy of the asymptotic approximation under the null hypothesis. Power simulations have shown that the new methods have comparable power with a popular method known to work well in low-dimensional situation but the new methods have shown enormous advantage when the dimension is large. Data from Electroencephalograph (EEG) experiment is analyzed to illustrate the application of the results.

Gibbs Sampler-Based λ-Dynamics and Rao-Blackwell Estimator for Alchemical Free Energy Calculation.

PubMed

Ding, Xinqiang; Vilseck, Jonah Z; Hayes, Ryan L; Brooks, Charles L

2017-06-13

λ-dynamics is a generalized ensemble method for alchemical free energy calculations. In traditional λ-dynamics, the alchemical switch variable λ is treated as a continuous variable ranging from 0 to 1 and an empirical estimator is utilized to approximate the free energy. In the present article, we describe an alternative formulation of λ-dynamics that utilizes the Gibbs sampler framework, which we call Gibbs sampler-based λ-dynamics (GSLD). GSLD, like traditional λ-dynamics, can be readily extended to calculate free energy differences between multiple ligands in one simulation. We also introduce a new free energy estimator, the Rao-Blackwell estimator (RBE), for use in conjunction with GSLD. Compared with the current empirical estimator, the advantage of RBE is that RBE is an unbiased estimator and its variance is usually smaller than the current empirical estimator. We also show that the multistate Bennett acceptance ratio equation or the unbinned weighted histogram analysis method equation can be derived using the RBE. We illustrate the use and performance of this new free energy computational framework by application to a simple harmonic system as well as relevant calculations of small molecule relative free energies of solvation and binding to a protein receptor. Our findings demonstrate consistent and improved performance compared with conventional alchemical free energy methods.

Analyzing self-controlled case series data when case confirmation rates are estimated from an internal validation sample.

PubMed

Xu, Stanley; Clarke, Christina L; Newcomer, Sophia R; Daley, Matthew F; Glanz, Jason M

2018-05-16

Vaccine safety studies are often electronic health record (EHR)-based observational studies. These studies often face significant methodological challenges, including confounding and misclassification of adverse event. Vaccine safety researchers use self-controlled case series (SCCS) study design to handle confounding effect and employ medical chart review to ascertain cases that are identified using EHR data. However, for common adverse events, limited resources often make it impossible to adjudicate all adverse events observed in electronic data. In this paper, we considered four approaches for analyzing SCCS data with confirmation rates estimated from an internal validation sample: (1) observed cases, (2) confirmed cases only, (3) known confirmation rate, and (4) multiple imputation (MI). We conducted a simulation study to evaluate these four approaches using type I error rates, percent bias, and empirical power. Our simulation results suggest that when misclassification of adverse events is present, approaches such as observed cases, confirmed case only, and known confirmation rate may inflate the type I error, yield biased point estimates, and affect statistical power. The multiple imputation approach considers the uncertainty of estimated confirmation rates from an internal validation sample, yields a proper type I error rate, largely unbiased point estimate, proper variance estimate, and statistical power. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Stochastic loss and gain of symmetric divisions in the C. elegans epidermis perturbs robustness of stem cell number

PubMed Central

Katsanos, Dimitris; Koneru, Sneha L.; Mestek Boukhibar, Lamia; Gritti, Nicola; Ghose, Ritobrata; Appleford, Peter J.; Doitsidou, Maria; Woollard, Alison; van Zon, Jeroen S.; Poole, Richard J.

2017-01-01

Biological systems are subject to inherent stochasticity. Nevertheless, development is remarkably robust, ensuring the consistency of key phenotypic traits such as correct cell numbers in a certain tissue. It is currently unclear which genes modulate phenotypic variability, what their relationship is to core components of developmental gene networks, and what is the developmental basis of variable phenotypes. Here, we start addressing these questions using the robust number of Caenorhabditis elegans epidermal stem cells, known as seam cells, as a readout. We employ genetics, cell lineage tracing, and single molecule imaging to show that mutations in lin-22, a Hes-related basic helix-loop-helix (bHLH) transcription factor, increase seam cell number variability. We show that the increase in phenotypic variability is due to stochastic conversion of normally symmetric cell divisions to asymmetric and vice versa during development, which affect the terminal seam cell number in opposing directions. We demonstrate that LIN-22 acts within the epidermal gene network to antagonise the Wnt signalling pathway. However, lin-22 mutants exhibit cell-to-cell variability in Wnt pathway activation, which correlates with and may drive phenotypic variability. Our study demonstrates the feasibility to study phenotypic trait variance in tractable model organisms using unbiased mutagenesis screens. PMID:29108019

High-Dimensional Multivariate Repeated Measures Analysis with Unequal Covariance Matrices

