Hussey, Michael A; Koch, Gary G; Preisser, John S; Saville, Benjamin R
2016-01-01
Time-to-event or dichotomous outcomes in randomized clinical trials often have analyses using the Cox proportional hazards model or conditional logistic regression, respectively, to obtain covariate-adjusted log hazard (or odds) ratios. Nonparametric Randomization-Based Analysis of Covariance (NPANCOVA) can be applied to unadjusted log hazard (or odds) ratios estimated from a model containing treatment as the only explanatory variable. These adjusted estimates are stratified population-averaged treatment effects and only require a valid randomization to the two treatment groups and avoid key modeling assumptions (e.g., proportional hazards in the case of a Cox model) for the adjustment variables. The methodology has application in the regulatory environment where such assumptions cannot be verified a priori. Application of the methodology is illustrated through three examples on real data from two randomized trials.
A Nonparametric Prior for Simultaneous Covariance Estimation.
Gaskins, Jeremy T; Daniels, Michael J
2013-01-01
In the modeling of longitudinal data from several groups, appropriate handling of the dependence structure is of central importance. Standard methods include specifying a single covariance matrix for all groups or independently estimating the covariance matrix for each group without regard to the others, but when these model assumptions are incorrect, these techniques can lead to biased mean effects or loss of efficiency, respectively. Thus, it is desirable to develop methods to simultaneously estimate the covariance matrix for each group that will borrow strength across groups in a way that is ultimately informed by the data. In addition, for several groups with covariance matrices of even medium dimension, it is difficult to manually select a single best parametric model among the huge number of possibilities given by incorporating structural zeros and/or commonality of individual parameters across groups. In this paper we develop a family of nonparametric priors using the matrix stick-breaking process of Dunson et al. (2008) that seeks to accomplish this task by parameterizing the covariance matrices in terms of the parameters of their modified Cholesky decomposition (Pourahmadi, 1999). We establish some theoretic properties of these priors, examine their effectiveness via a simulation study, and illustrate the priors using data from a longitudinal clinical trial.
Janes, Holly; Pepe, Margaret S
2009-06-01
Recent scientific and technological innovations have produced an abundance of potential markers that are being investigated for their use in disease screening and diagnosis. In evaluating these markers, it is often necessary to account for covariates associated with the marker of interest. Covariates may include subject characteristics, expertise of the test operator, test procedures or aspects of specimen handling. In this paper, we propose the covariate-adjusted receiver operating characteristic curve, a measure of covariate-adjusted classification accuracy. Nonparametric and semiparametric estimators are proposed, asymptotic distribution theory is provided and finite sample performance is investigated. For illustration we characterize the age-adjusted discriminatory accuracy of prostate-specific antigen as a biomarker for prostate cancer.
A Nonparametric Analogy of Analysis of Covariance
ERIC Educational Resources Information Center
Burnett, Thomas D.; Barr, Donald R.
1977-01-01
A nonparametric test of the hypothesis of no treatment effect is suggested for a situation where measures of the severity of the condition treated can be obtained and ranked both pre- and post-treatment. The test allows the pre-treatment rank to be used as a concomitant variable. (Author/JKS)
Combining biomarkers for classification with covariate adjustment.
Kim, Soyoung; Huang, Ying
2017-03-09
Combining multiple markers can improve classification accuracy compared with using a single marker. In practice, covariates associated with markers or disease outcome can affect the performance of a biomarker or biomarker combination in the population. The covariate-adjusted receiver operating characteristic (ROC) curve has been proposed as a tool to tease out the covariate effect in the evaluation of a single marker; this curve characterizes the classification accuracy solely because of the marker of interest. However, research on the effect of covariates on the performance of marker combinations and on how to adjust for the covariate effect when combining markers is still lacking. In this article, we examine the effect of covariates on classification performance of linear marker combinations and propose to adjust for covariates in combining markers by maximizing the nonparametric estimate of the area under the covariate-adjusted ROC curve. The proposed method provides a way to estimate the best linear biomarker combination that is robust to risk model assumptions underlying alternative regression-model-based methods. The proposed estimator is shown to be consistent and asymptotically normally distributed. We conduct simulations to evaluate the performance of our estimator in cohort and case/control designs and compare several different weighting strategies during estimation with respect to efficiency. Our estimator is also compared with alternative regression-model-based estimators or estimators that maximize the empirical area under the ROC curve, with respect to bias and efficiency. We apply the proposed method to a biomarker study from an human immunodeficiency virus vaccine trial. Copyright © 2017 John Wiley & Sons, Ltd.
Effect on Prediction when Modeling Covariates in Bayesian Nonparametric Models.
Cruz-Marcelo, Alejandro; Rosner, Gary L; Müller, Peter; Stewart, Clinton F
2013-04-01
In biomedical research, it is often of interest to characterize biologic processes giving rise to observations and to make predictions of future observations. Bayesian nonparametric methods provide a means for carrying out Bayesian inference making as few assumptions about restrictive parametric models as possible. There are several proposals in the literature for extending Bayesian nonparametric models to include dependence on covariates. Limited attention, however, has been directed to the following two aspects. In this article, we examine the effect on fitting and predictive performance of incorporating covariates in a class of Bayesian nonparametric models by one of two primary ways: either in the weights or in the locations of a discrete random probability measure. We show that different strategies for incorporating continuous covariates in Bayesian nonparametric models can result in big differences when used for prediction, even though they lead to otherwise similar posterior inferences. When one needs the predictive density, as in optimal design, and this density is a mixture, it is better to make the weights depend on the covariates. We demonstrate these points via a simulated data example and in an application in which one wants to determine the optimal dose of an anticancer drug used in pediatric oncology.
Covariate-Adjusted Linear Mixed Effects Model with an Application to Longitudinal Data
Nguyen, Danh V.; Şentürk, Damla; Carroll, Raymond J.
2009-01-01
Linear mixed effects (LME) models are useful for longitudinal data/repeated measurements. We propose a new class of covariate-adjusted LME models for longitudinal data that nonparametrically adjusts for a normalizing covariate. The proposed approach involves fitting a parametric LME model to the data after adjusting for the nonparametric effects of a baseline confounding covariate. In particular, the effect of the observable covariate on the response and predictors of the LME model is modeled nonparametrically via smooth unknown functions. In addition to covariate-adjusted estimation of fixed/population parameters and random effects, an estimation procedure for the variance components is also developed. Numerical properties of the proposed estimators are investigated with simulation studies. The consistency and convergence rates of the proposed estimators are also established. An application to a longitudinal data set on calcium absorption, accounting for baseline distortion from body mass index, illustrates the proposed methodology. PMID:19266053
A Review of Nonparametric Alternatives to Analysis of Covariance.
ERIC Educational Resources Information Center
Olejnik, Stephen F.; Algina, James
1985-01-01
Five distribution-free alternatives to parametric analysis of covariance are presented and demonstrated: Quade's distribution-free test, Puri and Sen's solution, McSweeney and Porter's rank transformation, Burnett and Barr's rank difference scores, and Shirley's general linear model solution. The results of simulation studies regarding Type I…
Gao, Feng; Manatunga, Amita K; Chen, Shande
2007-02-20
Often in many biomedical and epidemiologic studies, estimating hazards function is of interest. The Breslow's estimator is commonly used for estimating the integrated baseline hazard, but this estimator requires the functional form of covariate effects to be correctly specified. It is generally difficult to identify the true functional form of covariate effects in the presence of time-dependent covariates. To provide a complementary method to the traditional proportional hazard model, we propose a tree-type method which enables simultaneously estimating both baseline hazards function and the effects of time-dependent covariates. Our interest will be focused on exploring the potential data structures rather than formal hypothesis testing. The proposed method approximates the baseline hazards and covariate effects with step-functions. The jump points in time and in covariate space are searched via an algorithm based on the improvement of the full log-likelihood function. In contrast to most other estimating methods, the proposed method estimates the hazards function rather than integrated hazards. The method is applied to model the risk of withdrawal in a clinical trial that evaluates the anti-depression treatment in preventing the development of clinical depression. Finally, the performance of the method is evaluated by several simulation studies.
Ryu, Duchwan; Li, Erning; Mallick, Bani K
2011-06-01
We consider nonparametric regression analysis in a generalized linear model (GLM) framework for data with covariates that are the subject-specific random effects of longitudinal measurements. The usual assumption that the effects of the longitudinal covariate processes are linear in the GLM may be unrealistic and if this happens it can cast doubt on the inference of observed covariate effects. Allowing the regression functions to be unknown, we propose to apply Bayesian nonparametric methods including cubic smoothing splines or P-splines for the possible nonlinearity and use an additive model in this complex setting. To improve computational efficiency, we propose the use of data-augmentation schemes. The approach allows flexible covariance structures for the random effects and within-subject measurement errors of the longitudinal processes. The posterior model space is explored through a Markov chain Monte Carlo (MCMC) sampler. The proposed methods are illustrated and compared to other approaches, the "naive" approach and the regression calibration, via simulations and by an application that investigates the relationship between obesity in adulthood and childhood growth curves.
Chiou, Jeng-Min; Liang, Kung-Yee; Chiu, Yen-Feng
2005-01-01
Multipoint linkage analysis using sibpair designs remains a common approach to help investigators to narrow chromosomal regions for traits (either qualitative or quantitative) of interest. Despite its popularity, the success of this approach depends heavily on how issues such as genetic heterogeneity, gene-gene, and gene-environment interactions are properly handled. If addressed properly, the likelihood of detecting genetic linkage and of efficiently estimating the location of the trait locus would be enhanced, sometimes drastically. Previously, we have proposed an approach to deal with these issues by modeling the genetic effect of the target trait locus as a function of covariates pertained to the sibpairs. Here the genetic effect is simply the probability that a sibpair shares the same allele at the trait locus from their parents. Such modeling helps to divide the sibpairs into more homogeneous subgroups, which in turn helps to enhance the chance to detect linkage. One limitation of this approach is the need to categorize the covariates so that a small and fixed number of genetic effect parameters are introduced. In this report, we take advantage of the fact that nowadays multiple markers are readily available for genotyping simultaneously. This suggests that one could estimate the dependence of the generic effect on the covariates nonparametrically. We present an iterative procedure to estimate (1) the genetic effect nonparametrically and (2) the location of the trait locus through estimating functions developed by Liang et al. ([2001a] Hum Hered 51:67-76). We apply this new method to the linkage study of schizophrenia to illustrate how the onset ages of each sibpair may help to address the issue of genetic heterogeneity. This analysis sheds new light on the dependence of the trait effect on onset ages from affected sibpairs, an observation not revealed previously. In addition, we have carried out some simulation work, which suggests that this method provides
Adjusting power for a baseline covariate in linear models
Glueck, Deborah H.; Muller, Keith E.
2009-01-01
SUMMARY The analysis of covariance provides a common approach to adjusting for a baseline covariate in medical research. With Gaussian errors, adding random covariates does not change either the theory or the computations of general linear model data analysis. However, adding random covariates does change the theory and computation of power analysis. Many data analysts fail to fully account for this complication in planning a study. We present our results in five parts. (i) A review of published results helps document the importance of the problem and the limitations of available methods. (ii) A taxonomy for general linear multivariate models and hypotheses allows identifying a particular problem. (iii) We describe how random covariates introduce the need to consider quantiles and conditional values of power. (iv) We provide new exact and approximate methods for power analysis of a range of multivariate models with a Gaussian baseline covariate, for both small and large samples. The new results apply to the Hotelling-Lawley test and the four tests in the “univariate” approach to repeated measures (unadjusted, Huynh-Feldt, Geisser-Greenhouse, Box). The techniques allow rapid calculation and an interactive, graphical approach to sample size choice. (v) Calculating power for a clinical trial of a treatment for increasing bone density illustrates the new methods. We particularly recommend using quantile power with a new Satterthwaite-style approximation. PMID:12898543
Role of Experiment Covariance in Cross Section Adjustments
Giuseppe Palmiotti; M. Salvatores
2014-06-01
This paper is dedicated to the memory of R. D. McKnight, which gave a seminal contribution in establishing methodology and rigorous approach in the evaluation of the covariance of reactor physics integral experiments. His original assessment of the ZPPR experiment uncertainties and correlations has made nuclear data adjustments, based on these experiments, much more robust and reliable. In the present paper it has been shown with some numerical examples the actual impact on an adjustment of accounting for or neglecting such correlations.
Inverse probability weighting for covariate adjustment in randomized studies.
Shen, Changyu; Li, Xiaochun; Li, Lingling
2014-02-20
Covariate adjustment in randomized clinical trials has the potential benefit of precision gain. It also has the potential pitfall of reduced objectivity as it opens the possibility of selecting a 'favorable' model that yields strong treatment benefit estimate. Although there is a large volume of statistical literature targeting on the first aspect, realistic solutions to enforce objective inference and improve precision are rare. As a typical randomized trial needs to accommodate many implementation issues beyond statistical considerations, maintaining the objectivity is at least as important as precision gain if not more, particularly from the perspective of the regulatory agencies. In this article, we propose a two-stage estimation procedure based on inverse probability weighting to achieve better precision without compromising objectivity. The procedure is designed in a way such that the covariate adjustment is performed before seeing the outcome, effectively reducing the possibility of selecting a 'favorable' model that yields a strong intervention effect. Both theoretical and numerical properties of the estimation procedure are presented. Application of the proposed method to a real data example is presented.
Sample Size for Confidence Interval of Covariate-Adjusted Mean Difference
ERIC Educational Resources Information Center
Liu, Xiaofeng Steven
2010-01-01
This article provides a way to determine adequate sample size for the confidence interval of covariate-adjusted mean difference in randomized experiments. The standard error of adjusted mean difference depends on covariate variance and balance, which are two unknown quantities at the stage of planning sample size. If covariate observations are…
ERIC Educational Resources Information Center
Sabourin, Stephane; Valois, Pierre; Lussier, Yvan
2005-01-01
The main purpose of the current research was to develop an abbreviated form of the Dyadic Adjustment Scale (DAS) with nonparametric item response theory. The authors conducted 5 studies, with a total participation of 8,256 married or cohabiting individuals. Results showed that the item characteristic curves behaved in a monotonically increasing…
Covariate-adjusted confidence interval for the intraclass correlation coefficient.
Shoukri, Mohamed M; Donner, Allan; El-Dali, Abdelmoneim
2013-09-01
A crucial step in designing a new study is to estimate the required sample size. For a design involving cluster sampling, the appropriate sample size depends on the so-called design effect, which is a function of the average cluster size and the intracluster correlation coefficient (ICC). It is well-known that under the framework of hierarchical and generalized linear models, a reduction in residual error may be achieved by including risk factors as covariates. In this paper we show that the covariate design, indicating whether the covariates are measured at the cluster level or at the within-cluster subject level affects the estimation of the ICC, and hence the design effect. Therefore, the distinction between these two types of covariates should be made at the design stage. In this paper we use the nested-bootstrap method to assess the accuracy of the estimated ICC for continuous and binary response variables under different covariate structures. The codes of two SAS macros are made available by the authors for interested readers to facilitate the construction of confidence intervals for the ICC. Moreover, using Monte Carlo simulations we evaluate the relative efficiency of the estimators and evaluate the accuracy of the coverage probabilities of a 95% confidence interval on the population ICC. The methodology is illustrated using a published data set of blood pressure measurements taken on family members.
ERIC Educational Resources Information Center
Nimon, Kim; Henson, Robin K.
2015-01-01
The authors empirically examined whether the validity of a residualized dependent variable after covariance adjustment is comparable to that of the original variable of interest. When variance of a dependent variable is removed as a result of one or more covariates, the residual variance may not reflect the same meaning. Using the pretest-posttest…
ERIC Educational Resources Information Center
Safarkhani, Maryam; Moerbeek, Mirjam
2013-01-01
In a randomized controlled trial, a decision needs to be made about the total number of subjects for adequate statistical power. One way to increase the power of a trial is by including a predictive covariate in the model. In this article, the effects of various covariate adjustment strategies on increasing the power is studied for discrete-time…
Elze, Markus C; Gregson, John; Baber, Usman; Williamson, Elizabeth; Sartori, Samantha; Mehran, Roxana; Nichols, Melissa; Stone, Gregg W; Pocock, Stuart J
2017-01-24
Propensity scores (PS) are an increasingly popular method to adjust for confounding in observational studies. Propensity score methods have theoretical advantages over conventional covariate adjustment, but their relative performance in real-word scenarios is poorly characterized. We used datasets from 4 large-scale cardiovascular observational studies (PROMETHEUS, ADAPT-DES [the Assessment of Dual AntiPlatelet Therapy with Drug-Eluting Stents], THIN [The Health Improvement Network], and CHARM [Candesartan in Heart Failure-Assessment of Reduction in Mortality and Morbidity]) to compare the performance of conventional covariate adjustment with 4 common PS methods: matching, stratification, inverse probability weighting, and use of PS as a covariate. We found that stratification performed poorly with few outcome events, and inverse probability weighting gave imprecise estimates of treatment effect and undue influence to a small number of observations when substantial confounding was present. Covariate adjustment and matching performed well in all of our examples, although matching tended to give less precise estimates in some cases. PS methods are not necessarily superior to conventional covariate adjustment, and care should be taken to select the most suitable method.
Least-Squares Data Adjustment with Rank-Deficient Data Covariance Matrices
Williams, J.G.
2011-07-01
A derivation of the linear least-squares adjustment formulae is required that avoids the assumption that the covariance matrix of prior parameters can be inverted. Possible proofs are of several kinds, including: (i) extension of standard results for the linear regression formulae, and (ii) minimization by differentiation of a quadratic form of the deviations in parameters and responses. In this paper, the least-squares adjustment equations are derived in both these ways, while explicitly assuming that the covariance matrix of prior parameters is singular. It will be proved that the solutions are unique and that, contrary to statements that have appeared in the literature, the least-squares adjustment problem is not ill-posed. No modification is required to the adjustment formulae that have been used in the past in the case of a singular covariance matrix for the priors. In conclusion: The linear least-squares adjustment formula that has been used in the past is valid in the case of a singular covariance matrix for the covariance matrix of prior parameters. Furthermore, it provides a unique solution. Statements in the literature, to the effect that the problem is ill-posed are wrong. No regularization of the problem is required. This has been proved in the present paper by two methods, while explicitly assuming that the covariance matrix of prior parameters is singular: i) extension of standard results for the linear regression formulae, and (ii) minimization by differentiation of a quadratic form of the deviations in parameters and responses. No modification is needed to the adjustment formulae that have been used in the past. (author)
ERIC Educational Resources Information Center
Steiner, Peter M.; Cook, Thomas D.; Shadish, William R.
2011-01-01
The effect of unreliability of measurement on propensity score (PS) adjusted treatment effects has not been previously studied. The authors report on a study simulating different degrees of unreliability in the multiple covariates that were used to estimate the PS. The simulation uses the same data as two prior studies. Shadish, Clark, and Steiner…
Taking correlations in GPS least squares adjustments into account with a diagonal covariance matrix
NASA Astrophysics Data System (ADS)
Kermarrec, Gaël; Schön, Steffen
2016-09-01
Based on the results of Luati and Proietti (Ann Inst Stat Math 63:673-686, 2011) on an equivalence for a certain class of polynomial regressions between the diagonally weighted least squares (DWLS) and the generalized least squares (GLS) estimator, an alternative way to take correlations into account thanks to a diagonal covariance matrix is presented. The equivalent covariance matrix is much easier to compute than a diagonalization of the covariance matrix via eigenvalue decomposition which also implies a change of the least squares equations. This condensed matrix, for use in the least squares adjustment, can be seen as a diagonal or reduced version of the original matrix, its elements being simply the sums of the rows elements of the weighting matrix. The least squares results obtained with the equivalent diagonal matrices and those given by the fully populated covariance matrix are mathematically strictly equivalent for the mean estimator in terms of estimate and its a priori cofactor matrix. It is shown that this equivalence can be empirically extended to further classes of design matrices such as those used in GPS positioning (single point positioning, precise point positioning or relative positioning with double differences). Applying this new model to simulated time series of correlated observations, a significant reduction of the coordinate differences compared with the solutions computed with the commonly used diagonal elevation-dependent model was reached for the GPS relative positioning with double differences, single point positioning as well as precise point positioning cases. The estimate differences between the equivalent and classical model with fully populated covariance matrix were below the mm for all simulated GPS cases and below the sub-mm for the relative positioning with double differences. These results were confirmed by analyzing real data. Consequently, the equivalent diagonal covariance matrices, compared with the often used elevation
Haem, Elham; Harling, Kajsa; Ayatollahi, Seyyed Mohammad Taghi; Zare, Najaf; Karlsson, Mats O
2017-02-01
One important aim in population pharmacokinetics (PK) and pharmacodynamics is identification and quantification of the relationships between the parameters and covariates. Lasso has been suggested as a technique for simultaneous estimation and covariate selection. In linear regression, it has been shown that Lasso possesses no oracle properties, which means it asymptotically performs as though the true underlying model was given in advance. Adaptive Lasso (ALasso) with appropriate initial weights is claimed to possess oracle properties; however, it can lead to poor predictive performance when there is multicollinearity between covariates. This simulation study implemented a new version of ALasso, called adjusted ALasso (AALasso), to take into account the ratio of the standard error of the maximum likelihood (ML) estimator to the ML coefficient as the initial weight in ALasso to deal with multicollinearity in non-linear mixed-effect models. The performance of AALasso was compared with that of ALasso and Lasso. PK data was simulated in four set-ups from a one-compartment bolus input model. Covariates were created by sampling from a multivariate standard normal distribution with no, low (0.2), moderate (0.5) or high (0.7) correlation. The true covariates influenced only clearance at different magnitudes. AALasso, ALasso and Lasso were compared in terms of mean absolute prediction error and error of the estimated covariate coefficient. The results show that AALasso performed better in small data sets, even in those in which a high correlation existed between covariates. This makes AALasso a promising method for covariate selection in nonlinear mixed-effect models.
Nonparametric Streamflow Disaggregation Model
NASA Astrophysics Data System (ADS)
Lee, T.; Salas, J. D.; Prairie, J. R.
2009-05-01
Stochastic streamflow generation is generally utilized for planning and management of water resources systems. For this purpose a number of parametric and nonparametric modeling alternatives have been suggested in literature. Among them temporal and spatial disaggregation approaches play an important role particularly to make sure that historical variance-covariance properties are preserved at various temporal and spatial scales. In this paper, we review the underlying features of nonparametric disaggregation, identify some of their pros and cons, and propose a disaggregation algorithm that is capable of surmounting some of the shortcoming of the current models. The proposed models hinge on k-nearest neighbor resampling, the accurate adjusting procedure, and a genetic algorithm. The model has been tested and compared to an existing nonparametric disaggregation approach using data of the Colorado River system. It has been shown that the model is capable of (i) reproducing the season-to-season correlations including the correlation between the last season of the previous year and the first season of the current year, (ii) minimizing or avoiding the generation of flow patterns across the year that are literally the same as those of the historical records, and (iii) minimizing or avoiding the generation of negative flows. In addition, it is applicable to intermittent river regimes. Suggestions for further improving the model are discussed.
Asymptotically Normal and Efficient Estimation of Covariate-Adjusted Gaussian Graphical Model
Chen, Mengjie; Ren, Zhao; Zhao, Hongyu; Zhou, Harrison
2015-01-01
A tuning-free procedure is proposed to estimate the covariate-adjusted Gaussian graphical model. For each finite subgraph, this estimator is asymptotically normal and efficient. As a consequence, a confidence interval can be obtained for each edge. The procedure enjoys easy implementation and efficient computation through parallel estimation on subgraphs or edges. We further apply the asymptotic normality result to perform support recovery through edge-wise adaptive thresholding. This support recovery procedure is called ANTAC, standing for Asymptotically Normal estimation with Thresholding after Adjusting Covariates. ANTAC outperforms other methodologies in the literature in a range of simulation studies. We apply ANTAC to identify gene-gene interactions using an eQTL dataset. Our result achieves better interpretability and accuracy in comparison with CAMPE. PMID:27499564
Spatial and temporal patterns of enzootic raccoon rabies adjusted for multiple covariates
Recuenco, Sergio; Eidson, Millicent; Kulldorff, Martin; Johnson, Glen; Cherry, Bryan
2007-01-01
Background With the objective of identifying spatial and temporal patterns of enzootic raccoon variant rabies, a spatial scan statistic was utilized to search for significant terrestrial rabies clusters by year in New York State in 1997–2003. Cluster analyses were unadjusted for other factors, adjusted for covariates, and adjusted for covariates and large scale geographic variation (LSGV). Adjustments were intended to identify the unusual aggregations of cases given the expected distribution based on the observed locations. Results Statistically significant clusters were identified particularly in the Albany, Finger Lakes, and South Hudson areas. The clusters were generally persistent in the Albany area, but demonstrated cyclical changes in rabies activity every few years in the other areas. Cluster adjustments allowed the discussion of possible causes for the high risk raccoon rabies areas identified. Conclusion This study analyzed raccoon variant rabies spatial and temporal patterns in New York that have not been previously described at a focal (census tract) level. Comparisons across the type of spatial analysis performed with various degrees of adjustment allow consideration of the potential influence of geographical factors for raccoon rabies and possible reasons for the highest risk areas (statistically significant clusters). PMID:17428324
Shi, Ran
2016-01-01
Human brains perform tasks via complex functional networks consisting of separated brain regions. A popular approach to characterize brain functional networks in fMRI studies is independent component analysis (ICA), which is a powerful method to reconstruct latent source signals from their linear mixtures. In many fMRI studies, an important goal is to investigate how brain functional networks change according to specific clinical and demographic variabilities. Existing ICA methods, however, cannot directly incorporate covariate effects in ICA decomposition. Heuristic post-ICA analysis to address this need can be inaccurate and inefficient. In this paper, we propose a hierarchical covariate-adjusted ICA (hc-ICA) model that provides a formal statistical framework for estimating covariate effects and testing differences between brain functional networks. Our method provides a more reliable and powerful statistical tool for evaluating group differences in brain functional networks while appropriately controlling for potential confounding factors. We present an analytically tractable EM algorithm to obtain maximum likelihood estimates of our model. We also develop a subspace-based approximate EM that runs significantly faster while retaining high accuracy. To test the differences in functional networks, we introduce a voxel-wise approximate inference procedure which eliminates the need of computationally expensive covariance matrix estimation and inversion. We demonstrate the advantages of our methods over the existing method via simulation studies. We apply our method to an fMRI study to investigate differences in brain functional networks associated with post-traumatic stress disorder (PTSD).
HE, PENG; ERIKSSON, FRANK; SCHEIKE, THOMAS H.; ZHANG, MEI-JIE
2015-01-01
With competing risks data, one often needs to assess the treatment and covariate effects on the cumulative incidence function. Fine and Gray proposed a proportional hazards regression model for the subdistribution of a competing risk with the assumption that the censoring distribution and the covariates are independent. Covariate-dependent censoring sometimes occurs in medical studies. In this paper, we study the proportional hazards regression model for the subdistribution of a competing risk with proper adjustments for covariate-dependent censoring. We consider a covariate-adjusted weight function by fitting the Cox model for the censoring distribution and using the predictive probability for each individual. Our simulation study shows that the covariate-adjusted weight estimator is basically unbiased when the censoring time depends on the covariates, and the covariate-adjusted weight approach works well for the variance estimator as well. We illustrate our methods with bone marrow transplant data from the Center for International Blood and Marrow Transplant Research (CIBMTR). Here cancer relapse and death in complete remission are two competing risks. PMID:27034534
Covariate-Adjusted Precision Matrix Estimation with an Application in Genetical Genomics
Cai, T. Tony; Li, Hongzhe; Liu, Weidong; Xie, Jichun
2017-01-01
Summary Motivated by analysis of genetical genomics data, we introduce a sparse high dimensional multivariate regression model for studying conditional independence relationships among a set of genes adjusting for possible genetic effects. The precision matrix in the model specifies a covariate-adjusted Gaussian graph, which presents the conditional dependence structure of gene expression after the confounding genetic effects on gene expression are taken into account. We present a covariate-adjusted precision matrix estimation method using a constrained ℓ1 minimization, which can be easily implemented by linear programming. Asymptotic convergence rates in various matrix norms and sign consistency are established for the estimators of the regression coefficients and the precision matrix, allowing both the number of genes and the number of the genetic variants to diverge. Simulation shows that the proposed method results in significant improvements in both precision matrix estimation and graphical structure selection when compared to the standard Gaussian graphical model assuming constant means. The proposed method is also applied to analyze a yeast genetical genomics data for the identification of the gene network among a set of genes in the mitogen-activated protein kinase pathway.
ERIC Educational Resources Information Center
Forster, Fred
Statistical methods are described for diagnosing and treating three important problems in covariate tests of significance: curvilinearity, covariable effectiveness, and treatment-covariable interaction. Six major assumptions, prerequisites for covariate procedure, are discussed in detail: (1) normal distribution, (2) homogeneity of variances, (3)…
A note on the empirical likelihood confidence band for hazards ratio with covariate adjustment.
Zhu, Shihong; Yang, Yifan; Zhou, Mai
2015-09-01
In medical studies comparing two treatments in the presence of censored data, the stratified Cox model is an important tool that has the ability to flexibly handle non-proportional hazards while allowing parsimonious covariate adjustment. In order to capture the cumulative treatment effect, the ratio of the treatment specific cumulative baseline hazards is often used as a measure of the treatment effect. Pointwise and simultaneous confidence bands associated with the estimated ratio provide a global picture of how the treatment effect evolves over time. Recently, Dong and Matthews (2012, Biometrics 68, 408-418) proposed to construct a pointwise confidence interval for the ratio using a plug-in type empirical likelihood approach. However, their result on the limiting distribution of the empirical likelihood ratio is generally incorrect and the resulting confidence interval is asymptotically undercovering. In this article, we derive the correct limiting distribution for the likelihood ratio statistic. We also present simulation studies to demonstrate the effectiveness of our approach.
Bartz, Daniel; Hatrick, Kerr; Hesse, Christian W.; Müller, Klaus-Robert; Lemm, Steven
2013-01-01
Robust and reliable covariance estimates play a decisive role in financial and many other applications. An important class of estimators is based on factor models. Here, we show by extensive Monte Carlo simulations that covariance matrices derived from the statistical Factor Analysis model exhibit a systematic error, which is similar to the well-known systematic error of the spectrum of the sample covariance matrix. Moreover, we introduce the Directional Variance Adjustment (DVA) algorithm, which diminishes the systematic error. In a thorough empirical study for the US, European, and Hong Kong stock market we show that our proposed method leads to improved portfolio allocation. PMID:23844016
ERIC Educational Resources Information Center
Petscher, Yaacov; Schatschneider, Christopher
2011-01-01
Research by Huck and McLean (1975) demonstrated that the covariance-adjusted score is more powerful than the simple difference score, yet recent reviews indicate researchers are equally likely to use either score type in two-wave randomized experimental designs. A Monte Carlo simulation was conducted to examine the conditions under which the…
Use and Impact of Covariance Data in the Japanese Latest Adjusted Library ADJ2010 Based on JENDL-4.0
NASA Astrophysics Data System (ADS)
Yokoyama, K.; Ishikawa, M.
2015-01-01
The current status of covariance applications to fast reactor analysis and design in Japan is summarized. In Japan, the covariance data are mainly used for three purposes: (1) to quantify the uncertainty of nuclear core parameters, (2) to identify important nuclides, reactions and energy ranges which are dominant to the uncertainty of core parameters, and (3) to improve the accuracy of core design values by adopting the integral data such as the critical experiments and the power reactor operation data. For the last purpose, the cross section adjustment based on the Bayesian theorem is used. After the release of JENDL-4.0, a development project of the new adjusted group-constant set ADJ2010 was started in 2010 and completed in 2013. In the present paper, the final results of ADJ2010 are briefly summarized. In addition, the adjustment results of ADJ2010 are discussed from the viewpoint of use and impact of nuclear data covariances, focusing on 239Pu capture cross section alterations. For this purpose three kind of indices, called "degree of mobility," "adjustment motive force," and "adjustment potential," are proposed.
Use and Impact of Covariance Data in the Japanese Latest Adjusted Library ADJ2010 Based on JENDL-4.0
Yokoyama, K. Ishikawa, M.
2015-01-15
The current status of covariance applications to fast reactor analysis and design in Japan is summarized. In Japan, the covariance data are mainly used for three purposes: (1) to quantify the uncertainty of nuclear core parameters, (2) to identify important nuclides, reactions and energy ranges which are dominant to the uncertainty of core parameters, and (3) to improve the accuracy of core design values by adopting the integral data such as the critical experiments and the power reactor operation data. For the last purpose, the cross section adjustment based on the Bayesian theorem is used. After the release of JENDL-4.0, a development project of the new adjusted group-constant set ADJ2010 was started in 2010 and completed in 2013. In the present paper, the final results of ADJ2010 are briefly summarized. In addition, the adjustment results of ADJ2010 are discussed from the viewpoint of use and impact of nuclear data covariances, focusing on {sup 239}Pu capture cross section alterations. For this purpose three kind of indices, called “degree of mobility,” “adjustment motive force,” and “adjustment potential,” are proposed.
2010-01-01
Objectives To evaluate the use and reporting of adjusted analysis in randomised controlled trials (RCTs) and compare the quality of reporting before and after the revision of the CONSORT Statement in 2001. Design Comparison of two cross sectional samples of published articles. Data Sources Journal articles indexed on PubMed in December 2000 and December 2006. Study Selection Parallel group RCTs with a full publication carried out in humans and published in English Main outcome measures Proportion of articles reported adjusted analysis; use of adjusted analysis; the reason for adjustment; the method of adjustment and the reporting of adjusted analysis results in the main text and abstract. Results In both cohorts, 25% of studies reported adjusted analysis (84/355 in 2000 vs 113/422 in 2006). Compared with articles reporting only unadjusted analyses, articles that reported adjusted analyses were more likely to specify primary outcomes, involve multiple centers, perform stratified randomization, be published in general medical journals, and recruit larger sample sizes. In both years a minority of articles explained why and how covariates were selected for adjustment (20% to 30%). Almost all articles specified the statistical methods used for adjustment (99% in 2000 vs 100% in 2006) but only 5% and 10%, respectively, reported both adjusted and unadjusted results as recommended in the CONSORT guidelines. Conclusion There was no evidence of change in the reporting of adjusted analysis results five years after the revision of the CONSORT Statement and only a few articles adhered fully to the CONSORT recommendations. PMID:20482769
ERIC Educational Resources Information Center
Furtwengler, Scott R.
2015-01-01
The present study sought to determine the extent to which participation in a post-secondary honors program affected academic achievement. Archival data were collected on three cohorts of high-achieving students at a large public university. Propensity scores were calculated on factors predicting participation in honors and used as the covariate.…
ERIC Educational Resources Information Center
Steiner, Peter M.; Cook, Thomas D.; Shadish, William R.
2009-01-01
This paper investigates how bias reduction was affected when different degrees of measurement error were systematically introduced into the measures constituting the final estimated propensity score (PS), the PS only for the set of effective covariates and the PS only for the ineffective ones. Since there was already some error in the Shadish et…
Huo, Yuankai; Aboud, Katherine; Kang, Hakmook; Cutting, Laurie E; Landman, Bennett A
2016-10-01
Understanding brain volumetry is essential to understand neurodevelopment and disease. Historically, age-related changes have been studied in detail for specific age ranges (e.g., early childhood, teen, young adults, elderly, etc.) or more sparsely sampled for wider considerations of lifetime aging. Recent advancements in data sharing and robust processing have made available considerable quantities of brain images from normal, healthy volunteers. However, existing analysis approaches have had difficulty addressing (1) complex volumetric developments on the large cohort across the life time (e.g., beyond cubic age trends), (2) accounting for confound effects, and (3) maintaining an analysis framework consistent with the general linear model (GLM) approach pervasive in neuroscience. To address these challenges, we propose to use covariate-adjusted restricted cubic spline (C-RCS) regression within a multi-site cross-sectional framework. This model allows for flexible consideration of non-linear age-associated patterns while accounting for traditional covariates and interaction effects. As a demonstration of this approach on lifetime brain aging, we derive normative volumetric trajectories and 95% confidence intervals from 5111 healthy patients from 64 sites while accounting for confounding sex, intracranial volume and field strength effects. The volumetric results are shown to be consistent with traditional studies that have explored more limited age ranges using single-site analyses. This work represents the first integration of C-RCS with neuroimaging and the derivation of structural covariance networks (SCNs) from a large study of multi-site, cross-sectional data.
Chaste, Pauline; Klei, Lambertus; Sanders, Stephan J.; Murtha, Michael T.; Hus, Vanessa; Lowe, Jennifer K.; Willsey, A. Jeremy; Moreno-De-Luca, Daniel; Yu, Timothy W.; Fombonne, Eric; Geschwind, Daniel; Grice, Dorothy E.; Ledbetter, David H.; Lord, Catherine; Mane, Shrikant M.; Martin, Christa Lese; Martin, Donna M.; Morrow, Eric M.; Walsh, Christopher A.; Sutcliffe, James S.; State, Matthew W.; Devlin, Bernie; Cook, Edwin H.; Kim, Soo-Jeong
2013-01-01
BACKGROUND Brain development follows a different trajectory in children with Autism Spectrum Disorders (ASD) than in typically developing children. A proxy for neurodevelopment could be head circumference (HC), but studies assessing HC and its clinical correlates in ASD have been inconsistent. This study investigates HC and clinical correlates in the Simons Simplex Collection cohort. METHODS We used a mixed linear model to estimate effects of covariates and the deviation from the expected HC given parental HC (genetic deviation). After excluding individuals with incomplete data, 7225 individuals in 1891 families remained for analysis. We examined the relationship between HC/genetic deviation of HC and clinical parameters. RESULTS Gender, age, height, weight, genetic ancestry and ASD status were significant predictors of HC (estimate of the ASD effect=0.2cm). HC was approximately normally distributed in probands and unaffected relatives, with only a few outliers. Genetic deviation of HC was also normally distributed, consistent with a random sampling of parental genes. Whereas larger HC than expected was associated with ASD symptom severity and regression, IQ decreased with the absolute value of the genetic deviation of HC. CONCLUSIONS Measured against expected values derived from covariates of ASD subjects, statistical outliers for HC were uncommon. HC is a strongly heritable trait and population norms for HC would be far more accurate if covariates including genetic ancestry, height and age were taken into account. The association of diminishing IQ with absolute deviation from predicted HC values suggests HC could reflect subtle underlying brain development and warrants further investigation. PMID:23746936
Fecteau, Gilles; Arsenault, Julie; Paré, Julie; Van Metre, David C; Holmberg, Charles A; Smith, Bradford P
2013-04-01
The objective of this study was to develop prediction models for the serum IgG concentration in critically ill calves based on indirect assays and to assess if the predictive ability of the models could be improved by inclusion of age, clinical covariates, and/or laboratory covariates. Seventy-eight critically ill calves between 1 and 13 days old were selected from 1 farm. Statistical models to predict IgG concentration from the results of the radial immunodiffusion test, the gold standard, were built as a function of indirect assays of serum and plasma protein concentrations, zinc sulfate (ZnSO4) turbidity and transmittance, and serum γ-glutamyl transferase (GGT) activity. For each assay 4 models were built: without covariates, with age, with age and clinical covariates (infection and dehydration status), and with age and laboratory covariates (fibrinogen concentration and packed cell volume). For the protein models, dehydration status (clinical model) and fibrinogen concentration (laboratory model) were selected for inclusion owing to their statistical significance. These variables increased the coefficient of determination (R (2) ) of the models by ≥ 7% but did not significantly improve the sensitivity or specificity of the models to predict passive transfer with a cutoff IgG concentration of 1000 mg/dL. For the GGT assay, including age as a covariate increased the R (2) of the model by 3%. For the ZnSO4 turbidity test, none of the covariates were statistically significant. Overall, the R (2) of the models ranged from 34% to 62%. This study has provided insight into the importance of adjusting for covariates when using indirect assays to predict IgG concentration in critically ill calves. Results also indicate that ZnSO4 transmittance and turbidity assays could be used advantageously in a field setting.
Nonparametric Combinatorial Sequence Models
NASA Astrophysics Data System (ADS)
Wauthier, Fabian L.; Jordan, Michael I.; Jojic, Nebojsa
This work considers biological sequences that exhibit combinatorial structures in their composition: groups of positions of the aligned sequences are "linked" and covary as one unit across sequences. If multiple such groups exist, complex interactions can emerge between them. Sequences of this kind arise frequently in biology but methodologies for analyzing them are still being developed. This paper presents a nonparametric prior on sequences which allows combinatorial structures to emerge and which induces a posterior distribution over factorized sequence representations. We carry out experiments on three sequence datasets which indicate that combinatorial structures are indeed present and that combinatorial sequence models can more succinctly describe them than simpler mixture models. We conclude with an application to MHC binding prediction which highlights the utility of the posterior distribution induced by the prior. By integrating out the posterior our method compares favorably to leading binding predictors.
An enhanced nonparametric streamflow disaggregation model with genetic algorithm
NASA Astrophysics Data System (ADS)
Lee, T.; Salas, J. D.; Prairie, J.
2010-08-01
Stochastic streamflow generation is generally utilized for planning and management of water resources systems. For this purpose, a number of parametric and nonparametric models have been suggested in literature. Among them, temporal and spatial disaggregation approaches play an important role particularly to make sure that historical variance-covariance properties are preserved at various temporal and spatial scales. In this paper, we review the underlying features of existing nonparametric disaggregation methods, identify some of their pros and cons, and propose a disaggregation algorithm that is capable of surmounting some of the shortcomings of the current models. The proposed models hinge on k-nearest neighbor resampling, the accurate adjusting procedure, and a genetic algorithm. The models have been tested and compared to an existing nonparametric disaggregation approach using data of the Colorado River system. It has been shown that the model is capable of (1) reproducing the season-to-season correlations including the correlation between the last season of the previous year and the first season of the current year, (2) minimizing or avoiding the generation of flow patterns across the year that are literally the same as those of the historical records, and (3) minimizing or avoiding the generation of negative flows. In addition, it is applicable to intermittent river regimes.
Nonparametric identification experiment
NASA Technical Reports Server (NTRS)
Yam, Yeung
1988-01-01
The following constitutes a summary of this paper: on-orbit identification methodology starts with nonparametric techniques for a priori system identification; development of the nonparametric identification and model determination experiment software has been completed; the validation experiments to be performed on the JPL Control and Identification Technology Validation Laboratory have been designed.
Zhong, Sheng; McPeek, Mary Sara
2016-01-01
We consider the problem of genetic association testing of a binary trait in a sample that contains related individuals, where we adjust for relevant covariates and allow for missing data. We propose CERAMIC, an estimating equation approach that can be viewed as a hybrid of logistic regression and linear mixed-effects model (LMM) approaches. CERAMIC extends the recently proposed CARAT method to allow samples with related individuals and to incorporate partially missing data. In simulations, we show that CERAMIC outperforms existing LMM and generalized LMM approaches, maintaining high power and correct type 1 error across a wider range of scenarios. CERAMIC results in a particularly large power increase over existing methods when the sample includes related individuals with some missing data (e.g., when some individuals with phenotype and covariate information have missing genotype), because CERAMIC is able to make use of the relationship information to incorporate partially missing data in the analysis while correcting for dependence. Because CERAMIC is based on a retrospective analysis, it is robust to misspecification of the phenotype model, resulting in better control of type 1 error and higher power than that of prospective methods, such as GMMAT, when the phenotype model is misspecified. CERAMIC is computationally efficient for genomewide analysis in samples of related individuals of almost any configuration, including small families, unrelated individuals and even large, complex pedigrees. We apply CERAMIC to data on type 2 diabetes (T2D) from the Framingham Heart Study. In a genome scan, 9 of the 10 smallest CERAMIC p-values occur in or near either known T2D susceptibility loci or plausible candidates, verifying that CERAMIC is able to home in on the important loci in a genome scan. PMID:27695091
TGDA: Nonparametric Discriminant Analysis
ERIC Educational Resources Information Center
Pohl, Norval F.; Bruno, Albert V.
1976-01-01
A computer program for two-group nonparametric discriminant analysis is presented. Based on Bayes' Theorem for probability revision, the statistical rationale for this program uses the calculation of maximum likelihood estimates of group membership. The program compares the Bayesian procedure to the standard Linear Discriminant Function.…
Bias associated with using the estimated propensity score as a regression covariate.
Hade, Erinn M; Lu, Bo
2014-01-15
The use of propensity score methods to adjust for selection bias in observational studies has become increasingly popular in public health and medical research. A substantial portion of studies using propensity score adjustment treat the propensity score as a conventional regression predictor. Through a Monte Carlo simulation study, Austin and colleagues. investigated the bias associated with treatment effect estimation when the propensity score is used as a covariate in nonlinear regression models, such as logistic regression and Cox proportional hazards models. We show that the bias exists even in a linear regression model when the estimated propensity score is used and derive the explicit form of the bias. We also conduct an extensive simulation study to compare the performance of such covariate adjustment with propensity score stratification, propensity score matching, inverse probability of treatment weighted method, and nonparametric functional estimation using splines. The simulation scenarios are designed to reflect real data analysis practice. Instead of specifying a known parametric propensity score model, we generate the data by considering various degrees of overlap of the covariate distributions between treated and control groups. Propensity score matching excels when the treated group is contained within a larger control pool, while the model-based adjustment may have an edge when treated and control groups do not have too much overlap. Overall, adjusting for the propensity score through stratification or matching followed by regression or using splines, appears to be a good practical strategy.
Nonparametric Conditional Estimation
1987-02-01
have a Brownian limit. Using von Mises’ method, asymptotic normality is obtained for nonparametric conditional estimates of compactly differentiable ... differentiable statistical functionals. This res~arch supported by Office of Naval Research Contract NOOOl4-83-K-0472; supported National Science Foundation...2.5 Models for F. 2.6 Compact Differentiability and von Mises’ Method 3. Consistency . 3.1 Introduction and Definitions 3.2 Prohorov Consistency of
Nonparametric conditional estimation
Owen, A.B.
1987-01-01
Many nonparametric regression techniques (such as kernels, nearest neighbors, and smoothing splines) estimate the conditional mean of Y given X = chi by a weighted sum of observed Y values, where observations with X values near chi tend to have larger weights. In this report the weights are taken to represent a finite signed measure on the space of Y values. This measure is studied as an estimate of the conditional distribution of Y given X = chi. From estimates of the conditional distribution, estimates of conditional means, standard deviations, quantiles and other statistical functionals may be computed. Chapter 1 illustrates the computation of conditional quantiles and conditional survival probabilities on the Stanford Heart Transplant data. Chapter 2 contains a survey of nonparametric regression methods and introduces statistical metrics and von Mises' method for later use. Chapter 3 proves some consistency results. Chapter 4 provides conditions under which the suitably normalized errors in estimating the conditional distribution of Y have a Brownian limit. Using von Mises' method, asymptotic normality is obtained for nonparametric conditional estimates of compactly differentiable statistical functionals.
Modeling Non-Gaussian Time Series with Nonparametric Bayesian Model.
Xu, Zhiguang; MacEachern, Steven; Xu, Xinyi
2015-02-01
We present a class of Bayesian copula models whose major components are the marginal (limiting) distribution of a stationary time series and the internal dynamics of the series. We argue that these are the two features with which an analyst is typically most familiar, and hence that these are natural components with which to work. For the marginal distribution, we use a nonparametric Bayesian prior distribution along with a cdf-inverse cdf transformation to obtain large support. For the internal dynamics, we rely on the traditionally successful techniques of normal-theory time series. Coupling the two components gives us a family of (Gaussian) copula transformed autoregressive models. The models provide coherent adjustments of time scales and are compatible with many extensions, including changes in volatility of the series. We describe basic properties of the models, show their ability to recover non-Gaussian marginal distributions, and use a GARCH modification of the basic model to analyze stock index return series. The models are found to provide better fit and improved short-range and long-range predictions than Gaussian competitors. The models are extensible to a large variety of fields, including continuous time models, spatial models, models for multiple series, models driven by external covariate streams, and non-stationary models.
ERIC Educational Resources Information Center
Zwick, Rebecca
1985-01-01
Describes how the test statistic for nonparametric one-way multivariate analysis of variance can be obtained by submitting the data to a packaged computer program. Monte Carlo evidence indicates that the nonparametric approach is advantageous under certain violations of the assumptions of multinormality and homogeneity of covariance matrices.…
Nonparametric Bayes analysis of social science data
NASA Astrophysics Data System (ADS)
Kunihama, Tsuyoshi
Social science data often contain complex characteristics that standard statistical methods fail to capture. Social surveys assign many questions to respondents, which often consist of mixed-scale variables. Each of the variables can follow a complex distribution outside parametric families and associations among variables may have more complicated structures than standard linear dependence. Therefore, it is not straightforward to develop a statistical model which can approximate structures well in the social science data. In addition, many social surveys have collected data over time and therefore we need to incorporate dynamic dependence into the models. Also, it is standard to observe massive number of missing values in the social science data. To address these challenging problems, this thesis develops flexible nonparametric Bayesian methods for the analysis of social science data. Chapter 1 briefly explains backgrounds and motivations of the projects in the following chapters. Chapter 2 develops a nonparametric Bayesian modeling of temporal dependence in large sparse contingency tables, relying on a probabilistic factorization of the joint pmf. Chapter 3 proposes nonparametric Bayes inference on conditional independence with conditional mutual information used as a measure of the strength of conditional dependence. Chapter 4 proposes a novel Bayesian density estimation method in social surveys with complex designs where there is a gap between sample and population. We correct for the bias by adjusting mixture weights in Bayesian mixture models. Chapter 5 develops a nonparametric model for mixed-scale longitudinal surveys, in which various types of variables can be induced through latent continuous variables and dynamic latent factors lead to flexibly time-varying associations among variables.
Bayesian Nonparametric Models for Multiway Data Analysis.
Xu, Zenglin; Yan, Feng; Qi, Yuan
2015-02-01
Tensor decomposition is a powerful computational tool for multiway data analysis. Many popular tensor decomposition approaches-such as the Tucker decomposition and CANDECOMP/PARAFAC (CP)-amount to multi-linear factorization. They are insufficient to model (i) complex interactions between data entities, (ii) various data types (e.g., missing data and binary data), and (iii) noisy observations and outliers. To address these issues, we propose tensor-variate latent nonparametric Bayesian models for multiway data analysis. We name these models InfTucker. These new models essentially conduct Tucker decomposition in an infinite feature space. Unlike classical tensor decomposition models, our new approaches handle both continuous and binary data in a probabilistic framework. Unlike previous Bayesian models on matrices and tensors, our models are based on latent Gaussian or t processes with nonlinear covariance functions. Moreover, on network data, our models reduce to nonparametric stochastic blockmodels and can be used to discover latent groups and predict missing interactions. To learn the models efficiently from data, we develop a variational inference technique and explore properties of the Kronecker product for computational efficiency. Compared with a classical variational implementation, this technique reduces both time and space complexities by several orders of magnitude. On real multiway and network data, our new models achieved significantly higher prediction accuracy than state-of-art tensor decomposition methods and blockmodels.
Parametrically guided estimation in nonparametric varying coefficient models with quasi-likelihood
Davenport, Clemontina A.; Maity, Arnab; Wu, Yichao
2015-01-01
Varying coefficient models allow us to generalize standard linear regression models to incorporate complex covariate effects by modeling the regression coefficients as functions of another covariate. For nonparametric varying coefficients, we can borrow the idea of parametrically guided estimation to improve asymptotic bias. In this paper, we develop a guided estimation procedure for the nonparametric varying coefficient models. Asymptotic properties are established for the guided estimators and a method of bandwidth selection via bias-variance tradeoff is proposed. We compare the performance of the guided estimator with that of the unguided estimator via both simulation and real data examples. PMID:26146469
Agogo, George O
2017-01-01
Measurement error in exposure variables is a serious impediment in epidemiological studies that relate exposures to health outcomes. In nutritional studies, interest could be in the association between long-term dietary intake and disease occurrence. Long-term intake is usually assessed with food frequency questionnaire (FFQ), which is prone to recall bias. Measurement error in FFQ-reported intakes leads to bias in parameter estimate that quantifies the association. To adjust for bias in the association, a calibration study is required to obtain unbiased intake measurements using a short-term instrument such as 24-hour recall (24HR). The 24HR intakes are used as response in regression calibration to adjust for bias in the association. For foods not consumed daily, 24HR-reported intakes are usually characterized by excess zeroes, right skewness, and heteroscedasticity posing serious challenge in regression calibration modeling. We proposed a zero-augmented calibration model to adjust for measurement error in reported intake, while handling excess zeroes, skewness, and heteroscedasticity simultaneously without transforming 24HR intake values. We compared the proposed calibration method with the standard method and with methods that ignore measurement error by estimating long-term intake with 24HR and FFQ-reported intakes. The comparison was done in real and simulated datasets. With the 24HR, the mean increase in mercury level per ounce fish intake was about 0.4; with the FFQ intake, the increase was about 1.2. With both calibration methods, the mean increase was about 2.0. Similar trend was observed in the simulation study. In conclusion, the proposed calibration method performs at least as good as the standard method.