PubMed Central

Harrar, Solomon W.; Kong, Xiaoli

2015-01-01

In this paper, test statistics for repeated measures design are introduced when the dimension is large. By large dimension is meant the number of repeated measures and the total sample size grow together but either one could be larger than the other. Asymptotic distribution of the statistics are derived for the equal as well as unequal covariance cases in the balanced as well as unbalanced cases. The asymptotic framework considered requires proportional growth of the sample sizes and the dimension of the repeated measures in the unequal covariance case. In the equal covariance case, one can grow at much faster rate than the other. The derivations of the asymptotic distributions mimic that of Central Limit Theorem with some important peculiarities addressed with sufficient rigor. Consistent and unbiased estimators of the asymptotic variances, which make efficient use of all the observations, are also derived. Simulation study provides favorable evidence for the accuracy of the asymptotic approximation under the null hypothesis. Power simulations have shown that the new methods have comparable power with a popular method known to work well in low-dimensional situation but the new methods have shown enormous advantage when the dimension is large. Data from Electroencephalograph (EEG) experiment is analyzed to illustrate the application of the results. PMID:26778861

Machine Learning methods for Quantitative Radiomic Biomarkers.

PubMed

Parmar, Chintan; Grossmann, Patrick; Bussink, Johan; Lambin, Philippe; Aerts, Hugo J W L

2015-08-17

Radiomics extracts and mines large number of medical imaging features quantifying tumor phenotypic characteristics. Highly accurate and reliable machine-learning approaches can drive the success of radiomic applications in clinical care. In this radiomic study, fourteen feature selection methods and twelve classification methods were examined in terms of their performance and stability for predicting overall survival. A total of 440 radiomic features were extracted from pre-treatment computed tomography (CT) images of 464 lung cancer patients. To ensure the unbiased evaluation of different machine-learning methods, publicly available implementations along with reported parameter configurations were used. Furthermore, we used two independent radiomic cohorts for training (n = 310 patients) and validation (n = 154 patients). We identified that Wilcoxon test based feature selection method WLCX (stability = 0.84 ± 0.05, AUC = 0.65 ± 0.02) and a classification method random forest RF (RSD = 3.52%, AUC = 0.66 ± 0.03) had highest prognostic performance with high stability against data perturbation. Our variability analysis indicated that the choice of classification method is the most dominant source of performance variation (34.21% of total variance). Identification of optimal machine-learning methods for radiomic applications is a crucial step towards stable and clinically relevant radiomic biomarkers, providing a non-invasive way of quantifying and monitoring tumor-phenotypic characteristics in clinical practice.

Time-Varying Delay Estimation Applied to the Surface Electromyography Signals Using the Parametric Approach

NASA Astrophysics Data System (ADS)

Luu, Gia Thien; Boualem, Abdelbassit; Duy, Tran Trung; Ravier, Philippe; Butteli, Olivier

Muscle Fiber Conduction Velocity (MFCV) can be calculated from the time delay between the surface electromyographic (sEMG) signals recorded by electrodes aligned with the fiber direction. In order to take into account the non-stationarity during the dynamic contraction (the most daily life situation) of the data, the developed methods have to consider that the MFCV changes over time, which induces time-varying delays and the data is non-stationary (change of Power Spectral Density (PSD)). In this paper, the problem of TVD estimation is considered using a parametric method. First, the polynomial model of TVD has been proposed. Then, the TVD model parameters are estimated by using a maximum likelihood estimation (MLE) strategy solved by a deterministic optimization technique (Newton) and stochastic optimization technique, called simulated annealing (SA). The performance of the two techniques is also compared. We also derive two appropriate Cramer-Rao Lower Bounds (CRLB) for the estimated TVD model parameters and for the TVD waveforms. Monte-Carlo simulation results show that the estimation of both the model parameters and the TVD function is unbiased and that the variance obtained is close to the derived CRBs. A comparison with non-parametric approaches of the TVD estimation is also presented and shows the superiority of the method proposed.

Towards Automated Annotation of Benthic Survey Images: Variability of Human Experts and Operational Modes of Automation

PubMed Central

Beijbom, Oscar; Edmunds, Peter J.; Roelfsema, Chris; Smith, Jennifer; Kline, David I.; Neal, Benjamin P.; Dunlap, Matthew J.; Moriarty, Vincent; Fan, Tung-Yung; Tan, Chih-Jui; Chan, Stephen; Treibitz, Tali; Gamst, Anthony; Mitchell, B. Greg; Kriegman, David