Marginally specified priors for non-parametric Bayesian estimation.
Kessler, David C; Hoff, Peter D; Dunson, David B
2015-01-01
Prior specification for non-parametric Bayesian inference involves the difficult task of quantifying prior knowledge about a parameter of high, often infinite, dimension. A statistician is unlikely to have informed opinions about all aspects of such a parameter but will have real information about functionals of the parameter, such as the population mean or variance. The paper proposes a new framework for non-parametric Bayes inference in which the prior distribution for a possibly infinite dimensional parameter is decomposed into two parts: an informative prior on a finite set of functionals, and a non-parametric conditional prior for the parameter given the functionals. Such priors can be easily constructed from standard non-parametric prior distributions in common use and inherit the large support of the standard priors on which they are based. Additionally, posterior approximations under these informative priors can generally be made via minor adjustments to existing Markov chain approximation algorithms for standard non-parametric prior distributions. We illustrate the use of such priors in the context of multivariate density estimation using Dirichlet process mixture models, and in the modelling of high dimensional sparse contingency tables.
ERIC Educational Resources Information Center
Alterman, Arthur I.; Cacciola, John S.; Habing, Brian; Lynch, Kevin G.
2007-01-01
Baseline Addiction Severity Index (5th ed.; ASI-5) data of 2,142 substance abuse patients were analyzed with two nonparametric item response theory (NIRT) methods: Mokken scaling and conditional covariance techniques. Nine reliable and dimensionally homogeneous Recent Problem indexes emerged in the ASI-5's seven areas, including two each in the…
Conditional Covariance-based Representation of Multidimensional Test Structure.
ERIC Educational Resources Information Center
Bolt, Daniel M.
2001-01-01
Presents a new nonparametric method for constructing a spatial representation of multidimensional test structure, the Conditional Covariance-based SCALing (CCSCAL) method. Describes an index to measure the accuracy of the representation. Uses simulation and real-life data analyses to show that the method provides a suitable approximation to…
A Simple Class of Bayesian Nonparametric Autoregression Models.
Di Lucca, Maria Anna; Guglielmi, Alessandra; Müller, Peter; Quintana, Fernando A
2013-03-01
We introduce a model for a time series of continuous outcomes, that can be expressed as fully nonparametric regression or density regression on lagged terms. The model is based on a dependent Dirichlet process prior on a family of random probability measures indexed by the lagged covariates. The approach is also extended to sequences of binary responses. We discuss implementation and applications of the models to a sequence of waiting times between eruptions of the Old Faithful Geyser, and to a dataset consisting of sequences of recurrence indicators for tumors in the bladder of several patients.
Daye, Z. John; Xie, Jichun; Li, Hongzhe
2012-01-01
Many problems in genomics are related to variable selection where high-dimensional genomic data are treated as covariates. Such genomic covariates often have certain structures and can be represented as vertices of an undirected graph. Biological processes also vary as functions depending upon some biological state, such as time. High-dimensional variable selection where covariates are graph-structured and underlying model is nonparametric presents an important but largely unaddressed statistical challenge. Motivated by the problem of regression-based motif discovery, we consider the problem of variable selection for high-dimensional nonparametric varying-coefficient models and introduce a sparse structured shrinkage (SSS) estimator based on basis function expansions and a novel smoothed penalty function. We present an efficient algorithm for computing the SSS estimator. Results on model selection consistency and estimation bounds are derived. Moreover, finite-sample performances are studied via simulations, and the effects of high-dimensionality and structural information of the covariates are especially highlighted. We apply our method to motif finding problem using a yeast cell-cycle gene expression dataset and word counts in genes’ promoter sequences. Our results demonstrate that the proposed method can result in better variable selection and prediction for high-dimensional regression when the underlying model is nonparametric and covariates are structured. Supplemental materials for the article are available online. PMID:22904608
Bayesian non-parametrics and the probabilistic approach to modelling
Ghahramani, Zoubin
2013-01-01
Modelling is fundamental to many fields of science and engineering. A model can be thought of as a representation of possible data one could predict from a system. The probabilistic approach to modelling uses probability theory to express all aspects of uncertainty in the model. The probabilistic approach is synonymous with Bayesian modelling, which simply uses the rules of probability theory in order to make predictions, compare alternative models, and learn model parameters and structure from data. This simple and elegant framework is most powerful when coupled with flexible probabilistic models. Flexibility is achieved through the use of Bayesian non-parametrics. This article provides an overview of probabilistic modelling and an accessible survey of some of the main tools in Bayesian non-parametrics. The survey covers the use of Bayesian non-parametrics for modelling unknown functions, density estimation, clustering, time-series modelling, and representing sparsity, hierarchies, and covariance structure. More specifically, it gives brief non-technical overviews of Gaussian processes, Dirichlet processes, infinite hidden Markov models, Indian buffet processes, Kingman’s coalescent, Dirichlet diffusion trees and Wishart processes. PMID:23277609
Astronomical Methods for Nonparametric Regression
NASA Astrophysics Data System (ADS)
Steinhardt, Charles L.; Jermyn, Adam
2017-01-01
I will discuss commonly used techniques for nonparametric regression in astronomy. We find that several of them, particularly running averages and running medians, are generically biased, asymmetric between dependent and independent variables, and perform poorly in recovering the underlying function, even when errors are present only in one variable. We then examine less-commonly used techniques such as Multivariate Adaptive Regressive Splines and Boosted Trees and find them superior in bias, asymmetry, and variance both theoretically and in practice under a wide range of numerical benchmarks. In this context the chief advantage of the common techniques is runtime, which even for large datasets is now measured in microseconds compared with milliseconds for the more statistically robust techniques. This points to a tradeoff between bias, variance, and computational resources which in recent years has shifted heavily in favor of the more advanced methods, primarily driven by Moore's Law. Along these lines, we also propose a new algorithm which has better overall statistical properties than all techniques examined thus far, at the cost of significantly worse runtime, in addition to providing guidance on choosing the nonparametric regression technique most suitable to any specific problem. We then examine the more general problem of errors in both variables and provide a new algorithm which performs well in most cases and lacks the clear asymmetry of existing non-parametric methods, which fail to account for errors in both variables.
Tatarinova, Tatiana; Neely, Michael; Bartroff, Jay; van Guilder, Michael; Yamada, Walter; Bayard, David; Jelliffe, Roger; Leary, Robert; Chubatiuk, Alyona; Schumitzky, Alan
2013-04-01
Population pharmacokinetic (PK) modeling methods can be statistically classified as either parametric or nonparametric (NP). Each classification can be divided into maximum likelihood (ML) or Bayesian (B) approaches. In this paper we discuss the nonparametric case using both maximum likelihood and Bayesian approaches. We present two nonparametric methods for estimating the unknown joint population distribution of model parameter values in a pharmacokinetic/pharmacodynamic (PK/PD) dataset. The first method is the NP Adaptive Grid (NPAG). The second is the NP Bayesian (NPB) algorithm with a stick-breaking process to construct a Dirichlet prior. Our objective is to compare the performance of these two methods using a simulated PK/PD dataset. Our results showed excellent performance of NPAG and NPB in a realistically simulated PK study. This simulation allowed us to have benchmarks in the form of the true population parameters to compare with the estimates produced by the two methods, while incorporating challenges like unbalanced sample times and sample numbers as well as the ability to include the covariate of patient weight. We conclude that both NPML and NPB can be used in realistic PK/PD population analysis problems. The advantages of one versus the other are discussed in the paper. NPAG and NPB are implemented in R and freely available for download within the Pmetrics package from www.lapk.org.
Neely, Michael; Bartroff, Jay; van Guilder, Michael; Yamada, Walter; Bayard, David; Jelliffe, Roger; Leary, Robert; Chubatiuk, Alyona; Schumitzky, Alan
2013-01-01
Population pharmacokinetic (PK) modeling methods can be statistically classified as either parametric or nonparametric (NP). Each classification can be divided into maximum likelihood (ML) or Bayesian (B) approazches. In this paper we discuss the nonparametric case using both maximum likelihood and Bayesian approaches. We present two nonparametric methods for estimating the unknown joint population distribution of model parameter values in a pharmacokinetic/pharmacodynamic (PK/PD) dataset. The first method is the NP Adaptive Grid (NPAG). The second is the NP Bayesian (NPB) algorithm with a stick-breaking process to construct a Dirichlet prior. Our objective is to compare the performance of these two methods using a simulated PK/PD dataset. Our results showed excellent performance of NPAG and NPB in a realistically simulated PK study. This simulation allowed us to have benchmarks in the form of the true population parameters to compare with the estimates produced by the two methods, while incorporating challenges like unbalanced sample times and sample numbers as well as the ability to include the covariate of patient weight. We conclude that both NPML and NPB can be used in realistic PK/PD population analysis problems. The advantages of one versus the other are discussed in the paper. NPAG and NPB are implemented in R and freely available for download within the Pmetrics package from www.lapk.org. PMID:23404393
Variable Selection for Nonparametric Quantile Regression via Smoothing Spline AN OVA
Lin, Chen-Yen; Bondell, Howard; Zhang, Hao Helen; Zou, Hui
2014-01-01
Quantile regression provides a more thorough view of the effect of covariates on a response. Nonparametric quantile regression has become a viable alternative to avoid restrictive parametric assumption. The problem of variable selection for quantile regression is challenging, since important variables can influence various quantiles in different ways. We tackle the problem via regularization in the context of smoothing spline ANOVA models. The proposed sparse nonparametric quantile regression (SNQR) can identify important variables and provide flexible estimates for quantiles. Our numerical study suggests the promising performance of the new procedure in variable selection and function estimation. Supplementary materials for this article are available online. PMID:24554792
Covariate Imbalance and Precision in Measuring Treatment Effects
ERIC Educational Resources Information Center
Liu, Xiaofeng Steven
2011-01-01
Covariate adjustment can increase the precision of estimates by removing unexplained variance from the error in randomized experiments, although chance covariate imbalance tends to counteract the improvement in precision. The author develops an easy measure to examine chance covariate imbalance in randomization by standardizing the average…
Statistical sirens: The allure of nonparametrics
Johnson, Douglas H.
1995-01-01
Although nonparametric statistical methods have a role to play in the analysis of data, often their virtues are overstated and their deficiencies overlooked. A recent Special Feature in Ecology advocated nonparametric methods because of an erroneously stated advantage that they require no assumptions regarding the distribution underlying the observations. The present paper points out some often—ignored feature of nonparametric tests comparing two means, and advocates parameter estimation as a preferred alternative to hypothesis testing in many situations.
Multiatlas segmentation as nonparametric regression.
Awate, Suyash P; Whitaker, Ross T
2014-09-01
This paper proposes a novel theoretical framework to model and analyze the statistical characteristics of a wide range of segmentation methods that incorporate a database of label maps or atlases; such methods are termed as label fusion or multiatlas segmentation. We model these multiatlas segmentation problems as nonparametric regression problems in the high-dimensional space of image patches. We analyze the nonparametric estimator's convergence behavior that characterizes expected segmentation error as a function of the size of the multiatlas database. We show that this error has an analytic form involving several parameters that are fundamental to the specific segmentation problem (determined by the chosen anatomical structure, imaging modality, registration algorithm, and label-fusion algorithm). We describe how to estimate these parameters and show that several human anatomical structures exhibit the trends modeled analytically. We use these parameter estimates to optimize the regression estimator. We show that the expected error for large database sizes is well predicted by models learned on small databases. Thus, a few expert segmentations can help predict the database sizes required to keep the expected error below a specified tolerance level. Such cost-benefit analysis is crucial for deploying clinical multiatlas segmentation systems.
Multiatlas Segmentation as Nonparametric Regression
Awate, Suyash P.; Whitaker, Ross T.
2015-01-01
This paper proposes a novel theoretical framework to model and analyze the statistical characteristics of a wide range of segmentation methods that incorporate a database of label maps or atlases; such methods are termed as label fusion or multiatlas segmentation. We model these multiatlas segmentation problems as nonparametric regression problems in the high-dimensional space of image patches. We analyze the nonparametric estimator’s convergence behavior that characterizes expected segmentation error as a function of the size of the multiatlas database. We show that this error has an analytic form involving several parameters that are fundamental to the specific segmentation problem (determined by the chosen anatomical structure, imaging modality, registration algorithm, and label-fusion algorithm). We describe how to estimate these parameters and show that several human anatomical structures exhibit the trends modeled analytically. We use these parameter estimates to optimize the regression estimator. We show that the expected error for large database sizes is well predicted by models learned on small databases. Thus, a few expert segmentations can help predict the database sizes required to keep the expected error below a specified tolerance level. Such cost-benefit analysis is crucial for deploying clinical multiatlas segmentation systems. PMID:24802528
Choosing covariates in the analysis of clinical trials.
Beach, M L; Meier, P
1989-12-01
Much of the literature on clinical trials emphasizes the importance of adjusting the results for any covariates (baseline variables) for which randomization fails to produce nearly exact balance, but the literature is very nearly devoid of recipes for assessing the consequences of such adjustments. Several years ago, Paul Canner presented an approximate expression for the effect of a covariate adjustment, and he considered its use in the selection of covariates. With the aid of Canner's equation, using both formal analysis and simulation, the impact of covariate adjustment is further explored. Unless tight control over the analysis plans is established in advance, covariate adjustment can lead to seriously misleading inferences. Illustrations from the clinical trials literature are provided.
Bayesian inference for longitudinal data with non-parametric treatment effects.
Müller, Peter; Quintana, Fernando A; Rosner, Gary L; Maitland, Michael L
2014-04-01
We consider inference for longitudinal data based on mixed-effects models with a non-parametric Bayesian prior on the treatment effect. The proposed non-parametric Bayesian prior is a random partition model with a regression on patient-specific covariates. The main feature and motivation for the proposed model is the use of covariates with a mix of different data formats and possibly high-order interactions in the regression. The regression is not explicitly parameterized. It is implied by the random clustering of subjects. The motivating application is a study of the effect of an anticancer drug on a patient's blood pressure. The study involves blood pressure measurements taken periodically over several 24-h periods for 54 patients. The 24-h periods for each patient include a pretreatment period and several occasions after the start of therapy.
ERIC Educational Resources Information Center
Bolt, Daniel; Roussos, Louis; Stout, William
Several nonparametric dimensionality assessment tools have demonstrated the usefulness of item pair conditional covariances as building blocks for investigating multidimensional test structure. Recently, J. Zhang and W. Stout (1999) have related the structural properties of conditional covariances in a generalized compensatory framework to a test…
Evaluation of the Covariance Matrix of Estimated Resonance Parameters
NASA Astrophysics Data System (ADS)
Becker, B.; Capote, R.; Kopecky, S.; Massimi, C.; Schillebeeckx, P.; Sirakov, I.; Volev, K.
2014-04-01
In the resonance region nuclear resonance parameters are mostly obtained by a least square adjustment of a model to experimental data. Derived parameters can be mutually correlated through the adjustment procedure as well as through common experimental or model uncertainties. In this contribution we investigate four different methods to propagate the additional covariance caused by experimental or model uncertainties into the evaluation of the covariance matrix of the estimated parameters: (1) including the additional covariance into the experimental covariance matrix based on calculated or theoretical estimates of the data; (2) including the uncertainty affected parameter in the adjustment procedure; (3) evaluation of the full covariance matrix by Monte Carlo sampling of the common parameter; and (4) retroactively including the additional covariance by using the marginalization procedure of Habert et al.
Non-Parametric Collision Probability for Low-Velocity Encounters
NASA Technical Reports Server (NTRS)
Carpenter, J. Russell
2007-01-01
An implicit, but not necessarily obvious, assumption in all of the current techniques for assessing satellite collision probability is that the relative position uncertainty is perfectly correlated in time. If there is any mis-modeling of the dynamics in the propagation of the relative position error covariance matrix, time-wise de-correlation of the uncertainty will increase the probability of collision over a given time interval. The paper gives some examples that illustrate this point. This paper argues that, for the present, Monte Carlo analysis is the best available tool for handling low-velocity encounters, and suggests some techniques for addressing the issues just described. One proposal is for the use of a non-parametric technique that is widely used in actuarial and medical studies. The other suggestion is that accurate process noise models be used in the Monte Carlo trials to which the non-parametric estimate is applied. A further contribution of this paper is a description of how the time-wise decorrelation of uncertainty increases the probability of collision.
Nonparametric Methods in Molecular Biology
Wittkowski, Knut M.; Song, Tingting
2010-01-01
In 2003, the completion of the Human Genome Project[1] together with advances in computational resources[2] were expected to launch an era where the genetic and genomic contributions to many common diseases would be found. In the years following, however, researchers became increasingly frustrated as most reported ‘findings’ could not be replicated in independent studies[3]. To improve the signal/noise ratio, it was suggested to increase the number of cases to be included to tens of thousands[4], a requirement that would dramatically restrict the scope of personalized medicine. Similarly, there was little success in elucidating the gene–gene interactions involved in complex diseases or even in developing criteria for assessing their phenotypes. As a partial solution to these enigmata, we here introduce a class of statistical methods as the ‘missing link’ between advances in genetics and informatics. As a first step, we provide a unifying view of a plethora of non-parametric tests developed mainly in the 1940s, all of which can be expressed as u-statistics. Then, we will extend this approach to reflect categorical and ordinal relationships between variables, resulting in a flexible and powerful approach to deal with the impact of (1) multi-allelic genetic loci, (2) poly-locus genetic regions, and (3) oligo-genetic and oligo-genomic collaborative interactions on complex phenotypes. PMID:20652502
Elementary Estimates: An Introduction to Nonparametrics.
ERIC Educational Resources Information Center
Noether, Gottfried E.
1985-01-01
The paper presents a unified approach to some of the more popular nonparametric methods in current use, providing the reader with new insights by exhibiting relationships to relevant population parameters. (Author/LMO)
NON-PARAMETRIC ESTIMATION UNDER STRONG DEPENDENCE
Zhao, Zhibiao; Zhang, Yiyun; Li, Runze
2014-01-01
We study non-parametric regression function estimation for models with strong dependence. Compared with short-range dependent models, long-range dependent models often result in slower convergence rates. We propose a simple differencing-sequence based non-parametric estimator that achieves the same convergence rate as if the data were independent. Simulation studies show that the proposed method has good finite sample performance. PMID:25018572
NON-PARAMETRIC ESTIMATION UNDER STRONG DEPENDENCE.
Zhao, Zhibiao; Zhang, Yiyun; Li, Runze
2014-01-01
We study non-parametric regression function estimation for models with strong dependence. Compared with short-range dependent models, long-range dependent models often result in slower convergence rates. We propose a simple differencing-sequence based non-parametric estimator that achieves the same convergence rate as if the data were independent. Simulation studies show that the proposed method has good finite sample performance.
Galilean covariant harmonic oscillator
NASA Technical Reports Server (NTRS)
Horzela, Andrzej; Kapuscik, Edward
1993-01-01
A Galilean covariant approach to classical mechanics of a single particle is described. Within the proposed formalism, all non-covariant force laws defining acting forces which become to be defined covariantly by some differential equations are rejected. Such an approach leads out of the standard classical mechanics and gives an example of non-Newtonian mechanics. It is shown that the exactly solvable linear system of differential equations defining forces contains the Galilean covariant description of harmonic oscillator as its particular case. Additionally, it is demonstrated that in Galilean covariant classical mechanics the validity of the second Newton law of dynamics implies the Hooke law and vice versa. It is shown that the kinetic and total energies transform differently with respect to the Galilean transformations.
ERIC Educational Resources Information Center
Headrick, Todd C.; Vineyard, George
The Type I error and power properties of the parametric F test and three nonparametric competitors were compared in terms of 3 x 4 factorial analysis of covariance layout. The focus of the study was on the test for interaction either in the presence or absence of main effects. A variety of conditional distributions, sample sizes, levels of variate…
A Review of DIMPACK Version 1.0: Conditional Covariance-Based Test Dimensionality Analysis Package
ERIC Educational Resources Information Center
Deng, Nina; Han, Kyung T.; Hambleton, Ronald K.
2013-01-01
DIMPACK Version 1.0 for assessing test dimensionality based on a nonparametric conditional covariance approach is reviewed. This software was originally distributed by Assessment Systems Corporation and now can be freely accessed online. The software consists of Windows-based interfaces of three components: DIMTEST, DETECT, and CCPROX/HAC, which…
Why preferring parametric forecasting to nonparametric methods?
Jabot, Franck
2015-05-07
A recent series of papers by Charles T. Perretti and collaborators have shown that nonparametric forecasting methods can outperform parametric methods in noisy nonlinear systems. Such a situation can arise because of two main reasons: the instability of parametric inference procedures in chaotic systems which can lead to biased parameter estimates, and the discrepancy between the real system dynamics and the modeled one, a problem that Perretti and collaborators call "the true model myth". Should ecologists go on using the demanding parametric machinery when trying to forecast the dynamics of complex ecosystems? Or should they rely on the elegant nonparametric approach that appears so promising? It will be here argued that ecological forecasting based on parametric models presents two key comparative advantages over nonparametric approaches. First, the likelihood of parametric forecasting failure can be diagnosed thanks to simple Bayesian model checking procedures. Second, when parametric forecasting is diagnosed to be reliable, forecasting uncertainty can be estimated on virtual data generated with the fitted to data parametric model. In contrast, nonparametric techniques provide forecasts with unknown reliability. This argumentation is illustrated with the simple theta-logistic model that was previously used by Perretti and collaborators to make their point. It should convince ecologists to stick to standard parametric approaches, until methods have been developed to assess the reliability of nonparametric forecasting.
Covariant mutually unbiased bases
NASA Astrophysics Data System (ADS)
Carmeli, Claudio; Schultz, Jussi; Toigo, Alessandro
2016-06-01
The connection between maximal sets of mutually unbiased bases (MUBs) in a prime-power dimensional Hilbert space and finite phase-space geometries is well known. In this article, we classify MUBs according to their degree of covariance with respect to the natural symmetries of a finite phase-space, which are the group of its affine symplectic transformations. We prove that there exist maximal sets of MUBs that are covariant with respect to the full group only in odd prime-power dimensional spaces, and in this case, their equivalence class is actually unique. Despite this limitation, we show that in dimension 2r covariance can still be achieved by restricting to proper subgroups of the symplectic group, that constitute the finite analogues of the oscillator group. For these subgroups, we explicitly construct the unitary operators yielding the covariance.
Covariant Noncommutative Field Theory
Estrada-Jimenez, S.; Garcia-Compean, H.; Obregon, O.; Ramirez, C.
2008-07-02
The covariant approach to noncommutative field and gauge theories is revisited. In the process the formalism is applied to field theories invariant under diffeomorphisms. Local differentiable forms are defined in this context. The lagrangian and hamiltonian formalism is consistently introduced.
A Bayesian Nonparametric Approach to Test Equating
ERIC Educational Resources Information Center
Karabatsos, George; Walker, Stephen G.
2009-01-01
A Bayesian nonparametric model is introduced for score equating. It is applicable to all major equating designs, and has advantages over previous equating models. Unlike the previous models, the Bayesian model accounts for positive dependence between distributions of scores from two tests. The Bayesian model and the previous equating models are…
A Comparison of Parametric versus Nonparametric Statistics.
ERIC Educational Resources Information Center
Royeen, Charlotte Brasic
In order to examine the possible effects of violation of assumptions using parametric procedures, this study is an exploratory investigation into the use of parametric versus nonparametric procedures using a multiple case study design. The case study investigation guidelines outlined by Yin served as the methodology. The following univariate…
How Are Teachers Teaching? A Nonparametric Approach
ERIC Educational Resources Information Center
De Witte, Kristof; Van Klaveren, Chris
2014-01-01
This paper examines which configuration of teaching activities maximizes student performance. For this purpose a nonparametric efficiency model is formulated that accounts for (1) self-selection of students and teachers in better schools and (2) complementary teaching activities. The analysis distinguishes both individual teaching (i.e., a…
Nonparametric analysis of high wind speed data
NASA Astrophysics Data System (ADS)
Francisco-Fernández, Mario; Quintela-del-Río, Alejandro
2013-01-01
In this paper, nonparametric curve estimation methods are applied to analyze time series of wind speeds, focusing on the extreme events exceeding a chosen threshold. Classical parametric statistical approaches in this context consist in fitting a generalized Pareto distribution (GPD) to the tail of the empirical cumulative distribution, using maximum likelihood or the method of the moments to estimate the parameters of this distribution. Additionally, confidence intervals are usually computed to assess the uncertainty of the estimates. Nonparametric methods to estimate directly some quantities of interest, such as the probability of exceedance, the quantiles or return levels, or the return periods, are proposed. Moreover, bootstrap techniques are used to develop pointwise and simultaneous confidence intervals for these functions. The proposed models are applied to wind speed data in the Gulf Coast of US, comparing the results with those using the GPD approach, by means of a split-sample test. Results show that nonparametric methods are competitive with respect to the standard GPD approximations. The study is completed generating synthetic data sets and comparing the behavior of the parametric and the nonparametric estimates in this framework.
AFCI-2.0 Library of Neutron Cross Section Covariances
Herman, M.; Herman,M.; Oblozinsky,P.; Mattoon,C.; Pigni,M.; Hoblit,S.; Mughabghab,S.F.; Sonzogni,A.; Talou,P.; Chadwick,M.B.; Hale.G.M.; Kahler,A.C.; Kawano,T.; Little,R.C.; Young,P.G.