2015-01-01

Global climate change and other anthropogenic stressors have heightened the need to rapidly characterize ecological changes in marine benthic communities across large scales. Digital photography enables rapid collection of survey images to meet this need, but the subsequent image annotation is typically a time consuming, manual task. We investigated the feasibility of using automated point-annotation to expedite cover estimation of the 17 dominant benthic categories from survey-images captured at four Pacific coral reefs. Inter- and intra- annotator variability among six human experts was quantified and compared to semi- and fully- automated annotation methods, which are made available at coralnet.ucsd.edu. Our results indicate high expert agreement for identification of coral genera, but lower agreement for algal functional groups, in particular between turf algae and crustose coralline algae. This indicates the need for unequivocal definitions of algal groups, careful training of multiple annotators, and enhanced imaging technology. Semi-automated annotation, where 50% of the annotation decisions were performed automatically, yielded cover estimate errors comparable to those of the human experts. Furthermore, fully-automated annotation yielded rapid, unbiased cover estimates but with increased variance. These results show that automated annotation can increase spatial coverage and decrease time and financial outlay for image-based reef surveys. PMID:26154157

Modification of the Sandwich Estimator in Generalized Estimating Equations with Correlated Binary Outcomes in Rare Event and Small Sample Settings

PubMed Central

Rogers, Paul; Stoner, Julie

2016-01-01

Regression models for correlated binary outcomes are commonly fit using a Generalized Estimating Equations (GEE) methodology. GEE uses the Liang and Zeger sandwich estimator to produce unbiased standard error estimators for regression coefficients in large sample settings even when the covariance structure is misspecified. The sandwich estimator performs optimally in balanced designs when the number of participants is large, and there are few repeated measurements. The sandwich estimator is not without drawbacks; its asymptotic properties do not hold in small sample settings. In these situations, the sandwich estimator is biased downwards, underestimating the variances. In this project, a modified form for the sandwich estimator is proposed to correct this deficiency. The performance of this new sandwich estimator is compared to the traditional Liang and Zeger estimator as well as alternative forms proposed by Morel, Pan and Mancl and DeRouen. The performance of each estimator was assessed with 95% coverage probabilities for the regression coefficient estimators using simulated data under various combinations of sample sizes and outcome prevalence values with an Independence (IND), Autoregressive (AR) and Compound Symmetry (CS) correlation structure. This research is motivated by investigations involving rare-event outcomes in aviation data. PMID:26998504

A comparison of two indices for the intraclass correlation coefficient.

PubMed

Shieh, Gwowen

2012-12-01

In the present study, we examined the behavior of two indices for measuring the intraclass correlation in the one-way random effects model: the prevailing ICC(1) (Fisher, 1938) and the corrected eta-squared (Bliese & Halverson, 1998). These two procedures differ both in their methods of estimating the variance components that define the intraclass correlation coefficient and in their performance of bias and mean squared error in the estimation of the intraclass correlation coefficient. In contrast with the natural unbiased principle used to construct ICC(1), in the present study it was analytically shown that the corrected eta-squared estimator is identical to the maximum likelihood estimator and the pairwise estimator under equal group sizes. Moreover, the empirical results obtained from the present Monte Carlo simulation study across various group structures revealed the mutual dominance relationship between their truncated versions for negative values. The corrected eta-squared estimator performs better than the ICC(1) estimator when the underlying population intraclass correlation coefficient is small. Conversely, ICC(1) has a clear advantage over the corrected eta-squared for medium and large magnitudes of population intraclass correlation coefficient. The conceptual description and numerical investigation provide guidelines to help researchers choose between the two indices for more accurate reliability analysis in multilevel research.

Estimating and testing interactions when explanatory variables are subject to non-classical measurement error.

PubMed

Murad, Havi; Kipnis, Victor; Freedman, Laurence S

2016-10-01

Assessing interactions in linear regression models when covariates have measurement error (ME) is complex.We previously described regression calibration (RC) methods that yield consistent estimators and standard errors for interaction coefficients of normally distributed covariates having classical ME. Here we extend normal based RC (NBRC) and linear RC (LRC) methods to a non-classical ME model, and describe more efficient versions that combine estimates from the main study and internal sub-study. We apply these methods to data from the Observing Protein and Energy Nutrition (OPEN) study. Using simulations we show that (i) for normally distributed covariates efficient NBRC and LRC were nearly unbiased and performed well with sub-study size ≥200; (ii) efficient NBRC had lower MSE than efficient LRC; (iii) the naïve test for a single interaction had type I error probability close to the nominal significance level, whereas efficient NBRC and LRC were slightly anti-conservative but more powerful; (iv) for markedly non-normal covariates, efficient LRC yielded less biased estimators with smaller variance than efficient NBRC. Our simulations suggest that it is preferable to use: (i) efficient NBRC for estimating and testing interaction effects of normally distributed covariates and (ii) efficient LRC for estimating and testing interactions for markedly non-normal covariates. © The Author(s) 2013.