2011-06-26
Neutron cross section covariance library has been under development by BNL-LANL collaborative effort over the last three years. The primary purpose of the library is to provide covariances for the Advanced Fuel Cycle Initiative (AFCI) data adjustment project, which is focusing on the needs of fast advanced burner reactors. The covariances refer to central values given in the 2006 release of the U.S. neutron evaluated library ENDF/B-VII. The preliminary version (AFCI-2.0beta) has been completed in October 2010 and made available to the users for comments. In the final 2.0 release, covariances for a few materials were updated, in particular new LANL evaluations for {sup 238,240}Pu and {sup 241}Am were adopted. BNL was responsible for covariances for structural materials and fission products, management of the library and coordination of the work, while LANL was in charge of covariances for light nuclei and for actinides.
Incorporating covariates in skewed functional data models.
Li, Meng; Staicu, Ana-Maria; Bondell, Howard D
2015-07-01
We introduce a class of covariate-adjusted skewed functional models (cSFM) designed for functional data exhibiting location-dependent marginal distributions. We propose a semi-parametric copula model for the pointwise marginal distributions, which are allowed to depend on covariates, and the functional dependence, which is assumed covariate invariant. The proposed cSFM framework provides a unifying platform for pointwise quantile estimation and trajectory prediction. We consider a computationally feasible procedure that handles densely as well as sparsely observed functional data. The methods are examined numerically using simulations and is applied to a new tractography study of multiple sclerosis. Furthermore, the methodology is implemented in the R package cSFM, which is publicly available on CRAN.
Nonparametric Bayes Factors Based On Empirical Likelihood Ratios
Vexler, Albert; Deng, Wei; Wilding, Gregory E.
2012-01-01
Bayes methodology provides posterior distribution functions based on parametric likelihoods adjusted for prior distributions. A distribution-free alternative to the parametric likelihood is use of empirical likelihood (EL) techniques, well known in the context of nonparametric testing of statistical hypotheses. Empirical likelihoods have been shown to exhibit many of the properties of conventional parametric likelihoods. In this article, we propose and examine Bayes factors (BF) methods that are derived via the EL ratio approach. Following Kass & Wasserman [10], we consider Bayes factors type decision rules in the context of standard statistical testing techniques. We show that the asymptotic properties of the proposed procedure are similar to the classical BF’s asymptotic operating characteristics. Although we focus on hypothesis testing, the proposed approach also yields confidence interval estimators of unknown parameters. Monte Carlo simulations were conducted to evaluate the theoretical results as well as to demonstrate the power of the proposed test. PMID:23180904
Covariant Bardeen perturbation formalism
NASA Astrophysics Data System (ADS)
Vitenti, S. D. P.; Falciano, F. T.; Pinto-Neto, N.
2014-05-01
In a previous work we obtained a set of necessary conditions for the linear approximation in cosmology. Here we discuss the relations of this approach with the so-called covariant perturbations. It is often argued in the literature that one of the main advantages of the covariant approach to describe cosmological perturbations is that the Bardeen formalism is coordinate dependent. In this paper we will reformulate the Bardeen approach in a completely covariant manner. For that, we introduce the notion of pure and mixed tensors, which yields an adequate language to treat both perturbative approaches in a common framework. We then stress that in the referred covariant approach, one necessarily introduces an additional hypersurface choice to the problem. Using our mixed and pure tensors approach, we are able to construct a one-to-one map relating the usual gauge dependence of the Bardeen formalism with the hypersurface dependence inherent to the covariant approach. Finally, through the use of this map, we define full nonlinear tensors that at first order correspond to the three known gauge invariant variables Φ, Ψ and Ξ, which are simultaneously foliation and gauge invariant. We then stress that the use of the proposed mixed tensors allows one to construct simultaneously gauge and hypersurface invariant variables at any order.
NASA Astrophysics Data System (ADS)
Frasinski, Leszek J.
2016-08-01
Recent technological advances in the generation of intense femtosecond pulses have made covariance mapping an attractive analytical technique. The laser pulses available are so intense that often thousands of ionisation and Coulomb explosion events will occur within each pulse. To understand the physics of these processes the photoelectrons and photoions need to be correlated, and covariance mapping is well suited for operating at the high counting rates of these laser sources. Partial covariance is particularly useful in experiments with x-ray free electron lasers, because it is capable of suppressing pulse fluctuation effects. A variety of covariance mapping methods is described: simple, partial (single- and multi-parameter), sliced, contingent and multi-dimensional. The relationship to coincidence techniques is discussed. Covariance mapping has been used in many areas of science and technology: inner-shell excitation and Auger decay, multiphoton and multielectron ionisation, time-of-flight and angle-resolved spectrometry, infrared spectroscopy, nuclear magnetic resonance imaging, stimulated Raman scattering, directional gamma ray sensing, welding diagnostics and brain connectivity studies (connectomics). This review gives practical advice for implementing the technique and interpreting the results, including its limitations and instrumental constraints. It also summarises recent theoretical studies, highlights unsolved problems and outlines a personal view on the most promising research directions.
A Comparison of Bias Correction Adjustments for the DETECT Procedure
ERIC Educational Resources Information Center
Nandakumar, Ratna; Yu, Feng; Zhang, Yanwei
2011-01-01
DETECT is a nonparametric methodology to identify the dimensional structure underlying test data. The associated DETECT index, "D[subscript max]," denotes the degree of multidimensionality in data. Conditional covariances (CCOV) are the building blocks of this index. In specifying population CCOVs, the latent test composite [theta][subscript TT]…
Covariance Applications with Kiwi
NASA Astrophysics Data System (ADS)
Mattoon, C. M.; Brown, D.; Elliott, J. B.
2012-05-01
The Computational Nuclear Physics group at Lawrence Livermore National Laboratory (LLNL) is developing a new tool, named `Kiwi', that is intended as an interface between the covariance data increasingly available in major nuclear reaction libraries (including ENDF and ENDL) and large-scale Uncertainty Quantification (UQ) studies. Kiwi is designed to integrate smoothly into large UQ studies, using the covariance matrix to generate multiple variations of nuclear data. The code has been tested using critical assemblies as a test case, and is being integrated into LLNL's quality assurance and benchmarking for nuclear data.
Emura, Takeshi; Konno, Yoshihiko; Michimae, Hirofumi
2015-07-01
Doubly truncated data consist of samples whose observed values fall between the right- and left- truncation limits. With such samples, the distribution function of interest is estimated using the nonparametric maximum likelihood estimator (NPMLE) that is obtained through a self-consistency algorithm. Owing to the complicated asymptotic distribution of the NPMLE, the bootstrap method has been suggested for statistical inference. This paper proposes a closed-form estimator for the asymptotic covariance function of the NPMLE, which is computationally attractive alternative to bootstrapping. Furthermore, we develop various statistical inference procedures, such as confidence interval, goodness-of-fit tests, and confidence bands to demonstrate the usefulness of the proposed covariance estimator. Simulations are performed to compare the proposed method with both the bootstrap and jackknife methods. The methods are illustrated using the childhood cancer dataset.
A New Approach for Nuclear Data Covariance and Sensitivity Generation
Leal, L.C.; Larson, N.M.; Derrien, H.; Kawano, T.; Chadwick, M.B.
2005-05-24
Covariance data are required to correctly assess uncertainties in design parameters in nuclear applications. The error estimation of calculated quantities relies on the nuclear data uncertainty information available in the basic nuclear data libraries, such as the U.S. Evaluated Nuclear Data File, ENDF/B. The uncertainty files in the ENDF/B library are obtained from the analysis of experimental data and are stored as variance and covariance data. The computer code SAMMY is used in the analysis of the experimental data in the resolved and unresolved resonance energy regions. The data fitting of cross sections is based on generalized least-squares formalism (Bayes' theory) together with the resonance formalism described by R-matrix theory. Two approaches are used in SAMMY for the generation of resonance-parameter covariance data. In the evaluation process SAMMY generates a set of resonance parameters that fit the data, and, in addition, it also provides the resonance-parameter covariances. For existing resonance-parameter evaluations where no resonance-parameter covariance data are available, the alternative is to use an approach called the 'retroactive' resonance-parameter covariance generation. In the high-energy region the methodology for generating covariance data consists of least-squares fitting and model parameter adjustment. The least-squares fitting method calculates covariances directly from experimental data. The parameter adjustment method employs a nuclear model calculation such as the optical model and the Hauser-Feshbach model, and estimates a covariance for the nuclear model parameters. In this paper we describe the application of the retroactive method and the parameter adjustment method to generate covariance data for the gadolinium isotopes.
The intraclass covariance matrix.
Carey, Gregory
2005-09-01
Introduced by C.R. Rao in 1945, the intraclass covariance matrix has seen little use in behavioral genetic research, despite the fact that it was developed to deal with family data. Here, I reintroduce this matrix, and outline its estimation and basic properties for data sets on pairs of relatives. The intraclass covariance matrix is appropriate whenever the research design or mathematical model treats the ordering of the members of a pair as random. Because the matrix has only one estimate of a population variance and covariance, both the observed matrix and the residual matrix from a fitted model are easy to inspect visually; there is no need to mentally average homologous statistics. Fitting a model to the intraclass matrix also gives the same log likelihood, likelihood-ratio (LR) chi2, and parameter estimates as fitting that model to the raw data. A major advantage of the intraclass matrix is that only two factors influence the LR chi2--the sampling error in estimating population parameters and the discrepancy between the model and the observed statistics. The more frequently used interclass covariance matrix adds a third factor to the chi2--sampling error of homologous statistics. Because of this, the degrees of freedom for fitting models to an intraclass matrix differ from fitting that model to an interclass matrix. Future research is needed to establish differences in power-if any--between the interclass and the intraclass matrix.
Generalized Linear Covariance Analysis
NASA Technical Reports Server (NTRS)
Carpenter, James R.; Markley, F. Landis
2014-01-01
This talk presents a comprehensive approach to filter modeling for generalized covariance analysis of both batch least-squares and sequential estimators. We review and extend in two directions the results of prior work that allowed for partitioning of the state space into solve-for'' and consider'' parameters, accounted for differences between the formal values and the true values of the measurement noise, process noise, and textita priori solve-for and consider covariances, and explicitly partitioned the errors into subspaces containing only the influence of the measurement noise, process noise, and solve-for and consider covariances. In this work, we explicitly add sensitivity analysis to this prior work, and relax an implicit assumption that the batch estimator's epoch time occurs prior to the definitive span. We also apply the method to an integrated orbit and attitude problem, in which gyro and accelerometer errors, though not estimated, influence the orbit determination performance. We illustrate our results using two graphical presentations, which we call the variance sandpile'' and the sensitivity mosaic,'' and we compare the linear covariance results to confidence intervals associated with ensemble statistics from a Monte Carlo analysis.
Sample size determination for the confidence interval of linear contrast in analysis of covariance.
Liu, Xiaofeng Steven
2013-03-11
This article provides a way to determine sample size for the confidence interval of the linear contrast of treatment means in analysis of covariance (ANCOVA) without prior knowledge of the actual covariate means and covariate sum of squares, which are modeled as a t statistic. Using the t statistic, one can calculate the appropriate sample size to achieve the desired probability of obtaining a specified width in the confidence interval of the covariate-adjusted linear contrast.
Using Analysis of Covariance (ANCOVA) with Fallible Covariates
ERIC Educational Resources Information Center
Culpepper, Steven Andrew; Aguinis, Herman
2011-01-01
Analysis of covariance (ANCOVA) is used widely in psychological research implementing nonexperimental designs. However, when covariates are fallible (i.e., measured with error), which is the norm, researchers must choose from among 3 inadequate courses of action: (a) know that the assumption that covariates are perfectly reliable is violated but…
Covariant deformed oscillator algebras
NASA Technical Reports Server (NTRS)
Quesne, Christiane
1995-01-01
The general form and associativity conditions of deformed oscillator algebras are reviewed. It is shown how the latter can be fulfilled in terms of a solution of the Yang-Baxter equation when this solution has three distinct eigenvalues and satisfies a Birman-Wenzl-Murakami condition. As an example, an SU(sub q)(n) x SU(sub q)(m)-covariant q-bosonic algebra is discussed in some detail.
The Bayesian Covariance Lasso.
Khondker, Zakaria S; Zhu, Hongtu; Chu, Haitao; Lin, Weili; Ibrahim, Joseph G
2013-04-01
Estimation of sparse covariance matrices and their inverse subject to positive definiteness constraints has drawn a lot of attention in recent years. The abundance of high-dimensional data, where the sample size (n) is less than the dimension (d), requires shrinkage estimation methods since the maximum likelihood estimator is not positive definite in this case. Furthermore, when n is larger than d but not sufficiently larger, shrinkage estimation is more stable than maximum likelihood as it reduces the condition number of the precision matrix. Frequentist methods have utilized penalized likelihood methods, whereas Bayesian approaches rely on matrix decompositions or Wishart priors for shrinkage. In this paper we propose a new method, called the Bayesian Covariance Lasso (BCLASSO), for the shrinkage estimation of a precision (covariance) matrix. We consider a class of priors for the precision matrix that leads to the popular frequentist penalties as special cases, develop a Bayes estimator for the precision matrix, and propose an efficient sampling scheme that does not precalculate boundaries for positive definiteness. The proposed method is permutation invariant and performs shrinkage and estimation simultaneously for non-full rank data. Simulations show that the proposed BCLASSO performs similarly as frequentist methods for non-full rank data.
Khondker, Zakaria S; Zhu, Hongtu; Chu, Haitao; Lin, Weili; Ibrahim, Joseph G.
2012-01-01
Estimation of sparse covariance matrices and their inverse subject to positive definiteness constraints has drawn a lot of attention in recent years. The abundance of high-dimensional data, where the sample size (n) is less than the dimension (d), requires shrinkage estimation methods since the maximum likelihood estimator is not positive definite in this case. Furthermore, when n is larger than d but not sufficiently larger, shrinkage estimation is more stable than maximum likelihood as it reduces the condition number of the precision matrix. Frequentist methods have utilized penalized likelihood methods, whereas Bayesian approaches rely on matrix decompositions or Wishart priors for shrinkage. In this paper we propose a new method, called the Bayesian Covariance Lasso (BCLASSO), for the shrinkage estimation of a precision (covariance) matrix. We consider a class of priors for the precision matrix that leads to the popular frequentist penalties as special cases, develop a Bayes estimator for the precision matrix, and propose an efficient sampling scheme that does not precalculate boundaries for positive definiteness. The proposed method is permutation invariant and performs shrinkage and estimation simultaneously for non-full rank data. Simulations show that the proposed BCLASSO performs similarly as frequentist methods for non-full rank data. PMID:24551316
Estimating the extreme low-temperature event using nonparametric methods
NASA Astrophysics Data System (ADS)
D'Silva, Anisha
This thesis presents a new method of estimating the one-in-N low temperature threshold using a non-parametric statistical method called kernel density estimation applied to daily average wind-adjusted temperatures. We apply our One-in-N Algorithm to local gas distribution companies (LDCs), as they have to forecast the daily natural gas needs of their consumers. In winter, demand for natural gas is high. Extreme low temperature events are not directly related to an LDCs gas demand forecasting, but knowledge of extreme low temperatures is important to ensure that an LDC has enough capacity to meet customer demands when extreme low temperatures are experienced. We present a detailed explanation of our One-in-N Algorithm and compare it to the methods using the generalized extreme value distribution, the normal distribution, and the variance-weighted composite distribution. We show that our One-in-N Algorithm estimates the one-in- N low temperature threshold more accurately than the methods using the generalized extreme value distribution, the normal distribution, and the variance-weighted composite distribution according to root mean square error (RMSE) measure at a 5% level of significance. The One-in- N Algorithm is tested by counting the number of times the daily average wind-adjusted temperature is less than or equal to the one-in- N low temperature threshold.
Cadarso-Suárez, Carmen; Roca-Pardiñas, Javier; Figueiras, Adolfo; González-Manteiga, Wenceslao
2005-04-30
The generalized additive, model (GAM) is a powerful and widely used tool that allows researchers to fit, non-parametrically, the effect of continuous predictors on a transformation of the mean response variable. Such a transformation is given by a so-called link function, and in GAMs this link function is assumed to be known. Nevertheless, if an incorrect choice is made for the link, the resulting GAM is misspecified and the results obtained may be misleading. In this paper, we propose a modified version of the local scoring algorithm that allows for the non-parametric estimation of the link function, by using local linear kernel smoothers. To better understand the effect that each covariate produces on the outcome, results are expressed in terms of the non-parametric odds ratio (OR) curves. Bootstrap techniques were used to correct the bias in the OR estimation and to construct point-wise confidence intervals. A simulation study was carried out to assess the behaviour of the resulting estimates. The proposed methodology was illustrated using data from the AIDS Register of Galicia (NW Spain), with a view to assessing the effect of the CD4 lymphocyte count on the probability of being AIDS-diagnosed via Tuberculosis (TB). This application shows how the link's flexibility makes it possible to obtain OR curve estimates that are less sensitive to the presence of outliers and unusual values that are often present in the extremes of the covariate distributions.
Lottery spending: a non-parametric analysis.
Garibaldi, Skip; Frisoli, Kayla; Ke, Li; Lim, Melody
2015-01-01
We analyze the spending of individuals in the United States on lottery tickets in an average month, as reported in surveys. We view these surveys as sampling from an unknown distribution, and we use non-parametric methods to compare properties of this distribution for various demographic groups, as well as claims that some properties of this distribution are constant across surveys. We find that the observed higher spending by Hispanic lottery players can be attributed to differences in education levels, and we dispute previous claims that the top 10% of lottery players consistently account for 50% of lottery sales.
A nonparametric and diversified portfolio model
NASA Astrophysics Data System (ADS)
Shirazi, Yasaman Izadparast; Sabiruzzaman, Md.; Hamzah, Nor Aishah
2014-07-01
Traditional portfolio models, like mean-variance (MV) suffer from estimation error and lack of diversity. Alternatives, like mean-entropy (ME) or mean-variance-entropy (MVE) portfolio models focus independently on the issue of either a proper risk measure or the diversity. In this paper, we propose an asset allocation model that compromise between risk of historical data and future uncertainty. In the new model, entropy is presented as a nonparametric risk measure as well as an index of diversity. Our empirical evaluation with a variety of performance measures shows that this model has better out-of-sample performances and lower portfolio turnover than its competitors.
Lottery Spending: A Non-Parametric Analysis
Garibaldi, Skip; Frisoli, Kayla; Ke, Li; Lim, Melody
2015-01-01
We analyze the spending of individuals in the United States on lottery tickets in an average month, as reported in surveys. We view these surveys as sampling from an unknown distribution, and we use non-parametric methods to compare properties of this distribution for various demographic groups, as well as claims that some properties of this distribution are constant across surveys. We find that the observed higher spending by Hispanic lottery players can be attributed to differences in education levels, and we dispute previous claims that the top 10% of lottery players consistently account for 50% of lottery sales. PMID:25642699
Asymptotic Theory for Nonparametric Confidence Intervals.
1982-07-01
distributions. Ann. Math Statist. 14, 56-62. 24. ROY, S.N. and POTTHOFF, R.F. (1958). Confidence bounds on vector analogues of the "ratio of the mean" and...fl c,~_________ 14L TITLE feed &MV) S. TYPE or REPORT a PeftOo COVx:REC Asympeocic Theory for Nonaparuetric Technical Report Confidence Intevals 6...S..C-0S78 UNCLASSIFIED TŗU *uuuuumuuumhhhhmhhhm_4 ASYMPTOTIC THEORY FOR NONPARAMETRIC CONFIDENCE INTERVALS by Peter W. Glynn TECHNICAL REPORT NO. 63
Earth Observing System Covariance Realism
NASA Technical Reports Server (NTRS)
Zaidi, Waqar H.; Hejduk, Matthew D.
2016-01-01
The purpose of covariance realism is to properly size a primary object's covariance in order to add validity to the calculation of the probability of collision. The covariance realism technique in this paper consists of three parts: collection/calculation of definitive state estimates through orbit determination, calculation of covariance realism test statistics at each covariance propagation point, and proper assessment of those test statistics. An empirical cumulative distribution function (ECDF) Goodness-of-Fit (GOF) method is employed to determine if a covariance is properly sized by comparing the empirical distribution of Mahalanobis distance calculations to the hypothesized parent 3-DoF chi-squared distribution. To realistically size a covariance for collision probability calculations, this study uses a state noise compensation algorithm that adds process noise to the definitive epoch covariance to account for uncertainty in the force model. Process noise is added until the GOF tests pass a group significance level threshold. The results of this study indicate that when outliers attributed to persistently high or extreme levels of solar activity are removed, the aforementioned covariance realism compensation method produces a tuned covariance with up to 80 to 90% of the covariance propagation timespan passing (against a 60% minimum passing threshold) the GOF tests-a quite satisfactory and useful result.
Covariant magnetic connection hypersurfaces
NASA Astrophysics Data System (ADS)
Pegoraro, F.
2016-04-01
> In the single fluid, non-relativistic, ideal magnetohydrodynamic (MHD) plasma description, magnetic field lines play a fundamental role by defining dynamically preserved `magnetic connections' between plasma elements. Here we show how the concept of magnetic connection needs to be generalized in the case of a relativistic MHD description where we require covariance under arbitrary Lorentz transformations. This is performed by defining 2-D magnetic connection hypersurfaces in the 4-D Minkowski space. This generalization accounts for the loss of simultaneity between spatially separated events in different frames and is expected to provide a powerful insight into the 4-D geometry of electromagnetic fields when .
Decision boundary feature selection for non-parametric classifier
NASA Technical Reports Server (NTRS)
Lee, Chulhee; Landgrebe, David A.
1991-01-01
Feature selection has been one of the most important topics in pattern recognition. Although many authors have studied feature selection for parametric classifiers, few algorithms are available for feature selection for nonparametric classifiers. In this paper we propose a new feature selection algorithm based on decision boundaries for nonparametric classifiers. We first note that feature selection for pattern recognition is equivalent to retaining 'discriminantly informative features', and a discriminantly informative feature is related to the decision boundary. A procedure to extract discriminantly informative features based on a decision boundary for nonparametric classification is proposed. Experiments show that the proposed algorithm finds effective features for the nonparametric classifier with Parzen density estimation.
Low-Fidelity Covariances: Neutron Cross Section Covariance Estimates for 387 Materials
The Low-fidelity Covariance Project (Low-Fi) was funded in FY07-08 by DOEÆs Nuclear Criticality Safety Program (NCSP). The project was a collaboration among ANL, BNL, LANL, and ORNL. The motivation for the Low-Fi project stemmed from an imbalance in supply and demand of covariance data. The interest in, and demand for, covariance data has been in a continual uptrend over the past few years. Requirements to understand application-dependent uncertainties in simulated quantities of interest have led to the development of sensitivity / uncertainty and data adjustment software such as TSUNAMI [1] at Oak Ridge. To take full advantage of the capabilities of TSUNAMI requires general availability of covariance data. However, the supply of covariance data has not been able to keep up with the demand. This fact is highlighted by the observation that the recent release of the much-heralded ENDF/B-VII.0 included covariance data for only 26 of the 393 neutron evaluations (which is, in fact, considerably less covariance data than was included in the final ENDF/B-VI release).[Copied from R.C. Little et al., "Low-Fidelity Covariance Project", Nuclear Data Sheets 109 (2008) 2828-2833] The Low-Fi covariance data are now available at the National Nuclear Data Center. They are separate from ENDF/B-VII.0 and the NNDC warns that this information is not approved by CSEWG. NNDC describes the contents of this collection as: "Covariance data are provided for radiative capture (or (n,ch.p.) for light nuclei), elastic scattering (or total for some actinides), inelastic scattering, (n,2n) reactions, fission and nubars over the energy range from 10(-5{super}) eV to 20 MeV. The library contains 387 files including almost all (383 out of 393) materials of the ENDF/B-VII.0. Absent are data for (7{super})Li, (232{super})Th, (233,235,238{super})U and (239{super})Pu as well as (223,224,225,226{super})Ra, while (nat{super})Zn is replaced by (64,66,67,68,70{super})Zn
Nonparametric Scene Parsing via Label Transfer.
Liu, Ce; Yuen, Jenny; Torralba, Antonio
2011-12-01
While there has been a lot of recent work on object recognition and image understanding, the focus has been on carefully establishing mathematical models for images, scenes, and objects. In this paper, we propose a novel, nonparametric approach for object recognition and scene parsing using a new technology we name label transfer. For an input image, our system first retrieves its nearest neighbors from a large database containing fully annotated images. Then, the system establishes dense correspondences between the input image and each of the nearest neighbors using the dense SIFT flow algorithm [28], which aligns two images based on local image structures. Finally, based on the dense scene correspondences obtained from SIFT flow, our system warps the existing annotations and integrates multiple cues in a Markov random field framework to segment and recognize the query image. Promising experimental results have been achieved by our nonparametric scene parsing system on challenging databases. Compared to existing object recognition approaches that require training classifiers or appearance models for each object category, our system is easy to implement, has few parameters, and embeds contextual information naturally in the retrieval/alignment procedure.
Bayesian Nonparametric Clustering for Positive Definite Matrices.
Cherian, Anoop; Morellas, Vassilios; Papanikolopoulos, Nikolaos
2016-05-01
Symmetric Positive Definite (SPD) matrices emerge as data descriptors in several applications of computer vision such as object tracking, texture recognition, and diffusion tensor imaging. Clustering these data matrices forms an integral part of these applications, for which soft-clustering algorithms (K-Means, expectation maximization, etc.) are generally used. As is well-known, these algorithms need the number of clusters to be specified, which is difficult when the dataset scales. To address this issue, we resort to the classical nonparametric Bayesian framework by modeling the data as a mixture model using the Dirichlet process (DP) prior. Since these matrices do not conform to the Euclidean geometry, rather belongs to a curved Riemannian manifold,existing DP models cannot be directly applied. Thus, in this paper, we propose a novel DP mixture model framework for SPD matrices. Using the log-determinant divergence as the underlying dissimilarity measure to compare these matrices, and further using the connection between this measure and the Wishart distribution, we derive a novel DPM model based on the Wishart-Inverse-Wishart conjugate pair. We apply this model to several applications in computer vision. Our experiments demonstrate that our model is scalable to the dataset size and at the same time achieves superior accuracy compared to several state-of-the-art parametric and nonparametric clustering algorithms.
Nonparametric Bayes Stochastically Ordered Latent Class Models
Yang, Hongxia; O’Brien, Sean; Dunson, David B.
2012-01-01
Latent class models (LCMs) are used increasingly for addressing a broad variety of problems, including sparse modeling of multivariate and longitudinal data, model-based clustering, and flexible inferences on predictor effects. Typical frequentist LCMs require estimation of a single finite number of classes, which does not increase with the sample size, and have a well-known sensitivity to parametric assumptions on the distributions within a class. Bayesian nonparametric methods have been developed to allow an infinite number of classes in the general population, with the number represented in a sample increasing with sample size. In this article, we propose a new nonparametric Bayes model that allows predictors to flexibly impact the allocation to latent classes, while limiting sensitivity to parametric assumptions by allowing class-specific distributions to be unknown subject to a stochastic ordering constraint. An efficient MCMC algorithm is developed for posterior computation. The methods are validated using simulation studies and applied to the problem of ranking medical procedures in terms of the distribution of patient morbidity. PMID:22505787
A Bayesian Nonparametric Meta-Analysis Model
ERIC Educational Resources Information Center
Karabatsos, George; Talbott, Elizabeth; Walker, Stephen G.
2015-01-01
In a meta-analysis, it is important to specify a model that adequately describes the effect-size distribution of the underlying population of studies. The conventional normal fixed-effect and normal random-effects models assume a normal effect-size population distribution, conditionally on parameters and covariates. For estimating the mean overall…
Correcting eddy-covariance flux underestimates over a grassland.
Twine, T. E.; Kustas, W. P.; Norman, J. M.; Cook, D. R.; Houser, P. R.; Meyers, T. P.; Prueger, J. H.; Starks, P. J.; Wesely, M. L.; Environmental Research; Univ. of Wisconsin at Madison; DOE; National Aeronautics and Space Administration; National Oceanic and Atmospheric Administrationoratory
2000-06-08
Independent measurements of the major energy balance flux components are not often consistent with the principle of conservation of energy. This is referred to as a lack of closure of the surface energy balance. Most results in the literature have shown the sum of sensible and latent heat fluxes measured by eddy covariance to be less than the difference between net radiation and soil heat fluxes. This under-measurement of sensible and latent heat fluxes by eddy-covariance instruments has occurred in numerous field experiments and among many different manufacturers of instruments. Four eddy-covariance systems consisting of the same models of instruments were set up side-by-side during the Southern Great Plains 1997 Hydrology Experiment and all systems under-measured fluxes by similar amounts. One of these eddy-covariance systems was collocated with three other types of eddy-covariance systems at different sites; all of these systems under-measured the sensible and latent-heat fluxes. The net radiometers and soil heat flux plates used in conjunction with the eddy-covariance systems were calibrated independently and measurements of net radiation and soil heat flux showed little scatter for various sites. The 10% absolute uncertainty in available energy measurements was considerably smaller than the systematic closure problem in the surface energy budget, which varied from 10 to 30%. When available-energy measurement errors are known and modest, eddy-covariance measurements of sensible and latent heat fluxes should be adjusted for closure. Although the preferred method of energy balance closure is to maintain the Bowen-ratio, the method for obtaining closure appears to be less important than assuring that eddy-covariance measurements are consistent with conservation of energy. Based on numerous measurements over a sorghum canopy, carbon dioxide fluxes, which are measured by eddy covariance, are underestimated by the same factor as eddy covariance evaporation
Deriving covariant holographic entanglement
NASA Astrophysics Data System (ADS)
Dong, Xi; Lewkowycz, Aitor; Rangamani, Mukund
2016-11-01
We provide a gravitational argument in favour of the covariant holographic entanglement entropy proposal. In general time-dependent states, the proposal asserts that the entanglement entropy of a region in the boundary field theory is given by a quarter of the area of a bulk extremal surface in Planck units. The main element of our discussion is an implementation of an appropriate Schwinger-Keldysh contour to obtain the reduced density matrix (and its powers) of a given region, as is relevant for the replica construction. We map this contour into the bulk gravitational theory, and argue that the saddle point solutions of these replica geometries lead to a consistent prescription for computing the field theory Rényi entropies. In the limiting case where the replica index is taken to unity, a local analysis suffices to show that these saddles lead to the extremal surfaces of interest. We also comment on various properties of holographic entanglement that follow from this construction.
Stardust Navigation Covariance Analysis
NASA Technical Reports Server (NTRS)
Menon, Premkumar R.
2000-01-01
The Stardust spacecraft was launched on February 7, 1999 aboard a Boeing Delta-II rocket. Mission participants include the National Aeronautics and Space Administration (NASA), the Jet Propulsion Laboratory (JPL), Lockheed Martin Astronautics (LMA) and the University of Washington. The primary objective of the mission is to collect in-situ samples of the coma of comet Wild-2 and return those samples to the Earth for analysis. Mission design and operational navigation for Stardust is performed by the Jet Propulsion Laboratory (JPL). This paper will describe the extensive JPL effort in support of the Stardust pre-launch analysis of the orbit determination component of the mission covariance study. A description of the mission and it's trajectory will be provided first, followed by a discussion of the covariance procedure and models. Predicted accuracy's will be examined as they relate to navigation delivery requirements for specific critical events during the mission. Stardust was launched into a heliocentric trajectory in early 1999. It will perform an Earth Gravity Assist (EGA) on January 15, 2001 to acquire an orbit for the eventual rendezvous with comet Wild-2. The spacecraft will fly through the coma (atmosphere) on the dayside of Wild-2 on January 2, 2004. At that time samples will be obtained using an aerogel collector. After the comet encounter Stardust will return to Earth when the Sample Return Capsule (SRC) will separate and land at the Utah Test Site (UTTR) on January 15, 2006. The spacecraft will however be deflected off into a heliocentric orbit. The mission is divided into three phases for the covariance analysis. They are 1) Launch to EGA, 2) EGA to Wild-2 encounter and 3) Wild-2 encounter to Earth reentry. Orbit determination assumptions for each phase are provided. These include estimated and consider parameters and their associated a-priori uncertainties. Major perturbations to the trajectory include 19 deterministic and statistical maneuvers
An Empirical Study of Eight Nonparametric Tests in Hierarchical Regression.
ERIC Educational Resources Information Center
Harwell, Michael; Serlin, Ronald C.
When normality does not hold, nonparametric tests represent an important data-analytic alternative to parametric tests. However, the use of nonparametric tests in educational research has been limited by the absence of easily performed tests for complex experimental designs and analyses, such as factorial designs and multiple regression analyses,…
Covariance Manipulation for Conjunction Assessment
NASA Technical Reports Server (NTRS)
Hejduk, M. D.
2016-01-01
Use of probability of collision (Pc) has brought sophistication to CA. Made possible by JSpOC precision catalogue because provides covariance. Has essentially replaced miss distance as basic CA parameter. Embrace of Pc has elevated methods to 'manipulate' covariance to enable/improve CA calculations. Two such methods to be examined here; compensation for absent or unreliable covariances through 'Maximum Pc' calculation constructs, projection (not propagation) of epoch covariances forward in time to try to enable better risk assessments. Two questions to be answered about each; situations to which such approaches are properly applicable, amount of utility that such methods offer.
Adaptive Neural Network Nonparametric Identifier With Normalized Learning Laws.
Chairez, Isaac
2016-04-05
This paper addresses the design of a normalized convergent learning law for neural networks (NNs) with continuous dynamics. The NN is used here to obtain a nonparametric model for uncertain systems described by a set of ordinary differential equations. The source of uncertainties is the presence of some external perturbations and poor knowledge of the nonlinear function describing the system dynamics. A new adaptive algorithm based on normalized algorithms was used to adjust the weights of the NN. The adaptive algorithm was derived by means of a nonstandard logarithmic Lyapunov function (LLF). Two identifiers were designed using two variations of LLFs leading to a normalized learning law for the first identifier and a variable gain normalized learning law. In the case of the second identifier, the inclusion of normalized learning laws yields to reduce the size of the convergence region obtained as solution of the practical stability analysis. On the other hand, the velocity of convergence for the learning laws depends on the norm of errors in inverse form. This fact avoids the peaking transient behavior in the time evolution of weights that accelerates the convergence of identification error. A numerical example demonstrates the improvements achieved by the algorithm introduced in this paper compared with classical schemes with no-normalized continuous learning methods. A comparison of the identification performance achieved by the no-normalized identifier and the ones developed in this paper shows the benefits of the learning law proposed in this paper.
Nonparametric dark energy reconstruction from supernova data.
Holsclaw, Tracy; Alam, Ujjaini; Sansó, Bruno; Lee, Herbert; Heitmann, Katrin; Habib, Salman; Higdon, David
2010-12-10
Understanding the origin of the accelerated expansion of the Universe poses one of the greatest challenges in physics today. Lacking a compelling fundamental theory to test, observational efforts are targeted at a better characterization of the underlying cause. If a new form of mass-energy, dark energy, is driving the acceleration, the redshift evolution of the equation of state parameter w(z) will hold essential clues as to its origin. To best exploit data from observations it is necessary to develop a robust and accurate reconstruction approach, with controlled errors, for w(z). We introduce a new, nonparametric method for solving the associated statistical inverse problem based on Gaussian process modeling and Markov chain Monte Carlo sampling. Applying this method to recent supernova measurements, we reconstruct the continuous history of w out to redshift z=1.5.
Nonparametric spirometry reference values for Hispanic Americans.
Glenn, Nancy L; Brown, Vanessa M
2011-02-01
Recent literature sites ethnic origin as a major factor in developing pulmonary function reference values. Extensive studies established reference values for European and African Americans, but not for Hispanic Americans. The Third National Health and Nutrition Examination Survey defines Hispanic as individuals of Spanish speaking cultures. While no group was excluded from the target population, sample size requirements only allowed inclusion of individuals who identified themselves as Mexican Americans. This research constructs nonparametric reference value confidence intervals for Hispanic American pulmonary function. The method is applicable to all ethnicities. We use empirical likelihood confidence intervals to establish normal ranges for reference values. Its major advantage: it is model free, but shares asymptotic properties of model based methods. Statistical comparisons indicate that empirical likelihood interval lengths are comparable to normal theory intervals. Power and efficiency studies agree with previously published theoretical results.
Useful and little-known applications of the Least Square Method and some consequences of covariances
NASA Astrophysics Data System (ADS)
Helene, Otaviano; Mariano, Leandro; Guimarães-Filho, Zwinglio
2016-10-01
Covariances are as important as variances when dealing with experimental data and they must be considered in fitting procedures and adjustments in order to preserve the statistical properties of the adjusted quantities. In this paper, we apply the Least Square Method in matrix form to several simple problems in order to evaluate the consequences of covariances in the fitting procedure. Among the examples, we demonstrate how a measurement of a physical quantity can change the adopted value of all other covariant quantities and how a new single point (x , y) improves the parameters of a previously adjusted straight-line.
Nonparametric inference of network structure and dynamics
NASA Astrophysics Data System (ADS)
Peixoto, Tiago P.
The network structure of complex systems determine their function and serve as evidence for the evolutionary mechanisms that lie behind them. Despite considerable effort in recent years, it remains an open challenge to formulate general descriptions of the large-scale structure of network systems, and how to reliably extract such information from data. Although many approaches have been proposed, few methods attempt to gauge the statistical significance of the uncovered structures, and hence the majority cannot reliably separate actual structure from stochastic fluctuations. Due to the sheer size and high-dimensionality of many networks, this represents a major limitation that prevents meaningful interpretations of the results obtained with such nonstatistical methods. In this talk, I will show how these issues can be tackled in a principled and efficient fashion by formulating appropriate generative models of network structure that can have their parameters inferred from data. By employing a Bayesian description of such models, the inference can be performed in a nonparametric fashion, that does not require any a priori knowledge or ad hoc assumptions about the data. I will show how this approach can be used to perform model comparison, and how hierarchical models yield the most appropriate trade-off between model complexity and quality of fit based on the statistical evidence present in the data. I will also show how this general approach can be elegantly extended to networks with edge attributes, that are embedded in latent spaces, and that change in time. The latter is obtained via a fully dynamic generative network model, based on arbitrary-order Markov chains, that can also be inferred in a nonparametric fashion. Throughout the talk I will illustrate the application of the methods with many empirical networks such as the internet at the autonomous systems level, the global airport network, the network of actors and films, social networks, citations among
Covariant harmonic oscillators: 1973 revisited
NASA Technical Reports Server (NTRS)
Noz, M. E.
1993-01-01
Using the relativistic harmonic oscillator, a physical basis is given to the phenomenological wave function of Yukawa which is covariant and normalizable. It is shown that this wave function can be interpreted in terms of the unitary irreducible representations of the Poincare group. The transformation properties of these covariant wave functions are also demonstrated.
Covariance hypotheses for LANDSAT data
NASA Technical Reports Server (NTRS)
Decell, H. P.; Peters, C.
1983-01-01
Two covariance hypotheses are considered for LANDSAT data acquired by sampling fields, one an autoregressive covariance structure and the other the hypothesis of exchangeability. A minimum entropy approximation of the first structure by the second is derived and shown to have desirable properties for incorporation into a mixture density estimation procedure. Results of a rough test of the exchangeability hypothesis are presented.
Covariance specification and estimation to improve top-down Green House Gas emission estimates
NASA Astrophysics Data System (ADS)
Ghosh, S.; Lopez-Coto, I.; Prasad, K.; Whetstone, J. R.
2015-12-01
accuracy, we perform a sensitivity study to further tune covariance parameters. Finally, we introduce a shrinkage based sample covariance estimation technique for both prior and mismatch covariances. This technique allows us to achieve similar accuracy nonparametrically in a more efficient and automated way.
Covariance Manipulation for Conjunction Assessment
NASA Technical Reports Server (NTRS)
Hejduk, M. D.
2016-01-01
The manipulation of space object covariances to try to provide additional or improved information to conjunction risk assessment is not an uncommon practice. Types of manipulation include fabricating a covariance when it is missing or unreliable to force the probability of collision (Pc) to a maximum value ('PcMax'), scaling a covariance to try to improve its realism or see the effect of covariance volatility on the calculated Pc, and constructing the equivalent of an epoch covariance at a convenient future point in the event ('covariance forecasting'). In bringing these methods to bear for Conjunction Assessment (CA) operations, however, some do not remain fully consistent with best practices for conducting risk management, some seem to be of relatively low utility, and some require additional information before they can contribute fully to risk analysis. This study describes some basic principles of modern risk management (following the Kaplan construct) and then examines the PcMax and covariance forecasting paradigms for alignment with these principles; it then further examines the expected utility of these methods in the modern CA framework. Both paradigms are found to be not without utility, but only in situations that are somewhat carefully circumscribed.
NONPARAMETRIC BAYESIAN ESTIMATION OF PERIODIC LIGHT CURVES
Wang Yuyang; Khardon, Roni; Protopapas, Pavlos
2012-09-01
Many astronomical phenomena exhibit patterns that have periodic behavior. An important step when analyzing data from such processes is the problem of identifying the period: estimating the period of a periodic function based on noisy observations made at irregularly spaced time points. This problem is still a difficult challenge despite extensive study in different disciplines. This paper makes several contributions toward solving this problem. First, we present a nonparametric Bayesian model for period finding, based on Gaussian Processes (GPs), that does not make assumptions on the shape of the periodic function. As our experiments demonstrate, the new model leads to significantly better results in period estimation especially when the light curve does not exhibit sinusoidal shape. Second, we develop a new algorithm for parameter optimization for GP which is useful when the likelihood function is very sensitive to the parameters with numerous local minima, as in the case of period estimation. The algorithm combines gradient optimization with grid search and incorporates several mechanisms to overcome the high computational complexity of GP. Third, we develop a novel approach for using domain knowledge, in the form of a probabilistic generative model, and incorporate it into the period estimation algorithm. Experimental results validate our approach showing significant improvement over existing methods.
Aggregate nonparametric safety analysis of traffic zones.
Siddiqui, Chowdhury; Abdel-Aty, Mohamed; Huang, Helai
2012-03-01
Exploring the significant variables related to specific types of crashes is vitally important in the planning stage of a transportation network. This paper aims to identify and examine important variables associated with total crashes and severe crashes per traffic analysis zone (TAZ) in four counties of the state of Florida by applying nonparametric statistical techniques such as data mining and random forest. The intention of investigating these factors in such aggregate level analysis is to incorporate proactive safety measures in transportation planning. Total and severe crashes per TAZ were modeled to provide predictive decision trees. The variables which carried higher weight of importance for total crashes per TAZ were - total number of intersections per TAZ, airport trip productions, light truck productions, and total roadway segment length with 35 mph posted speed limit. The other significant variables identified for total crashes were total roadway length with 15 mph posted speed limit, total roadway length with 65 mph posted speed limit, and non-home based work productions. For severe crashes, total number of intersections per TAZ, light truck productions, total roadway length with 35 mph posted speed limit, and total roadway length with 65 mph posted speed limit were among the significant variables. These variables were further verified and supported by the random forest results.
Nonparametric methods in actigraphy: An update
Gonçalves, Bruno S.B.; Cavalcanti, Paula R.A.; Tavares, Gracilene R.; Campos, Tania F.; Araujo, John F.
2014-01-01
Circadian rhythmicity in humans has been well studied using actigraphy, a method of measuring gross motor movement. As actigraphic technology continues to evolve, it is important for data analysis to keep pace with new variables and features. Our objective is to study the behavior of two variables, interdaily stability and intradaily variability, to describe rest activity rhythm. Simulated data and actigraphy data of humans, rats, and marmosets were used in this study. We modified the method of calculation for IV and IS by modifying the time intervals of analysis. For each variable, we calculated the average value (IVm and ISm) results for each time interval. Simulated data showed that (1) synchronization analysis depends on sample size, and (2) fragmentation is independent of the amplitude of the generated noise. We were able to obtain a significant difference in the fragmentation patterns of stroke patients using an IVm variable, while the variable IV60 was not identified. Rhythmic synchronization of activity and rest was significantly higher in young than adults with Parkinson׳s when using the ISM variable; however, this difference was not seen using IS60. We propose an updated format to calculate rhythmic fragmentation, including two additional optional variables. These alternative methods of nonparametric analysis aim to more precisely detect sleep–wake cycle fragmentation and synchronization. PMID:26483921
Covariance Models for Hydrological Applications
NASA Astrophysics Data System (ADS)
Hristopulos, Dionissios
2014-05-01
This methodological contribution aims to present some new covariance models with applications in the stochastic analysis of hydrological processes. More specifically, we present explicit expressions for radially symmetric, non-differentiable, Spartan covariance functions in one, two, and three dimensions. The Spartan covariance parameters include a characteristic length, an amplitude coefficient, and a rigidity coefficient which determines the shape of the covariance function. Different expressions are obtained depending on the value of the rigidity coefficient and the dimensionality. If the value of the rigidity coefficient is much larger than one, the Spartan covariance function exhibits multiscaling. Spartan covariance models are more flexible than the classical geostatatistical models (e.g., spherical, exponential). Their non-differentiability makes them suitable for modelling the properties of geological media. We also present a family of radially symmetric, infinitely differentiable Bessel-Lommel covariance functions which are valid in any dimension. These models involve combinations of Bessel and Lommel functions. They provide a generalization of the J-Bessel covariance function, and they can be used to model smooth processes with an oscillatory decay of correlations. We discuss the dependence of the integral range of the Spartan and Bessel-Lommel covariance functions on the parameters. We point out that the dependence is not uniquely specified by the characteristic length, unlike the classical geostatistical models. Finally, we define and discuss the use of the generalized spectrum for characterizing different correlation length scales; the spectrum is defined in terms of an exponent α. We show that the spectrum values obtained for exponent values less than one can be used to discriminate between mean-square continuous but non-differentiable random fields. References [1] D. T. Hristopulos and S. Elogne, 2007. Analytic properties and covariance functions of
[Clinical research XIX. From clinical judgment to analysis of covariance].
Pérez-Rodríguez, Marcela; Palacios-Cruz, Lino; Moreno, Jorge; Rivas-Ruiz, Rodolfo; Talavera, Juan O
2014-01-01
The analysis of covariance (ANCOVA) is based on the general linear models. This technique involves a regression model, often multiple, in which the outcome is presented as a continuous variable, the independent variables are qualitative or are introduced into the model as dummy or dichotomous variables, and factors for which adjustment is required (covariates) can be in any measurement level (i.e. nominal, ordinal or continuous). The maneuvers can be entered into the model as 1) fixed effects, or 2) random effects. The difference between fixed effects and random effects depends on the type of information we want from the analysis of the effects. ANCOVA effect separates the independent variables from the effect of co-variables, i.e., corrects the dependent variable eliminating the influence of covariates, given that these variables change in conjunction with maneuvers or treatments, affecting the outcome variable. ANCOVA should be done only if it meets three assumptions: 1) the relationship between the covariate and the outcome is linear, 2) there is homogeneity of slopes, and 3) the covariate and the independent variable are independent from each other.
Nonparametric Bayesian Modeling for Automated Database Schema Matching
Ferragut, Erik M; Laska, Jason A
2015-01-01
The problem of merging databases arises in many government and commercial applications. Schema matching, a common first step, identifies equivalent fields between databases. We introduce a schema matching framework that builds nonparametric Bayesian models for each field and compares them by computing the probability that a single model could have generated both fields. Our experiments show that our method is more accurate and faster than the existing instance-based matching algorithms in part because of the use of nonparametric Bayesian models.
Nonparametric Representations for Integrated Inference, Control, and Sensing
2015-10-01
learning and modeling from data, and tightly integrate these models into new decentralized cooperative planning algorithms. The main output of this effort...AFRL-RY-WP-TR-2015-0174 NONPARAMETRIC REPRESENTATIONS FOR INTEGRATED INFERENCE, CONTROL, AND SENSING John Fisher and Jon How...SUBTITLE NONPARAMETRIC REPRESENTATIONS FOR INTEGRATED INFERENCE, CONTROL, AND SENSING 5a. CONTRACT NUMBER FA8650-11-1-7154 5b. GRANT NUMBER 5c
Hawking radiation and covariant anomalies
Banerjee, Rabin; Kulkarni, Shailesh
2008-01-15
Generalizing the method of Wilczek and collaborators we provide a derivation of Hawking radiation from charged black holes using only covariant gauge and gravitational anomalies. The reliability and universality of the anomaly cancellation approach to Hawking radiation is also discussed.
Das, Kiranmoy; Daniels, Michael J
2014-03-01
Estimation of the covariance structure for irregular sparse longitudinal data has been studied by many authors in recent years but typically using fully parametric specifications. In addition, when data are collected from several groups over time, it is known that assuming the same or completely different covariance matrices over groups can lead to loss of efficiency and/or bias. Nonparametric approaches have been proposed for estimating the covariance matrix for regular univariate longitudinal data by sharing information across the groups under study. For the irregular case, with longitudinal measurements that are bivariate or multivariate, modeling becomes more difficult. In this article, to model bivariate sparse longitudinal data from several groups, we propose a flexible covariance structure via a novel matrix stick-breaking process for the residual covariance structure and a Dirichlet process mixture of normals for the random effects. Simulation studies are performed to investigate the effectiveness of the proposed approach over more traditional approaches. We also analyze a subset of Framingham Heart Study data to examine how the blood pressure trajectories and covariance structures differ for the patients from different BMI groups (high, medium, and low) at baseline.
Multiple Imputation of a Randomly Censored Covariate Improves Logistic Regression Analysis.
Atem, Folefac D; Qian, Jing; Maye, Jacqueline E; Johnson, Keith A; Betensky, Rebecca A
2016-01-01
Randomly censored covariates arise frequently in epidemiologic studies. The most commonly used methods, including complete case and single imputation or substitution, suffer from inefficiency and bias. They make strong parametric assumptions or they consider limit of detection censoring only. We employ multiple imputation, in conjunction with semi-parametric modeling of the censored covariate, to overcome these shortcomings and to facilitate robust estimation. We develop a multiple imputation approach for randomly censored covariates within the framework of a logistic regression model. We use the non-parametric estimate of the covariate distribution or the semiparametric Cox model estimate in the presence of additional covariates in the model. We evaluate this procedure in simulations, and compare its operating characteristics to those from the complete case analysis and a survival regression approach. We apply the procedures to an Alzheimer's study of the association between amyloid positivity and maternal age of onset of dementia. Multiple imputation achieves lower standard errors and higher power than the complete case approach under heavy and moderate censoring and is comparable under light censoring. The survival regression approach achieves the highest power among all procedures, but does not produce interpretable estimates of association. Multiple imputation offers a favorable alternative to complete case analysis and ad hoc substitution methods in the presence of randomly censored covariates within the framework of logistic regression.
Network Reconstruction Using Nonparametric Additive ODE Models
Henderson, James; Michailidis, George
2014-01-01
Network representations of biological systems are widespread and reconstructing unknown networks from data is a focal problem for computational biologists. For example, the series of biochemical reactions in a metabolic pathway can be represented as a network, with nodes corresponding to metabolites and edges linking reactants to products. In a different context, regulatory relationships among genes are commonly represented as directed networks with edges pointing from influential genes to their targets. Reconstructing such networks from data is a challenging problem receiving much attention in the literature. There is a particular need for approaches tailored to time-series data and not reliant on direct intervention experiments, as the former are often more readily available. In this paper, we introduce an approach to reconstructing directed networks based on dynamic systems models. Our approach generalizes commonly used ODE models based on linear or nonlinear dynamics by extending the functional class for the functions involved from parametric to nonparametric models. Concomitantly we limit the complexity by imposing an additive structure on the estimated slope functions. Thus the submodel associated with each node is a sum of univariate functions. These univariate component functions form the basis for a novel coupling metric that we define in order to quantify the strength of proposed relationships and hence rank potential edges. We show the utility of the method by reconstructing networks using simulated data from computational models for the glycolytic pathway of Lactocaccus Lactis and a gene network regulating the pluripotency of mouse embryonic stem cells. For purposes of comparison, we also assess reconstruction performance using gene networks from the DREAM challenges. We compare our method to those that similarly rely on dynamic systems models and use the results to attempt to disentangle the distinct roles of linearity, sparsity, and derivative
Chan, Kwun Chuen Gary; Yam, Sheung Chi Phillip; Zhang, Zheng
2016-06-01
The estimation of average treatment effects based on observational data is extremely important in practice and has been studied by generations of statisticians under different frameworks. Existing globally efficient estimators require non-parametric estimation of a propensity score function, an outcome regression function or both, but their performance can be poor in practical sample sizes. Without explicitly estimating either functions, we consider a wide class calibration weights constructed to attain an exact three-way balance of the moments of observed covariates among the treated, the control, and the combined group. The wide class includes exponential tilting, empirical likelihood and generalized regression as important special cases, and extends survey calibration estimators to different statistical problems and with important distinctions. Global semiparametric efficiency for the estimation of average treatment effects is established for this general class of calibration estimators. The results show that efficiency can be achieved by solely balancing the covariate distributions without resorting to direct estimation of propensity score or outcome regression function. We also propose a consistent estimator for the efficient asymptotic variance, which does not involve additional functional estimation of either the propensity score or the outcome regression functions. The proposed variance estimator outperforms existing estimators that require a direct approximation of the efficient influence function.
Aghamousa, Amir; Shafieloo, Arman; Arjunwadkar, Mihir; Souradeep, Tarun E-mail: shafieloo@kasi.re.kr E-mail: tarun@iucaa.ernet.in
2015-02-01
Estimation of the angular power spectrum is one of the important steps in Cosmic Microwave Background (CMB) data analysis. Here, we present a nonparametric estimate of the temperature angular power spectrum for the Planck 2013 CMB data. The method implemented in this work is model-independent, and allows the data, rather than the model, to dictate the fit. Since one of the main targets of our analysis is to test the consistency of the ΛCDM model with Planck 2013 data, we use the nuisance parameters associated with the best-fit ΛCDM angular power spectrum to remove foreground contributions from the data at multipoles ℓ ≥50. We thus obtain a combined angular power spectrum data set together with the full covariance matrix, appropriately weighted over frequency channels. Our subsequent nonparametric analysis resolves six peaks (and five dips) up to ℓ ∼1850 in the temperature angular power spectrum. We present uncertainties in the peak/dip locations and heights at the 95% confidence level. We further show how these reflect the harmonicity of acoustic peaks, and can be used for acoustic scale estimation. Based on this nonparametric formalism, we found the best-fit ΛCDM model to be at 36% confidence distance from the center of the nonparametric confidence set—this is considerably larger than the confidence distance (9%) derived earlier from a similar analysis of the WMAP 7-year data. Another interesting result of our analysis is that at low multipoles, the Planck data do not suggest any upturn, contrary to the expectation based on the integrated Sachs-Wolfe contribution in the best-fit ΛCDM cosmology.
Evaluation of Tungsten Nuclear Reaction Data with Covariances
Trkov, A. Capote, R.; Kodeli, I.; Leal, L.
2008-12-15
As a follow-up of the work presented at the ND-2007 conference in Nice, additional fast reactor benchmarks were analyzed. Adjustment to the cross sections in the keV region was necessary. Evaluated neutron cross section data files for {sup 180,182,183,184,186}W isotopes were produced. Covariances were generated for all isotopes except {sup 180}W. In the resonance range the retro-active method was used. Above the resolved resonance range the covariance prior was generated by the Monte Carlo technique from nuclear model calculations with the Empire-II code. Experimental data were taken into account through the GANDR system using the generalized least-squares technique. Introducing experimental data results in relatively small changes in the cross sections, but greatly constrains the uncertainties. The covariance files are currently undergoing testing.
Evaluation of Tungsten Nuclear Reaction Data with Covariances
Trkov, A.; Capote, R.; Kodeli, I.; Leal, Luiz C.
2008-12-01
As a follow-up of the work presented at the ND-2007 conference in Nice, additional fast reactor benchmarks were analyzed. Adjustment to the cross sections in the keV region was necessary. Evaluated neutron cross section data files for 180,182,183,184,186W isotopes were produced. Covariances were generated for all isotopes except 180W. In the resonance range the retro-active method was used. Above the resolved resonance range the covariance prior was generated by the Monte Carlo technique from nuclear model calculations with the Empire-II code. Experimental data were taken into account through the GANDR system using the generalized least-squares technique. Introducing experimental data results in relatively small changes in the cross sections, but greatly constrains the uncertainties. The covariance files are currently undergoing testing.
Covariate-free and Covariate-dependent Reliability.
Bentler, Peter M
2016-12-01
Classical test theory reliability coefficients are said to be population specific. Reliability generalization, a meta-analysis method, is the main procedure for evaluating the stability of reliability coefficients across populations. A new approach is developed to evaluate the degree of invariance of reliability coefficients to population characteristics. Factor or common variance of a reliability measure is partitioned into parts that are, and are not, influenced by control variables, resulting in a partition of reliability into a covariate-dependent and a covariate-free part. The approach can be implemented in a single sample and can be applied to a variety of reliability coefficients.
Levy Matrices and Financial Covariances
NASA Astrophysics Data System (ADS)
Burda, Zdzislaw; Jurkiewicz, Jerzy; Nowak, Maciej A.; Papp, Gabor; Zahed, Ismail
2003-10-01
In a given market, financial covariances capture the intra-stock correlations and can be used to address statistically the bulk nature of the market as a complex system. We provide a statistical analysis of three SP500 covariances with evidence for raw tail distributions. We study the stability of these tails against reshuffling for the SP500 data and show that the covariance with the strongest tails is robust, with a spectral density in remarkable agreement with random Lévy matrix theory. We study the inverse participation ratio for the three covariances. The strong localization observed at both ends of the spectral density is analogous to the localization exhibited in the random Lévy matrix ensemble. We discuss two competitive mechanisms responsible for the occurrence of an extensive and delocalized eigenvalue at the edge of the spectrum: (a) the Lévy character of the entries of the correlation matrix and (b) a sort of off-diagonal order induced by underlying inter-stock correlations. (b) can be destroyed by reshuffling, while (a) cannot. We show that the stocks with the largest scattering are the least susceptible to correlations, and likely candidates for the localized states. We introduce a simple model for price fluctuations which captures behavior of the SP500 covariances. It may be of importance for assets diversification.
AFCI-2.0 Neutron Cross Section Covariance Library
Herman, M.; Herman, M; Oblozinsky, P.; Mattoon, C.M.; Pigni, M.; Hoblit, S.; Mughabghab, S.F.; Sonzogni, A.; Talou, P.; Chadwick, M.B.; Hale, G.M.; Kahler, A.C.; Kawano, T.; Little, R.C.; Yount, P.G.
2011-03-01
The cross section covariance library has been under development by BNL-LANL collaborative effort over the last three years. The project builds on two covariance libraries developed earlier, with considerable input from BNL and LANL. In 2006, international effort under WPEC Subgroup 26 produced BOLNA covariance library by putting together data, often preliminary, from various sources for most important materials for nuclear reactor technology. This was followed in 2007 by collaborative effort of four US national laboratories to produce covariances, often of modest quality - hence the name low-fidelity, for virtually complete set of materials included in ENDF/B-VII.0. The present project is focusing on covariances of 4-5 major reaction channels for 110 materials of importance for power reactors. The work started under Global Nuclear Energy Partnership (GNEP) in 2008, which changed to Advanced Fuel Cycle Initiative (AFCI) in 2009. With the 2011 release the name has changed to the Covariance Multigroup Matrix for Advanced Reactor Applications (COMMARA) version 2.0. The primary purpose of the library is to provide covariances for AFCI data adjustment project, which is focusing on the needs of fast advanced burner reactors. Responsibility of BNL was defined as developing covariances for structural materials and fission products, management of the library and coordination of the work; LANL responsibility was defined as covariances for light nuclei and actinides. The COMMARA-2.0 covariance library has been developed by BNL-LANL collaboration for Advanced Fuel Cycle Initiative applications over the period of three years, 2008-2010. It contains covariances for 110 materials relevant to fast reactor R&D. The library is to be used together with the ENDF/B-VII.0 central values of the latest official release of US files of evaluated neutron cross sections. COMMARA-2.0 library contains neutron cross section covariances for 12 light nuclei (coolants and moderators), 78 structural
Nonparametric statistical testing of EEG- and MEG-data.
Maris, Eric; Oostenveld, Robert
2007-08-15
In this paper, we show how ElectroEncephaloGraphic (EEG) and MagnetoEncephaloGraphic (MEG) data can be analyzed statistically using nonparametric techniques. Nonparametric statistical tests offer complete freedom to the user with respect to the test statistic by means of which the experimental conditions are compared. This freedom provides a straightforward way to solve the multiple comparisons problem (MCP) and it allows to incorporate biophysically motivated constraints in the test statistic, which may drastically increase the sensitivity of the statistical test. The paper is written for two audiences: (1) empirical neuroscientists looking for the most appropriate data analysis method, and (2) methodologists interested in the theoretical concepts behind nonparametric statistical tests. For the empirical neuroscientist, a large part of the paper is written in a tutorial-like fashion, enabling neuroscientists to construct their own statistical test, maximizing the sensitivity to the expected effect. And for the methodologist, it is explained why the nonparametric test is formally correct. This means that we formulate a null hypothesis (identical probability distribution in the different experimental conditions) and show that the nonparametric test controls the false alarm rate under this null hypothesis.
A class of covariate-dependent spatiotemporal covariance functions.
Reich, Brian J; Eidsvik, Jo; Guindani, Michele; Nail, Amy J; Schmidt, Alexandra M
2011-12-01
In geostatistics, it is common to model spatially distributed phenomena through an underlying stationary and isotropic spatial process. However, these assumptions are often untenable in practice because of the influence of local effects in the correlation structure. Therefore, it has been of prolonged interest in the literature to provide flexible and effective ways to model non-stationarity in the spatial effects. Arguably, due to the local nature of the problem, we might envision that the correlation structure would be highly dependent on local characteristics of the domain of study, namely the latitude, longitude and altitude of the observation sites, as well as other locally defined covariate information. In this work, we provide a flexible and computationally feasible way for allowing the correlation structure of the underlying processes to depend on local covariate information. We discuss the properties of the induced covariance functions and discuss methods to assess its dependence on local covariate information by means of a simulation study and the analysis of data observed at ozone-monitoring stations in the Southeast United States.
Graph embedded nonparametric mutual information for supervised dimensionality reduction.
Bouzas, Dimitrios; Arvanitopoulos, Nikolaos; Tefas, Anastasios
2015-05-01
In this paper, we propose a novel algorithm for dimensionality reduction that uses as a criterion the mutual information (MI) between the transformed data and their corresponding class labels. The MI is a powerful criterion that can be used as a proxy to the Bayes error rate. Furthermore, recent quadratic nonparametric implementations of MI are computationally efficient and do not require any prior assumptions about the class densities. We show that the quadratic nonparametric MI can be formulated as a kernel objective in the graph embedding framework. Moreover, we propose its linear equivalent as a novel linear dimensionality reduction algorithm. The derived methods are compared against the state-of-the-art dimensionality reduction algorithms with various classifiers and on various benchmark and real-life datasets. The experimental results show that nonparametric MI as an optimization objective for dimensionality reduction gives comparable and in most of the cases better results compared with other dimensionality reduction methods.
Predicting Market Impact Costs Using Nonparametric Machine Learning Models.
Park, Saerom; Lee, Jaewook; Son, Youngdoo
2016-01-01
Market impact cost is the most significant portion of implicit transaction costs that can reduce the overall transaction cost, although it cannot be measured directly. In this paper, we employed the state-of-the-art nonparametric machine learning models: neural networks, Bayesian neural network, Gaussian process, and support vector regression, to predict market impact cost accurately and to provide the predictive model that is versatile in the number of variables. We collected a large amount of real single transaction data of US stock market from Bloomberg Terminal and generated three independent input variables. As a result, most nonparametric machine learning models outperformed a-state-of-the-art benchmark parametric model such as I-star model in four error measures. Although these models encounter certain difficulties in separating the permanent and temporary cost directly, nonparametric machine learning models can be good alternatives in reducing transaction costs by considerably improving in prediction performance.
Mathematical models for nonparametric inferences from line transect data
Burnham, K.P.; Anderson, D.R.
1976-01-01
A general mathematical theory of line transects is develoepd which supplies a framework for nonparametric density estimation based on either right angle or sighting distances. The probability of observing a point given its right angle distance (y) from the line is generalized to an arbitrary function g(y). Given only that g(O) = 1, it is shown there are nonparametric approaches to density estimation using the observed right angle distances. The model is then generalized to include sighting distances (r). Let f(y/r) be the conditional distribution of right angle distance given sighting distance. It is shown that nonparametric estimation based only on sighting distances requires we know the transformation of r given by f(O/r).
Predicting Market Impact Costs Using Nonparametric Machine Learning Models
Park, Saerom; Lee, Jaewook; Son, Youngdoo
2016-01-01
Market impact cost is the most significant portion of implicit transaction costs that can reduce the overall transaction cost, although it cannot be measured directly. In this paper, we employed the state-of-the-art nonparametric machine learning models: neural networks, Bayesian neural network, Gaussian process, and support vector regression, to predict market impact cost accurately and to provide the predictive model that is versatile in the number of variables. We collected a large amount of real single transaction data of US stock market from Bloomberg Terminal and generated three independent input variables. As a result, most nonparametric machine learning models outperformed a-state-of-the-art benchmark parametric model such as I-star model in four error measures. Although these models encounter certain difficulties in separating the permanent and temporary cost directly, nonparametric machine learning models can be good alternatives in reducing transaction costs by considerably improving in prediction performance. PMID:26926235
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A simulation-based marginal method for longitudinal data with dropout and mismeasured covariates.
Yi, Grace Y
2008-07-01
Longitudinal data often contain missing observations and error-prone covariates. Extensive attention has been directed to analysis methods to adjust for the bias induced by missing observations. There is relatively little work on investigating the effects of covariate measurement error on estimation of the response parameters, especially on simultaneously accounting for the biases induced by both missing values and mismeasured covariates. It is not clear what the impact of ignoring measurement error is when analyzing longitudinal data with both missing observations and error-prone covariates. In this article, we study the effects of covariate measurement error on estimation of the response parameters for longitudinal studies. We develop an inference method that adjusts for the biases induced by measurement error as well as by missingness. The proposed method does not require the full specification of the distribution of the response vector but only requires modeling its mean and variance structures. Furthermore, the proposed method employs the so-called functional modeling strategy to handle the covariate process, with the distribution of covariates left unspecified. These features, plus the simplicity of implementation, make the proposed method very attractive. In this paper, we establish the asymptotic properties for the resulting estimators. With the proposed method, we conduct sensitivity analyses on a cohort data set arising from the Framingham Heart Study. Simulation studies are carried out to evaluate the impact of ignoring covariate measurement error and to assess the performance of the proposed method.
Covariation Neglect among Novice Investors
ERIC Educational Resources Information Center
Hedesstrom, Ted Martin; Svedsater, Henrik; Garling, Tommy
2006-01-01
In 4 experiments, undergraduates made hypothetical investment choices. In Experiment 1, participants paid more attention to the volatility of individual assets than to the volatility of aggregated portfolios. The results of Experiment 2 show that most participants diversified even when this increased risk because of covariation between the returns…
Condition Number Regularized Covariance Estimation.
Won, Joong-Ho; Lim, Johan; Kim, Seung-Jean; Rajaratnam, Bala
2013-06-01
Estimation of high-dimensional covariance matrices is known to be a difficult problem, has many applications, and is of current interest to the larger statistics community. In many applications including so-called the "large p small n" setting, the estimate of the covariance matrix is required to be not only invertible, but also well-conditioned. Although many regularization schemes attempt to do this, none of them address the ill-conditioning problem directly. In this paper, we propose a maximum likelihood approach, with the direct goal of obtaining a well-conditioned estimator. No sparsity assumption on either the covariance matrix or its inverse are are imposed, thus making our procedure more widely applicable. We demonstrate that the proposed regularization scheme is computationally efficient, yields a type of Steinian shrinkage estimator, and has a natural Bayesian interpretation. We investigate the theoretical properties of the regularized covariance estimator comprehensively, including its regularization path, and proceed to develop an approach that adaptively determines the level of regularization that is required. Finally, we demonstrate the performance of the regularized estimator in decision-theoretic comparisons and in the financial portfolio optimization setting. The proposed approach has desirable properties, and can serve as a competitive procedure, especially when the sample size is small and when a well-conditioned estimator is required.
Covariance Modifications to Subspace Bases
Harris, D B
2008-11-19
Adaptive signal processing algorithms that rely upon representations of signal and noise subspaces often require updates to those representations when new data become available. Subspace representations frequently are estimated from available data with singular value (SVD) decompositions. Subspace updates require modifications to these decompositions. Updates can be performed inexpensively provided they are low-rank. A substantial literature on SVD updates exists, frequently focusing on rank-1 updates (see e.g. [Karasalo, 1986; Comon and Golub, 1990, Badeau, 2004]). In these methods, data matrices are modified by addition or deletion of a row or column, or data covariance matrices are modified by addition of the outer product of a new vector. A recent paper by Brand [2006] provides a general and efficient method for arbitrary rank updates to an SVD. The purpose of this note is to describe a closely-related method for applications where right singular vectors are not required. This note also describes the SVD updates to a particular scenario of interest in seismic array signal processing. The particular application involve updating the wideband subspace representation used in seismic subspace detectors [Harris, 2006]. These subspace detectors generalize waveform correlation algorithms to detect signals that lie in a subspace of waveforms of dimension d {ge} 1. They potentially are of interest because they extend the range of waveform variation over which these sensitive detectors apply. Subspace detectors operate by projecting waveform data from a detection window into a subspace specified by a collection of orthonormal waveform basis vectors (referred to as the template). Subspace templates are constructed from a suite of normalized, aligned master event waveforms that may be acquired by a single sensor, a three-component sensor, an array of such sensors or a sensor network. The template design process entails constructing a data matrix whose columns contain the
Nonparametric estimation of a convex bathtub-shaped hazard function.
Jankowski, Hanna K; Wellner, Jon A
2009-11-01
In this paper, we study the nonparametric maximum likelihood estimator (MLE) of a convex hazard function. We show that the MLE is consistent and converges at a local rate of n(2/5) at points x(0) where the true hazard function is positive and strictly convex. Moreover, we establish the pointwise asymptotic distribution theory of our estimator under these same assumptions. One notable feature of the nonparametric MLE studied here is that no arbitrary choice of tuning parameter (or complicated data-adaptive selection of the tuning parameter) is required.
Nonparametric estimation of a convex bathtub-shaped hazard function
JANKOWSKI, HANNA K.; WELLNER, JON A.
2010-01-01
In this paper, we study the nonparametric maximum likelihood estimator (MLE) of a convex hazard function. We show that the MLE is consistent and converges at a local rate of n2/5 at points x0 where the true hazard function is positive and strictly convex. Moreover, we establish the pointwise asymptotic distribution theory of our estimator under these same assumptions. One notable feature of the nonparametric MLE studied here is that no arbitrary choice of tuning parameter (or complicated data-adaptive selection of the tuning parameter) is required. PMID:20383267
Liu, Xiaofeng Steven
2011-05-01
The use of covariates is commonly believed to reduce the unexplained error variance and the standard error for the comparison of treatment means, but the reduction in the standard error is neither guaranteed nor uniform over different sample sizes. The covariate mean differences between the treatment conditions can inflate the standard error of the covariate-adjusted mean difference and can actually produce a larger standard error for the adjusted mean difference than that for the unadjusted mean difference. When the covariate observations are conceived of as randomly varying from one study to another, the covariate mean differences can be related to a Hotelling's T(2) . Using this Hotelling's T(2) statistic, one can always find a minimum sample size to achieve a high probability of reducing the standard error and confidence interval width for the adjusted mean difference.
Nonparametric Item Response Curve Estimation with Correction for Measurement Error
ERIC Educational Resources Information Center
Guo, Hongwen; Sinharay, Sandip
2011-01-01
Nonparametric or kernel regression estimation of item response curves (IRCs) is often used in item analysis in testing programs. These estimates are biased when the observed scores are used as the regressor because the observed scores are contaminated by measurement error. Accuracy of this estimation is a concern theoretically and operationally.…
Three Classes of Nonparametric Differential Step Functioning Effect Estimators
ERIC Educational Resources Information Center
Penfield, Randall D.
2008-01-01
The examination of measurement invariance in polytomous items is complicated by the possibility that the magnitude and sign of lack of invariance may vary across the steps underlying the set of polytomous response options, a concept referred to as differential step functioning (DSF). This article describes three classes of nonparametric DSF effect…
A Unifying Framework for Teaching Nonparametric Statistical Tests
ERIC Educational Resources Information Center
Bargagliotti, Anna E.; Orrison, Michael E.
2014-01-01
Increased importance is being placed on statistics at both the K-12 and undergraduate level. Research divulging effective methods to teach specific statistical concepts is still widely sought after. In this paper, we focus on best practices for teaching topics in nonparametric statistics at the undergraduate level. To motivate the work, we…
A Simulation Comparison of Parametric and Nonparametric Dimensionality Detection Procedures
ERIC Educational Resources Information Center
Mroch, Andrew A.; Bolt, Daniel M.
2006-01-01
Recently, nonparametric methods have been proposed that provide a dimensionally based description of test structure for tests with dichotomous items. Because such methods are based on different notions of dimensionality than are assumed when using a psychometric model, it remains unclear whether these procedures might lead to a different…
Joint Entropy Minimization for Learning in Nonparametric Framework
2006-06-09
Tibshirani, G. Sherlock , W. C. Chan, T. C. Greiner, D. D. Weisenburger, J. O. Armitage, R. Warnke, R. Levy, W. Wilson, M. R. Grever, J. C. Byrd, D. Botstein, P...Entropy Minimization for Learning in Nonparametric Framework 33 [11] D.L. Collins, A.P. Zijdenbos, J.G. Kollokian, N.J. Sled, C.J. Kabani, C.J. Holmes
Estimation of Spatial Dynamic Nonparametric Durbin Models with Fixed Effects
ERIC Educational Resources Information Center
Qian, Minghui; Hu, Ridong; Chen, Jianwei
2016-01-01
Spatial panel data models have been widely studied and applied in both scientific and social science disciplines, especially in the analysis of spatial influence. In this paper, we consider the spatial dynamic nonparametric Durbin model (SDNDM) with fixed effects, which takes the nonlinear factors into account base on the spatial dynamic panel…
A New Nonparametric Levene Test for Equal Variances
ERIC Educational Resources Information Center
Nordstokke, David W.; Zumbo, Bruno D.
2010-01-01
Tests of the equality of variances are sometimes used on their own to compare variability across groups of experimental or non-experimental conditions but they are most often used alongside other methods to support assumptions made about variances. A new nonparametric test of equality of variances is described and compared to current "gold…
ERIC Educational Resources Information Center
Gierl, Mark J.; Bolt, Daniel M.
2001-01-01
Presents an overview of nonparametric regression as it allies to differential item functioning analysis and then provides three examples to illustrate how nonparametric regression can be applied to multilingual, multicultural data to study group differences. (SLD)
Neutron Cross Section Covariances for Structural Materials and Fission Products
NASA Astrophysics Data System (ADS)
Hoblit, S.; Cho, Y.-S.; Herman, M.; Mattoon, C. M.; Mughabghab, S. F.; Obložinský, P.; Pigni, M. T.; Sonzogni, A. A.
2011-12-01
We describe neutron cross section covariances for 78 structural materials and fission products produced for the new US evaluated nuclear reaction library ENDF/B-VII.1. Neutron incident energies cover full range from 10 eV to 20 MeV and covariances are primarily provided for capture, elastic and inelastic scattering as well as (n,2n). The list of materials follows priorities defined by the Advanced Fuel Cycle Initiative, the major application being data adjustment for advanced fast reactor systems. Thus, in addition to 28 structural materials and 49 fission products, the list includes also 23Na which is important fast reactor coolant. Due to extensive amount of materials, we adopted a variety of methodologies depending on the priority of a specific material. In the resolved resonance region we primarily used resonance parameter uncertainties given in Atlas of Neutron Resonances and either applied the kernel approximation to propagate these uncertainties into cross section uncertainties or resorted to simplified estimates based on integral quantities. For several priority materials we adopted MF32 covariances produced by SAMMY at ORNL, modified by us by adding MF33 covariances to account for systematic uncertainties. In the fast neutron region we resorted to three methods. The most sophisticated was EMPIRE-KALMAN method which combines experimental data from EXFOR library with nuclear reaction modeling and least-squares fitting. The two other methods used simplified estimates, either based on the propagation of nuclear reaction model parameter uncertainties or on a dispersion analysis of central cross section values in recent evaluated data files. All covariances were subject to quality assurance procedures adopted recently by CSEWG. In addition, tools were developed to allow inspection of processed covariances and computed integral quantities, and for comparing these values to data from the Atlas and the astrophysics database KADoNiS.
Fu, Wei; Simonoff, Jeffrey S
2016-12-26
SUMMARYTree methods (recursive partitioning) are a popular class of nonparametric methods for analyzing data. One extension of the basic tree methodology is the survival tree, which applies recursive partitioning to censored survival data. There are several existing survival tree methods in the literature, which are mainly designed for right-censored data. We propose two new survival trees for left-truncated and right-censored (LTRC) data, which can be seen as a generalization of the traditional survival tree for right-censored data. Further, we show that such trees can be used to analyze survival data with time-varying covariates, essentially building a time-varying covariates survival tree. Implementation of the methods is easy, and simulations and real data analysis results show that the proposed methods work well for LTRC data and survival data with time-varying covariates, respectively.
Understanding covariate shift in model performance
McGaughey, Georgia; Walters, W. Patrick; Goldman, Brian
2016-01-01
Three (3) different methods (logistic regression, covariate shift and k-NN) were applied to five (5) internal datasets and one (1) external, publically available dataset where covariate shift existed. In all cases, k-NN’s performance was inferior to either logistic regression or covariate shift. Surprisingly, there was no obvious advantage for using covariate shift to reweight the training data in the examined datasets. PMID:27803797
Covariate analysis of survival data: a small-sample study of Cox's model
Johnson, M.E.; Tolley, H.D.; Bryson, M.C.; Goldman, A.S.
1982-09-01
Cox's proportional-hazards model is frequently used to adjust for covariate effects in survival-data analysis. The small-sample performances of the maximum partial likelihood estimators of the regression parameters in a two-covariate hazard function model are evaluated with respect to bias, variance, and power in hypothesis tests. Previous Monte Carlo work on the two-sample problem is reviewed.
Are Maxwell's equations Lorentz-covariant?
NASA Astrophysics Data System (ADS)
Redžić, D. V.
2017-01-01
It is stated in many textbooks that Maxwell's equations are manifestly covariant when written down in tensorial form. We recall that tensorial form of Maxwell's equations does not secure their tensorial contents; they become covariant by postulating certain transformation properties of field functions. That fact should be stressed when teaching about the covariance of Maxwell's equations.
Lorentz-covariant dissipative Lagrangian systems
NASA Technical Reports Server (NTRS)
Kaufman, A. N.
1985-01-01
The concept of dissipative Hamiltonian system is converted to Lorentz-covariant form, with evolution generated jointly by two scalar functionals, the Lagrangian action and the global entropy. A bracket formulation yields the local covariant laws of energy-momentum conservation and of entropy production. The formalism is illustrated by a derivation of the covariant Landau kinetic equation.
Missing continuous outcomes under covariate dependent missingness in cluster randomised trials.
Hossain, Anower; Diaz-Ordaz, Karla; Bartlett, Jonathan W
2016-05-13
Attrition is a common occurrence in cluster randomised trials which leads to missing outcome data. Two approaches for analysing such trials are cluster-level analysis and individual-level analysis. This paper compares the performance of unadjusted cluster-level analysis, baseline covariate adjusted cluster-level analysis and linear mixed model analysis, under baseline covariate dependent missingness in continuous outcomes, in terms of bias, average estimated standard error and coverage probability. The methods of complete records analysis and multiple imputation are used to handle the missing outcome data. We considered four scenarios, with the missingness mechanism and baseline covariate effect on outcome either the same or different between intervention groups. We show that both unadjusted cluster-level analysis and baseline covariate adjusted cluster-level analysis give unbiased estimates of the intervention effect only if both intervention groups have the same missingness mechanisms and there is no interaction between baseline covariate and intervention group. Linear mixed model and multiple imputation give unbiased estimates under all four considered scenarios, provided that an interaction of intervention and baseline covariate is included in the model when appropriate. Cluster mean imputation has been proposed as a valid approach for handling missing outcomes in cluster randomised trials. We show that cluster mean imputation only gives unbiased estimates when missingness mechanism is the same between the intervention groups and there is no interaction between baseline covariate and intervention group. Multiple imputation shows overcoverage for small number of clusters in each intervention group.
Covariance matrices for use in criticality safety predictability studies
Derrien, H.; Larson, N.M.; Leal, L.C.
1997-09-01
Criticality predictability applications require as input the best available information on fissile and other nuclides. In recent years important work has been performed in the analysis of neutron transmission and cross-section data for fissile nuclei in the resonance region by using the computer code SAMMY. The code uses Bayes method (a form of generalized least squares) for sequential analyses of several sets of experimental data. Values for Reich-Moore resonance parameters, their covariances, and the derivatives with respect to the adjusted parameters (data sensitivities) are obtained. In general, the parameter file contains several thousand values and the dimension of the covariance matrices is correspondingly large. These matrices are not reported in the current evaluated data files due to their large dimensions and to the inadequacy of the file formats. The present work has two goals: the first is to calculate the covariances of group-averaged cross sections from the covariance files generated by SAMMY, because these can be more readily utilized in criticality predictability calculations. The second goal is to propose a more practical interface between SAMMY and the evaluated files. Examples are given for {sup 235}U in the popular 199- and 238-group structures, using the latest ORNL evaluation of the {sup 235}U resonance parameters.
Harry, H.H.
1988-03-11
Abstract and method for the adjustment and alignment of shafts in high power devices. A plurality of adjacent rotatable angled cylinders are positioned between a base and the shaft to be aligned which when rotated introduce an axial offset. The apparatus is electrically conductive and constructed of a structurally rigid material. The angled cylinders allow the shaft such as the center conductor in a pulse line machine to be offset in any desired alignment position within the range of the apparatus. 3 figs.
Harry, Herbert H.
1989-01-01
Apparatus and method for the adjustment and alignment of shafts in high power devices. A plurality of adjacent rotatable angled cylinders are positioned between a base and the shaft to be aligned which when rotated introduce an axial offset. The apparatus is electrically conductive and constructed of a structurally rigid material. The angled cylinders allow the shaft such as the center conductor in a pulse line machine to be offset in any desired alignment position within the range of the apparatus.
Robust estimation for partially linear models with large-dimensional covariates.
Zhu, LiPing; Li, RunZe; Cui, HengJian
2013-10-01
We are concerned with robust estimation procedures to estimate the parameters in partially linear models with large-dimensional covariates. To enhance the interpretability, we suggest implementing a noncon-cave regularization method in the robust estimation procedure to select important covariates from the linear component. We establish the consistency for both the linear and the nonlinear components when the covariate dimension diverges at the rate of [Formula: see text], where n is the sample size. We show that the robust estimate of linear component performs asymptotically as well as its oracle counterpart which assumes the baseline function and the unimportant covariates were known a priori. With a consistent estimator of the linear component, we estimate the nonparametric component by a robust local linear regression. It is proved that the robust estimate of nonlinear component performs asymptotically as well as if the linear component were known in advance. Comprehensive simulation studies are carried out and an application is presented to examine the finite-sample performance of the proposed procedures.
Covariance Evaluation Methodology for Neutron Cross Sections
Herman,M.; Arcilla, R.; Mattoon, C.M.; Mughabghab, S.F.; Oblozinsky, P.; Pigni, M.; Pritychenko, b.; Songzoni, A.A.
2008-09-01
We present the NNDC-BNL methodology for estimating neutron cross section covariances in thermal, resolved resonance, unresolved resonance and fast neutron regions. The three key elements of the methodology are Atlas of Neutron Resonances, nuclear reaction code EMPIRE, and the Bayesian code implementing Kalman filter concept. The covariance data processing, visualization and distribution capabilities are integral components of the NNDC methodology. We illustrate its application on examples including relatively detailed evaluation of covariances for two individual nuclei and massive production of simple covariance estimates for 307 materials. Certain peculiarities regarding evaluation of covariances for resolved resonances and the consistency between resonance parameter uncertainties and thermal cross section uncertainties are also discussed.
Non-parametric estimation of gap time survival functions for ordered multivariate failure time data.
Schaubel, Douglas E; Cai, Jianwen
2004-06-30
Times between sequentially ordered events (gap times) are often of interest in biomedical studies. For example, in a cancer study, the gap times from incidence-to-remission and remission-to-recurrence may be examined. Such data are usually subject to right censoring, and within-subject failure times are generally not independent. Statistical challenges in the analysis of the second and subsequent gap times include induced dependent censoring and non-identifiability of the marginal distributions. We propose a non-parametric method for constructing one-sample estimators of conditional gap-time specific survival functions. The estimators are uniformly consistent and, upon standardization, converge weakly to a zero-mean Gaussian process, with a covariance function which can be consistently estimated. Simulation studies reveal that the asymptotic approximations are appropriate for finite samples. Methods for confidence bands are provided. The proposed methods are illustrated on a renal failure data set, where the probabilities of transplant wait-listing and kidney transplantation are of interest.
Phase-covariant quantum benchmarks
Calsamiglia, J.; Aspachs, M.; Munoz-Tapia, R.; Bagan, E.
2009-05-15
We give a quantum benchmark for teleportation and quantum storage experiments suited for pure and mixed test states. The benchmark is based on the average fidelity over a family of phase-covariant states and certifies that an experiment cannot be emulated by a classical setup, i.e., by a measure-and-prepare scheme. We give an analytical solution for qubits, which shows important differences with standard state estimation approach, and compute the value of the benchmark for coherent and squeezed states, both pure and mixed.
Recent advances in nonparametric function estimation: Hydrologic applications
NASA Astrophysics Data System (ADS)
Lall, U.
1995-07-01
Nonparametric function estimation refers to methods that strive to approximate a target function locally, i.e., using data from a "small" neighborhood of the point of estimate. "Weak" assumptions, such as continuity of the target function and its differentiability to some order in the neighborhood, rather than an a priori assumption of the global form (e.g., linear or quadratic) of the entire target function are used. Traditionally, parametric assumptions (e.g., hydraulic conductivity is log normally distributed, floods follow a log Pearson III (LP3) distribution, annual stream flow is either log normal or gamma distributed, daily rainfall amounts are exponentially distributed, and the variograms of spatial hydrologic data follow a power law) have dominated statistical hydrologic estimation. Applications of nonparametric methods to some classical problems (frequency analysis, classification, spatial surface fitting, trend analysis, time series forecasting and simulation) of stochastic hydrology are reviewed.
Nonparametric probability density estimation by optimization theoretic techniques
NASA Technical Reports Server (NTRS)
Scott, D. W.
1976-01-01
Two nonparametric probability density estimators are considered. The first is the kernel estimator. The problem of choosing the kernel scaling factor based solely on a random sample is addressed. An interactive mode is discussed and an algorithm proposed to choose the scaling factor automatically. The second nonparametric probability estimate uses penalty function techniques with the maximum likelihood criterion. A discrete maximum penalized likelihood estimator is proposed and is shown to be consistent in the mean square error. A numerical implementation technique for the discrete solution is discussed and examples displayed. An extensive simulation study compares the integrated mean square error of the discrete and kernel estimators. The robustness of the discrete estimator is demonstrated graphically.
Nonparametric inference procedures for multistate life table analysis.
Dow, M M
1985-01-01
Recent generalizations of the classical single state life table procedures to the multistate case provide the means to analyze simultaneously the mobility and mortality experience of 1 or more cohorts. This paper examines fairly general nonparametric combinatorial matrix procedures, known as quadratic assignment, as an analysis technic of various transitional patterns commonly generated by cohorts over the life cycle course. To some degree, the output from a multistate life table analysis suggests inference procedures. In his discussion of multstate life table construction features, the author focuses on the matrix formulation of the problem. He then presents several examples of the proposed nonparametric procedures. Data for the mobility and life expectancies at birth matrices come from the 458 member Cayo Santiago rhesus monkey colony. The author's matrix combinatorial approach to hypotheses testing may prove to be a useful inferential strategy in several multidimensional demographic areas.
Nonparametric estimation of plant density by the distance method
Patil, S.A.; Burnham, K.P.; Kovner, J.L.
1979-01-01
A relation between the plant density and the probability density function of the nearest neighbor distance (squared) from a random point is established under fairly broad conditions. Based upon this relationship, a nonparametric estimator for the plant density is developed and presented in terms of order statistics. Consistency and asymptotic normality of the estimator are discussed. An interval estimator for the density is obtained. The modifications of this estimator and its variance are given when the distribution is truncated. Simulation results are presented for regular, random and aggregated populations to illustrate the nonparametric estimator and its variance. A numerical example from field data is given. Merits and deficiencies of the estimator are discussed with regard to its robustness and variance.
Nonparametric estimation of Fisher information from real data
NASA Astrophysics Data System (ADS)
Har-Shemesh, Omri; Quax, Rick; Miñano, Borja; Hoekstra, Alfons G.; Sloot, Peter M. A.
2016-02-01
The Fisher information matrix (FIM) is a widely used measure for applications including statistical inference, information geometry, experiment design, and the study of criticality in biological systems. The FIM is defined for a parametric family of probability distributions and its estimation from data follows one of two paths: either the distribution is assumed to be known and the parameters are estimated from the data or the parameters are known and the distribution is estimated from the data. We consider the latter case which is applicable, for example, to experiments where the parameters are controlled by the experimenter and a complicated relation exists between the input parameters and the resulting distribution of the data. Since we assume that the distribution is unknown, we use a nonparametric density estimation on the data and then compute the FIM directly from that estimate using a finite-difference approximation to estimate the derivatives in its definition. The accuracy of the estimate depends on both the method of nonparametric estimation and the difference Δ θ between the densities used in the finite-difference formula. We develop an approach for choosing the optimal parameter difference Δ θ based on large deviations theory and compare two nonparametric density estimation methods, the Gaussian kernel density estimator and a novel density estimation using field theory method. We also compare these two methods to a recently published approach that circumvents the need for density estimation by estimating a nonparametric f divergence and using it to approximate the FIM. We use the Fisher information of the normal distribution to validate our method and as a more involved example we compute the temperature component of the FIM in the two-dimensional Ising model and show that it obeys the expected relation to the heat capacity and therefore peaks at the phase transition at the correct critical temperature.
Parametric and nonparametric linkage analysis: A unified multipoint approach
Kruglyak, L.; Daly, M.J.; Reeve-Daly, M.P.; Lander, E.S.
1996-06-01
In complex disease studies, it is crucial to perform multipoint linkage analysis with many markers and to use robust nonparametric methods that take account of all pedigree information. Currently available methods fall short in both regards. In this paper, we describe how to extract complete multipoint inheritance information from general pedigrees of moderate size. This information is captured in the multipoint inheritance distribution, which provides a framework for a unified approach to both parametric and nonparametric methods of linkage analysis. Specifically, the approach includes the following: (1) Rapid exact computation of multipoint LOD scores involving dozens of highly polymorphic markers, even in the presence of loops and missing data. (2) Nonparametric linkage (NPL) analysis, a powerful new approach to pedigree analysis. We show that NPL is robust to uncertainty about mode of inheritance, is much more powerful than commonly used nonparametric methods, and loses little power relative to parametric linkage analysis. NPL thus appears to be the method of choice for pedigree studies of complex traits. (3) Information-content mapping, which measures the fraction of the total inheritance information extracted by the available marker data and points out the regions in which typing additional markers is most useful. (4) Maximum-likelihood reconstruction of many-marker haplotypes, even in pedigrees with missing data. We have implemented NPL analysis, LOD-score computation, information-content mapping, and haplotype reconstruction in a new computer package, GENEHUNTER. The package allows efficient multipoint analysis of pedigree data to be performed rapidly in a single user-friendly environment. 34 refs., 9 figs., 2 tabs.
Fusion of Hard and Soft Information in Nonparametric Density Estimation
2015-06-10
Fusion of Hard and Soft Information in Nonparametric Density Estimation∗ Johannes O. Royset Roger J-B Wets Department of Operations Research...univariate density estimation in situations when the sample ( hard information) is supplemented by “soft” information about the random phenomenon. These... hard and soft information, and give rates of convergence. Numerical examples illustrate the value of soft information, the ability to generate a
Optimum nonparametric estimation of population density based on ordered distances
Patil, S.A.; Kovner, J.L.; Burnham, Kenneth P.
1982-01-01
The asymptotic mean and error mean square are determined for the nonparametric estimator of plant density by distance sampling proposed by Patil, Burnham and Kovner (1979, Biometrics 35, 597-604. On the basis of these formulae, a bias-reduced version of this estimator is given, and its specific form is determined which gives minimum mean square error under varying assumptions about the true probability density function of the sampled data. Extension is given to line-transect sampling.
Nonparametric Analysis of Bivariate Gap Time with Competing Risks
Huang, Chiung-Yu; Wang, Chenguang; Wang, Mei-Cheng
2016-01-01
Summary This article considers nonparametric methods for studying recurrent disease and death with competing risks. We first point out that comparisons based on the well-known cumulative incidence function can be confounded by different prevalence rates of the competing events, and that comparisons of the conditional distribution of the survival time given the failure event type are more relevant for investigating the prognosis of different patterns of recurrence disease. We then propose nonparametric estimators for the conditional cumulative incidence function as well as the conditional bivariate cumulative incidence function for the bivariate gap times, that is, the time to disease recurrence and the residual lifetime after recurrence. To quantify the association between the two gap times in the competing risks setting, a modified Kendall’s tau statistic is proposed. The proposed estimators for the conditional bivariate cumulative incidence distribution and the association measure account for the induced dependent censoring for the second gap time. Uniform consistency and weak convergence of the proposed estimators are established. Hypothesis testing procedures for two-sample comparisons are discussed. Numerical simulation studies with practical sample sizes are conducted to evaluate the performance of the proposed nonparametric estimators and tests. An application to data from a pancreatic cancer study is presented to illustrate the methods developed in this article. PMID:26990686
Relativistic covariance of Ohm's law
NASA Astrophysics Data System (ADS)
Starke, R.; Schober, G. A. H.
2016-04-01
The derivation of Lorentz-covariant generalizations of Ohm's law has been a long-term issue in theoretical physics with deep implications for the study of relativistic effects in optical and atomic physics. In this article, we propose an alternative route to this problem, which is motivated by the tremendous progress in first-principles materials physics in general and ab initio electronic structure theory in particular. We start from the most general, Lorentz-covariant first-order response law, which is written in terms of the fundamental response tensor χμ ν relating induced four-currents to external four-potentials. By showing the equivalence of this description to Ohm's law, we prove the validity of Ohm's law in every inertial frame. We further use the universal relation between χμ ν and the microscopic conductivity tensor σkℓ to derive a fully relativistic transformation law for the latter, which includes all effects of anisotropy and relativistic retardation. In the special case of a constant, scalar conductivity, this transformation law can be used to rederive a standard textbook generalization of Ohm's law.
COVARIANCE ASSISTED SCREENING AND ESTIMATION.
Ke, By Tracy; Jin, Jiashun; Fan, Jianqing
2014-11-01
Consider a linear model Y = X β + z, where X = Xn,p and z ~ N(0, In ). The vector β is unknown and it is of interest to separate its nonzero coordinates from the zero ones (i.e., variable selection). Motivated by examples in long-memory time series (Fan and Yao, 2003) and the change-point problem (Bhattacharya, 1994), we are primarily interested in the case where the Gram matrix G = X'X is non-sparse but sparsifiable by a finite order linear filter. We focus on the regime where signals are both rare and weak so that successful variable selection is very challenging but is still possible. We approach this problem by a new procedure called the Covariance Assisted Screening and Estimation (CASE). CASE first uses a linear filtering to reduce the original setting to a new regression model where the corresponding Gram (covariance) matrix is sparse. The new covariance matrix induces a sparse graph, which guides us to conduct multivariate screening without visiting all the submodels. By interacting with the signal sparsity, the graph enables us to decompose the original problem into many separated small-size subproblems (if only we know where they are!). Linear filtering also induces a so-called problem of information leakage, which can be overcome by the newly introduced patching technique. Together, these give rise to CASE, which is a two-stage Screen and Clean (Fan and Song, 2010; Wasserman and Roeder, 2009) procedure, where we first identify candidates of these submodels by patching and screening, and then re-examine each candidate to remove false positives. For any procedure β̂ for variable selection, we measure the performance by the minimax Hamming distance between the sign vectors of β̂ and β. We show that in a broad class of situations where the Gram matrix is non-sparse but sparsifiable, CASE achieves the optimal rate of convergence. The results are successfully applied to long-memory time series and the change-point model.
COVARIANCE ASSISTED SCREENING AND ESTIMATION
Ke, By Tracy; Jin, Jiashun; Fan, Jianqing
2014-01-01
Consider a linear model Y = X β + z, where X = Xn,p and z ~ N(0, In). The vector β is unknown and it is of interest to separate its nonzero coordinates from the zero ones (i.e., variable selection). Motivated by examples in long-memory time series (Fan and Yao, 2003) and the change-point problem (Bhattacharya, 1994), we are primarily interested in the case where the Gram matrix G = X′X is non-sparse but sparsifiable by a finite order linear filter. We focus on the regime where signals are both rare and weak so that successful variable selection is very challenging but is still possible. We approach this problem by a new procedure called the Covariance Assisted Screening and Estimation (CASE). CASE first uses a linear filtering to reduce the original setting to a new regression model where the corresponding Gram (covariance) matrix is sparse. The new covariance matrix induces a sparse graph, which guides us to conduct multivariate screening without visiting all the submodels. By interacting with the signal sparsity, the graph enables us to decompose the original problem into many separated small-size subproblems (if only we know where they are!). Linear filtering also induces a so-called problem of information leakage, which can be overcome by the newly introduced patching technique. Together, these give rise to CASE, which is a two-stage Screen and Clean (Fan and Song, 2010; Wasserman and Roeder, 2009) procedure, where we first identify candidates of these submodels by patching and screening, and then re-examine each candidate to remove false positives. For any procedure β̂ for variable selection, we measure the performance by the minimax Hamming distance between the sign vectors of β̂ and β. We show that in a broad class of situations where the Gram matrix is non-sparse but sparsifiable, CASE achieves the optimal rate of convergence. The results are successfully applied to long-memory time series and the change-point model. PMID:25541567
Computation of transform domain covariance matrices
NASA Technical Reports Server (NTRS)
Fino, B. J.; Algazi, V. R.
1975-01-01
It is often of interest in applications to compute the covariance matrix of a random process transformed by a fast unitary transform. Here, the recursive definition of fast unitary transforms is used to derive recursive relations for the covariance matrices of the transformed process. These relations lead to fast methods of computation of covariance matrices and to substantial reductions of the number of arithmetic operations required.
Shrinkage approach for EEG covariance matrix estimation.
Beltrachini, Leandro; von Ellenrieder, Nicolas; Muravchik, Carlos H
2010-01-01
We present a shrinkage estimator for the EEG spatial covariance matrix of the background activity. We show that such an estimator has some advantages over the maximum likelihood and sample covariance estimators when the number of available data to carry out the estimation is low. We find sufficient conditions for the consistency of the shrinkage estimators and results concerning their numerical stability. We compare several shrinkage schemes and show how to improve the estimator by incorporating known structure of the covariance matrix.
Frailty models with missing covariates.
Herring, Amy H; Ibrahim, Joseph G; Lipsitz, Stuart R
2002-03-01
We present a method for estimating the parameters in random effects models for survival data when covariates are subject to missingness. Our method is more general than the usual frailty model as it accommodates a wide range of distributions for the random effects, which are included as an offset in the linear predictor in a manner analogous to that used in generalized linear mixed models. We propose using a Monte Carlo EM algorithm along with the Gibbs sampler to obtain parameter estimates. This method is useful in reducing the bias that may be incurred using complete-case methods in this setting. The methodology is applied to data from Eastern Cooperative Oncology Group melanoma clinical trials in which observations were believed to be clustered and several tumor characteristics were not always observed.
Non-parametric estimators of a monotonic dose-response curve and bootstrap confidence intervals.
Dilleen, Maria; Heimann, Günter; Hirsch, Ian
2003-03-30
In this paper we consider study designs which include a placebo and an active control group as well as several dose groups of a new drug. A monotonically increasing dose-response function is assumed, and the objective is to estimate a dose with equivalent response to the active control group, including a confidence interval for this dose. We present different non-parametric methods to estimate the monotonic dose-response curve. These are derived from the isotonic regression estimator, a non-negative least squares estimator, and a bias adjusted non-negative least squares estimator using linear interpolation. The different confidence intervals are based upon an approach described by Korn, and upon two different bootstrap approaches. One of these bootstrap approaches is standard, and the second ensures that resampling is done from empiric distributions which comply with the order restrictions imposed. In our simulations we did not find any differences between the two bootstrap methods, and both clearly outperform Korn's confidence intervals. The non-negative least squares estimator yields biased results for moderate sample sizes. The bias adjustment for this estimator works well, even for small and moderate sample sizes, and surprisingly outperforms the isotonic regression method in certain situations.
Lorentz covariant {kappa}-Minkowski spacetime
DaPbrowski, Ludwik; Godlinski, Michal; Piacitelli, Gherardo
2010-06-15
In recent years, different views on the interpretation of Lorentz covariance of noncommuting coordinates have been discussed. By a general procedure, we construct the minimal canonical central covariantization of the {kappa}-Minkowski spacetime. Here, undeformed Lorentz covariance is implemented by unitary operators, in the presence of two dimensionful parameters. We then show that, though the usual {kappa}-Minkowski spacetime is covariant under deformed (or twisted) Lorentz action, the resulting framework is equivalent to taking a noncovariant restriction of the covariantized model. We conclude with some general comments on the approach of deformed covariance.
Balancing continuous covariates based on Kernel densities.
Ma, Zhenjun; Hu, Feifang
2013-03-01
The balance of important baseline covariates is essential for convincing treatment comparisons. Stratified permuted block design and minimization are the two most commonly used balancing strategies, both of which require the covariates to be discrete. Continuous covariates are typically discretized in order to be included in the randomization scheme. But breakdown of continuous covariates into subcategories often changes the nature of the covariates and makes distributional balance unattainable. In this article, we propose to balance continuous covariates based on Kernel density estimations, which keeps the continuity of the covariates. Simulation studies show that the proposed Kernel-Minimization can achieve distributional balance of both continuous and categorical covariates, while also keeping the group size well balanced. It is also shown that the Kernel-Minimization is less predictable than stratified permuted block design and minimization. Finally, we apply the proposed method to redesign the NINDS trial, which has been a source of controversy due to imbalance of continuous baseline covariates. Simulation shows that imbalances such as those observed in the NINDS trial can be generally avoided through the implementation of the new method.
Li, L; Kleinman, K; Gillman, M W
2014-12-01
We implemented six confounding adjustment methods: (1) covariate-adjusted regression, (2) propensity score (PS) regression, (3) PS stratification, (4) PS matching with two calipers, (5) inverse probability weighting and (6) doubly robust estimation to examine the associations between the body mass index (BMI) z-score at 3 years and two separate dichotomous exposure measures: exclusive breastfeeding v. formula only (n=437) and cesarean section v. vaginal delivery (n=1236). Data were drawn from a prospective pre-birth cohort study, Project Viva. The goal is to demonstrate the necessity and usefulness, and approaches for multiple confounding adjustment methods to analyze observational data. Unadjusted (univariate) and covariate-adjusted linear regression associations of breastfeeding with BMI z-score were -0.33 (95% CI -0.53, -0.13) and -0.24 (-0.46, -0.02), respectively. The other approaches resulted in smaller n (204-276) because of poor overlap of covariates, but CIs were of similar width except for inverse probability weighting (75% wider) and PS matching with a wider caliper (76% wider). Point estimates ranged widely, however, from -0.01 to -0.38. For cesarean section, because of better covariate overlap, the covariate-adjusted regression estimate (0.20) was remarkably robust to all adjustment methods, and the widths of the 95% CIs differed less than in the breastfeeding example. Choice of covariate adjustment method can matter. Lack of overlap in covariate structure between exposed and unexposed participants in observational studies can lead to erroneous covariate-adjusted estimates and confidence intervals. We recommend inspecting covariate overlap and using multiple confounding adjustment methods. Similar results bring reassurance. Contradictory results suggest issues with either the data or the analytic method.
Kleinman, Ken; Gillman, Matthew W.
2014-01-01
We implemented 6 confounding adjustment methods: 1) covariate-adjusted regression, 2) propensity score (PS) regression, 3) PS stratification, 4) PS matching with two calipers, 5) inverse-probability-weighting, and 6) doubly-robust estimation to examine the associations between the BMI z-score at 3 years and two separate dichotomous exposure measures: exclusive breastfeeding versus formula only (N = 437) and cesarean section versus vaginal delivery (N = 1236). Data were drawn from a prospective pre-birth cohort study, Project Viva. The goal is to demonstrate the necessity and usefulness, and approaches for multiple confounding adjustment methods to analyze observational data. Unadjusted (univariate) and covariate-adjusted linear regression associations of breastfeeding with BMI z-score were −0.33 (95% CI −0.53, −0.13) and −0.24 (−0.46, −0.02), respectively. The other approaches resulted in smaller N (204 to 276) because of poor overlap of covariates, but CIs were of similar width except for inverse-probability-weighting (75% wider) and PS matching with a wider caliper (76% wider). Point estimates ranged widely, however, from −0.01 to −0.38. For cesarean section, because of better covariate overlap, the covariate-adjusted regression estimate (0.20) was remarkably robust to all adjustment methods, and the widths of the 95% CIs differed less than in the breastfeeding example. Choice of covariate adjustment method can matter. Lack of overlap in covariate structure between exposed and unexposed participants in observational studies can lead to erroneous covariate-adjusted estimates and confidence intervals. We recommend inspecting covariate overlap and using multiple confounding adjustment methods. Similar results bring reassurance. Contradictory results suggest issues with either the data or the analytic method. PMID:25171142
Generalized covariance analysis for partially autonomous deep space missions
NASA Technical Reports Server (NTRS)
Boone, Jack N.
1991-01-01
A new covariance analysis method is presented that is suitable for the evaluation of multiple impulsive controllers acting on some stochastic process x. The method accommodates batch and sequential estimators with equal ease and accounts for time-delay effects in a natural manner. The formalism is developed in terms of a generalized state vector that is formed from the system state vector x, augmented by various fixed epoch estimates, and a data vector formed from discrete time observations of the system. Recursions are developed for time transition, measurement incorporation, and impulsive control updating of the generalized covariance matrix. Means of limiting the dimensional growth of the generalized state vector via the processes of estimator epoch adjustment and measurement vector deflation are described and the application of numerically stable matrix factorization methods to the generalized covariance recursions is outlined. The method is applied to the Magellan spacecraft to demonstrate the capability of ground-based optimal estimation and control of gyro/star scanner misalignment.
Non-parametric morphologies of mergers in the Illustris simulation
NASA Astrophysics Data System (ADS)
Bignone, L. A.; Tissera, P. B.; Sillero, E.; Pedrosa, S. E.; Pellizza, L. J.; Lambas, D. G.
2017-02-01
We study non-parametric morphologies of mergers events in a cosmological context, using the Illustris project. We produce mock g-band images comparable to observational surveys from the publicly available Illustris simulation idealized mock images at z = 0. We then measure non-parametric indicators: asymmetry, Gini, M20, clumpiness, and concentration for a set of galaxies with M* > 1010 M⊙. We correlate these automatic statistics with the recent merger history of galaxies and with the presence of close companions. Our main contribution is to assess in a cosmological framework, the empirically derived non-parametric demarcation line and average time-scales used to determine the merger rate observationally. We found that 98 per cent of galaxies above the demarcation line have a close companion or have experienced a recent merger event. On average, merger signatures obtained from the G-M20 criterion anti-correlate clearly with the elapsing time to the last merger event. We also find that the asymmetry correlates with galaxy pair separation and relative velocity, exhibiting the larger enhancements for those systems with pair separations d < 50 h-1 kpc and relative velocities V < 350 km s-1. We find that the G-M20 is most sensitive to recent mergers (∼0.14 Gyr) and to ongoing mergers with stellar mass ratios greater than 0.1. For this indicator, we compute a merger average observability time-scale of ∼0.2 Gyr, in agreement with previous results and demonstrate that the morphologically derived merger rate recovers the intrinsic total merger rate of the simulation and the merger rate as a function of stellar mass.
Nonparametric maximum likelihood estimation for the multisample Wicksell corpuscle problem
Chan, Kwun Chuen Gary; Qin, Jing
2016-01-01
We study nonparametric maximum likelihood estimation for the distribution of spherical radii using samples containing a mixture of one-dimensional, two-dimensional biased and three-dimensional unbiased observations. Since direct maximization of the likelihood function is intractable, we propose an expectation-maximization algorithm for implementing the estimator, which handles an indirect measurement problem and a sampling bias problem separately in the E- and M-steps, and circumvents the need to solve an Abel-type integral equation, which creates numerical instability in the one-sample problem. Extensions to ellipsoids are studied and connections to multiplicative censoring are discussed. PMID:27279657
Computation of nonparametric convex hazard estimators via profile methods
Jankowski, Hanna K.; Wellner, Jon A.
2010-01-01
This paper proposes a profile likelihood algorithm to compute the nonparametric maximum likelihood estimator of a convex hazard function. The maximisation is performed in two steps: First the support reduction algorithm is used to maximise the likelihood over all hazard functions with a given point of minimum (or antimode). Then it is shown that the profile (or partially maximised) likelihood is quasi-concave as a function of the antimode, so that a bisection algorithm can be applied to find the maximum of the profile likelihood, and hence also the global maximum. The new algorithm is illustrated using both artificial and real data, including lifetime data for Canadian males and females. PMID:20300560
Mixed LICORS: A Nonparametric Algorithm for Predictive State Reconstruction
Goerg, Georg M.; Shalizi, Cosma Rohilla
2015-01-01
We introduce mixed LICORS, an algorithm for learning nonlinear, high-dimensional dynamics from spatio-temporal data, suitable for both prediction and simulation. Mixed LICORS extends the recent LICORS algorithm (Goerg and Shalizi, 2012) from hard clustering of predictive distributions to a non-parametric, EM-like soft clustering. This retains the asymptotic predictive optimality of LICORS, but, as we show in simulations, greatly improves out-of-sample forecasts with limited data. The new method is implemented in the publicly-available R package LICORS. PMID:26279743
Empirically Estimable Classification Bounds Based on a Nonparametric Divergence Measure
Berisha, Visar; Wisler, Alan; Hero, Alfred O.; Spanias, Andreas
2015-01-01
Information divergence functions play a critical role in statistics and information theory. In this paper we show that a non-parametric f-divergence measure can be used to provide improved bounds on the minimum binary classification probability of error for the case when the training and test data are drawn from the same distribution and for the case where there exists some mismatch between training and test distributions. We confirm the theoretical results by designing feature selection algorithms using the criteria from these bounds and by evaluating the algorithms on a series of pathological speech classification tasks. PMID:26807014
Covariance Structure Analysis of Ordinal Ipsative Data.
ERIC Educational Resources Information Center
Chan, Wai; Bentler, Peter M.
1998-01-01
Proposes a two-stage estimation method for the analysis of covariance structure models with ordinal ipsative data (OID). A goodness-of-fit statistic is given for testing the hypothesized covariance structure matrix, and simulation results show that the method works well with a large sample. (SLD)
Quality Quantification of Evaluated Cross Section Covariances
Varet, S.; Dossantos-Uzarralde, P.
2015-01-15
Presently, several methods are used to estimate the covariance matrix of evaluated nuclear cross sections. Because the resulting covariance matrices can be different according to the method used and according to the assumptions of the method, we propose a general and objective approach to quantify the quality of the covariance estimation for evaluated cross sections. The first step consists in defining an objective criterion. The second step is computation of the criterion. In this paper the Kullback-Leibler distance is proposed for the quality quantification of a covariance matrix estimation and its inverse. It is based on the distance to the true covariance matrix. A method based on the bootstrap is presented for the estimation of this criterion, which can be applied with most methods for covariance matrix estimation and without the knowledge of the true covariance matrix. The full approach is illustrated on the {sup 85}Rb nucleus evaluations and the results are then used for a discussion on scoring and Monte Carlo approaches for covariance matrix estimation of the cross section evaluations.
Group Theory of Covariant Harmonic Oscillators
ERIC Educational Resources Information Center
Kim, Y. S.; Noz, Marilyn E.
1978-01-01
A simple and concrete example for illustrating the properties of noncompact groups is presented. The example is based on the covariant harmonic-oscillator formalism in which the relativistic wave functions carry a covariant-probability interpretation. This can be used in a group theory course for graduate students who have some background in…
Position Error Covariance Matrix Validation and Correction
NASA Technical Reports Server (NTRS)
Frisbee, Joe, Jr.
2016-01-01
In order to calculate operationally accurate collision probabilities, the position error covariance matrices predicted at times of closest approach must be sufficiently accurate representations of the position uncertainties. This presentation will discuss why the Gaussian distribution is a reasonable expectation for the position uncertainty and how this assumed distribution type is used in the validation and correction of position error covariance matrices.
Adjoints and Low-rank Covariance Representation
NASA Technical Reports Server (NTRS)
Tippett, Michael K.; Cohn, Stephen E.
2000-01-01
Quantitative measures of the uncertainty of Earth System estimates can be as important as the estimates themselves. Second moments of estimation errors are described by the covariance matrix, whose direct calculation is impractical when the number of degrees of freedom of the system state is large. Ensemble and reduced-state approaches to prediction and data assimilation replace full estimation error covariance matrices by low-rank approximations. The appropriateness of such approximations depends on the spectrum of the full error covariance matrix, whose calculation is also often impractical. Here we examine the situation where the error covariance is a linear transformation of a forcing error covariance. We use operator norms and adjoints to relate the appropriateness of low-rank representations to the conditioning of this transformation. The analysis is used to investigate low-rank representations of the steady-state response to random forcing of an idealized discrete-time dynamical system.
Johnson, H.O.; Gupta, S.C.; Vecchia, A.V.; Zvomuya, F.
2009-01-01
Excessive loading of sediment and nutrients to rivers is a major problem in many parts of the United States. In this study, we tested the non-parametric Seasonal Kendall (SEAKEN) trend model and the parametric USGS Quality of Water trend program (QWTREND) to quantify trends in water quality of the Minnesota River at Fort Snelling from 1976 to 2003. Both methods indicated decreasing trends in flow-adjusted concentrations of total suspended solids (TSS), total phosphorus (TP), and orthophosphorus (OP) and a generally increasing trend in flow-adjusted nitrate plus nitrite-nitrogen (NO3-N) concentration. The SEAKEN results were strongly influenced by the length of the record as well as extreme years (dry or wet) earlier in the record. The QWTREND results, though influenced somewhat by the same factors, were more stable. The magnitudes of trends between the two methods were somewhat different and appeared to be associated with conceptual differences between the flow-adjustment processes used and with data processing methods. The decreasing trends in TSS, TP, and OP concentrations are likely related to conservation measures implemented in the basin. However, dilution effects from wet climate or additional tile drainage cannot be ruled out. The increasing trend in NO3-N concentrations was likely due to increased drainage in the basin. Since the Minnesota River is the main source of sediments to the Mississippi River, this study also addressed the rapid filling of Lake Pepin on the Mississippi River and found the likely cause to be increased flow due to recent wet climate in the region. Copyright ?? 2009 by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America. All rights reserved.
Petersen estimator, Chapman adjustment, list effects, and heterogeneity.
Mao, Chang Xuan; Huang, Ruochen; Zhang, Sijia
2017-03-01
We use a nonparametric mixture model for the purpose of estimating the size of a population from multiple lists in which both the individual effects and list effects are allowed to vary. We propose a lower bound of the population size that admits an analytic expression. The lower bound can be estimated without the necessity of model-fitting. The asymptotical normality of the estimator is established. Both the estimator itself and that for the estimable bound of its variance are adjusted. These adjusted versions are shown to be unbiased in the limit. Simulation experiments are performed to assess the proposed approach and real applications are studied.
Stochastic Earthquake Rupture Modeling Using Nonparametric Co-Regionalization
NASA Astrophysics Data System (ADS)
Lee, Kyungbook; Song, Seok Goo
2016-10-01
Accurate predictions of the intensity and variability of ground motions are essential in simulation-based seismic hazard assessment. Advanced simulation-based ground motion prediction methods have been proposed to complement the empirical approach, which suffers from the lack of observed ground motion data, especially in the near-source region for large events. It is important to quantify the variability of the earthquake rupture process for future events and to produce a number of rupture scenario models to capture the variability in simulation-based ground motion predictions. In this study, we improved the previously developed stochastic earthquake rupture modeling method by applying the nonparametric co-regionalization, which was proposed in geostatistics, to the correlation models estimated from dynamically derived earthquake rupture models. The nonparametric approach adopted in this study is computationally efficient and, therefore, enables us to simulate numerous rupture scenarios, including large events (M > 7.0). It also gives us an opportunity to check the shape of true input correlation models in stochastic modeling after being deformed for permissibility. We expect that this type of modeling will improve our ability to simulate a wide range of rupture scenario models and thereby predict ground motions and perform seismic hazard assessment more accurately.
A High-Dimensional Nonparametric Multivariate Test for Mean Vector
Wang, Lan; Peng, Bo; Li, Runze
2015-01-01
This work is concerned with testing the population mean vector of nonnormal high-dimensional multivariate data. Several tests for high-dimensional mean vector, based on modifying the classical Hotelling T2 test, have been proposed in the literature. Despite their usefulness, they tend to have unsatisfactory power performance for heavy-tailed multivariate data, which frequently arise in genomics and quantitative finance. This paper proposes a novel high-dimensional nonparametric test for the population mean vector for a general class of multivariate distributions. With the aid of new tools in modern probability theory, we proved that the limiting null distribution of the proposed test is normal under mild conditions when p is substantially larger than n. We further study the local power of the proposed test and compare its relative efficiency with a modified Hotelling T2 test for high-dimensional data. An interesting finding is that the newly proposed test can have even more substantial power gain with large p than the traditional nonparametric multivariate test does with finite fixed p. We study the finite sample performance of the proposed test via Monte Carlo simulations. We further illustrate its application by an empirical analysis of a genomics data set. PMID:26848205
A comparative study of nonparametric methods for pattern recognition
NASA Technical Reports Server (NTRS)
Hahn, S. F.; Nelson, G. D.
1972-01-01
The applied research discussed in this report determines and compares the correct classification percentage of the nonparametric sign test, Wilcoxon's signed rank test, and K-class classifier with the performance of the Bayes classifier. The performance is determined for data which have Gaussian, Laplacian and Rayleigh probability density functions. The correct classification percentage is shown graphically for differences in modes and/or means of the probability density functions for four, eight and sixteen samples. The K-class classifier performed very well with respect to the other classifiers used. Since the K-class classifier is a nonparametric technique, it usually performed better than the Bayes classifier which assumes the data to be Gaussian even though it may not be. The K-class classifier has the advantage over the Bayes in that it works well with non-Gaussian data without having to determine the probability density function of the data. It should be noted that the data in this experiment was always unimodal.
Bayesian nonparametric centered random effects models with variable selection.
Yang, Mingan
2013-03-01
In a linear mixed effects model, it is common practice to assume that the random effects follow a parametric distribution such as a normal distribution with mean zero. However, in the case of variable selection, substantial violation of the normality assumption can potentially impact the subset selection and result in poor interpretation and even incorrect results. In nonparametric random effects models, the random effects generally have a nonzero mean, which causes an identifiability problem for the fixed effects that are paired with the random effects. In this article, we focus on a Bayesian method for variable selection. We characterize the subject-specific random effects nonparametrically with a Dirichlet process and resolve the bias simultaneously. In particular, we propose flexible modeling of the conditional distribution of the random effects with changes across the predictor space. The approach is implemented using a stochastic search Gibbs sampler to identify subsets of fixed effects and random effects to be included in the model. Simulations are provided to evaluate and compare the performance of our approach to the existing ones. We then apply the new approach to a real data example, cross-country and interlaboratory rodent uterotrophic bioassay.
Combined Use of Integral Experiments and Covariance Data
NASA Astrophysics Data System (ADS)
Palmiotti, G.; Salvatores, M.; Aliberti, G.; Herman, M.; Hoblit, S. D.; McKnight, R. D.; Obložinský, P.; Talou, P.; Hale, G. M.; Hiruta, H.; Kawano, T.; Mattoon, C. M.; Nobre, G. P. A.; Palumbo, A.; Pigni, M.; Rising, M. E.; Yang, W.-S.; Kahler, A. C.
2014-04-01
In the frame of a US-DOE sponsored project, ANL, BNL, INL and LANL have performed a joint multidisciplinary research activity in order to explore the combined use of integral experiments and covariance data with the objective to both give quantitative indications on possible improvements of the ENDF evaluated data files and to reduce at the same time crucial reactor design parameter uncertainties. Methods that have been developed in the last four decades for the purposes indicated above have been improved by some new developments that benefited also by continuous exchanges with international groups working in similar areas. The major new developments that allowed significant progress are to be found in several specific domains: a) new science-based covariance data; b) integral experiment covariance data assessment and improved experiment analysis, e.g., of sample irradiation experiments; c) sensitivity analysis, where several improvements were necessary despite the generally good understanding of these techniques, e.g., to account for fission spectrum sensitivity; d) a critical approach to the analysis of statistical adjustments performance, both a priori and a posteriori; e) generalization of the assimilation method, now applied for the first time not only to multigroup cross sections data but also to nuclear model parameters (the "consistent" method). This article describes the major results obtained in each of these areas; a large scale nuclear data adjustment, based on the use of approximately one hundred high-accuracy integral experiments, will be reported along with a significant example of the application of the new "consistent" method of data assimilation.
Sparse estimation of a covariance matrix.
Bien, Jacob; Tibshirani, Robert J
2011-12-01
We suggest a method for estimating a covariance matrix on the basis of a sample of vectors drawn from a multivariate normal distribution. In particular, we penalize the likelihood with a lasso penalty on the entries of the covariance matrix. This penalty plays two important roles: it reduces the effective number of parameters, which is important even when the dimension of the vectors is smaller than the sample size since the number of parameters grows quadratically in the number of variables, and it produces an estimate which is sparse. In contrast to sparse inverse covariance estimation, our method's close relative, the sparsity attained here is in the covariance matrix itself rather than in the inverse matrix. Zeros in the covariance matrix correspond to marginal independencies; thus, our method performs model selection while providing a positive definite estimate of the covariance. The proposed penalized maximum likelihood problem is not convex, so we use a majorize-minimize approach in which we iteratively solve convex approximations to the original nonconvex problem. We discuss tuning parameter selection and demonstrate on a flow-cytometry dataset how our method produces an interpretable graphical display of the relationship between variables. We perform simulations that suggest that simple elementwise thresholding of the empirical covariance matrix is competitive with our method for identifying the sparsity structure. Additionally, we show how our method can be used to solve a previously studied special case in which a desired sparsity pattern is prespecified.
Concordance between criteria for covariate model building.
Hennig, Stefanie; Karlsson, Mats O
2014-04-01
When performing a population pharmacokinetic modelling analysis covariates are often added to the model. Such additions are often justified by improved goodness of fit and/or decreased in unexplained (random) parameter variability. Increased goodness of fit is most commonly measured by the decrease in the objective function value. Parameter variability can be defined as the sum of unexplained (random) and explained (predictable) variability. Increase in magnitude of explained parameter variability could be another possible criterion for judging improvement in the model. The agreement between these three criteria in diagnosing covariate-parameter relationships of different strengths and nature using stochastic simulations and estimations as well as assessing covariate-parameter relationships in four previously published real data examples were explored. Total estimated parameter variability was found to vary with the number of covariates introduced on the parameter. In the simulated examples and two real examples, the parameter variability increased with increasing number of included covariates. For the other real examples parameter variability decreased or did not change systematically with the addition of covariates. The three criteria were highly correlated, with the decrease in unexplained variability being more closely associated with changes in objective function values than increases in explained parameter variability were. The often used assumption that inclusion of covariates in models only shifts unexplained parameter variability to explained parameter variability appears not to be true, which may have implications for modelling decisions.
Karabatsos, George
2017-02-01
Most of applied statistics involves regression analysis of data. In practice, it is important to specify a regression model that has minimal assumptions which are not violated by data, to ensure that statistical inferences from the model are informative and not misleading. This paper presents a stand-alone and menu-driven software package, Bayesian Regression: Nonparametric and Parametric Models, constructed from MATLAB Compiler. Currently, this package gives the user a choice from 83 Bayesian models for data analysis. They include 47 Bayesian nonparametric (BNP) infinite-mixture regression models; 5 BNP infinite-mixture models for density estimation; and 31 normal random effects models (HLMs), including normal linear models. Each of the 78 regression models handles either a continuous, binary, or ordinal dependent variable, and can handle multi-level (grouped) data. All 83 Bayesian models can handle the analysis of weighted observations (e.g., for meta-analysis), and the analysis of left-censored, right-censored, and/or interval-censored data. Each BNP infinite-mixture model has a mixture distribution assigned one of various BNP prior distributions, including priors defined by either the Dirichlet process, Pitman-Yor process (including the normalized stable process), beta (two-parameter) process, normalized inverse-Gaussian process, geometric weights prior, dependent Dirichlet process, or the dependent infinite-probits prior. The software user can mouse-click to select a Bayesian model and perform data analysis via Markov chain Monte Carlo (MCMC) sampling. After the sampling completes, the software automatically opens text output that reports MCMC-based estimates of the model's posterior distribution and model predictive fit to the data. Additional text and/or graphical output can be generated by mouse-clicking other menu options. This includes output of MCMC convergence analyses, and estimates of the model's posterior predictive distribution, for selected
Confidence interval of difference of proportions in logistic regression in presence of covariates.
Reeve, Russell
2016-03-16
Comparison of treatment differences in incidence rates is an important objective of many clinical trials. However, often the proportion is affected by covariates, and the adjustment of the predicted proportion is made using logistic regression. It is desirable to estimate the treatment differences in proportions adjusting for the covariates, similarly to the comparison of adjusted means in analysis of variance. Because of the correlation between the point estimates in the different treatment groups, the standard methods for constructing confidence intervals are inadequate. The problem is more difficult in the binary case, as the comparison is not uniquely defined, and the sampling distribution more difficult to analyze. Four procedures for analyzing the data are presented, which expand upon existing methods and generalize the link function. It is shown that, among the four methods studied, the resampling method based on the exact distribution function yields a coverage rate closest to the nominal.
ERIC Educational Resources Information Center
Lee, Young-Sun; Wollack, James A.; Douglas, Jeffrey
2009-01-01
The purpose of this study was to assess the model fit of a 2PL through comparison with the nonparametric item characteristic curve (ICC) estimation procedures. Results indicate that three nonparametric procedures implemented produced ICCs that are similar to that of the 2PL for items simulated to fit the 2PL. However for misfitting items,…
Out-of-Sample Extensions for Non-Parametric Kernel Methods.
Pan, Binbin; Chen, Wen-Sheng; Chen, Bo; Xu, Chen; Lai, Jianhuang
2017-02-01
Choosing suitable kernels plays an important role in the performance of kernel methods. Recently, a number of studies were devoted to developing nonparametric kernels. Without assuming any parametric form of the target kernel, nonparametric kernel learning offers a flexible scheme to utilize the information of the data, which may potentially characterize the data similarity better. The kernel methods using nonparametric kernels are referred to as nonparametric kernel methods. However, many nonparametric kernel methods are restricted to transductive learning, where the prediction function is defined only over the data points given beforehand. They have no straightforward extension for the out-of-sample data points, and thus cannot be applied to inductive learning. In this paper, we show how to make the nonparametric kernel methods applicable to inductive learning. The key problem of out-of-sample extension is how to extend the nonparametric kernel matrix to the corresponding kernel function. A regression approach in the hyper reproducing kernel Hilbert space is proposed to solve this problem. Empirical results indicate that the out-of-sample performance is comparable to the in-sample performance in most cases. Experiments on face recognition demonstrate the superiority of our nonparametric kernel method over the state-of-the-art parametric kernel methods.
Non-parametric three-way mixed ANOVA with aligned rank tests.
Oliver-Rodríguez, Juan C; Wang, X T
2015-02-01
Research problems that require a non-parametric analysis of multifactor designs with repeated measures arise in the behavioural sciences. There is, however, a lack of available procedures in commonly used statistical packages. In the present study, a generalization of the aligned rank test for the two-way interaction is proposed for the analysis of the typical sources of variation in a three-way analysis of variance (ANOVA) with repeated measures. It can be implemented in the usual statistical packages. Its statistical properties are tested by using simulation methods with two sample sizes (n = 30 and n = 10) and three distributions (normal, exponential and double exponential). Results indicate substantial increases in power for non-normal distributions in comparison with the usual parametric tests. Similar levels of Type I error for both parametric and aligned rank ANOVA were obtained with non-normal distributions and large sample sizes. Degrees-of-freedom adjustments for Type I error control in small samples are proposed. The procedure is applied to a case study with 30 participants per group where it detects gender differences in linguistic abilities in blind children not shown previously by other methods.
A nonparametric vs. latent class model of general practitioner utilization: evidence from Canada.
McLeod, Logan
2011-12-01
Predicting health care utilization is the foundation of many health economics analyses, such as calculating risk-adjustment capitation payments or measuring equity in health care utilization. The most common econometric models of physician utilization are parametric count data models, since the most common metric of physician utilization is the number of physician visits. This paper makes two distinct contributions to the literature analyzing GP utilization: (i) it is the first to use a nonparametric kernel conditional density estimator to model GP utilization and compare the predicted utilization with that from a latent class negative binomial model; and (ii) it uses panel data to control for the potential endogeneity between self-reported health status and the number of GP visits. The goodness-of-fit results show the kernel conditional density estimator provides a better fit to the observed distribution of GP visits than the latent class negative binomial model. There are some meaningful differences in how the predicted conditional mean number of GP visits changes with a change in an individual's characteristics, called the incremental effect (IE), between the kernel conditional density estimator and the latent class negative binomial model. The most notable differences are observed in the right tail of the distribution where the IEs from the latent class negative binomial model are up to 190 times the magnitude of the IEs from the kernel conditional density estimator.
Covariance Spectroscopy for Fissile Material Detection
Rusty Trainham, Jim Tinsley, Paul Hurley, Ray Keegan
2009-06-02
Nuclear fission produces multiple prompt neutrons and gammas at each fission event. The resulting daughter nuclei continue to emit delayed radiation as neutrons boil off, beta decay occurs, etc. All of the radiations are causally connected, and therefore correlated. The correlations are generally positive, but when different decay channels compete, so that some radiations tend to exclude others, negative correlations could also be observed. A similar problem of reduced complexity is that of cascades radiation, whereby a simple radioactive decay produces two or more correlated gamma rays at each decay. Covariance is the usual means for measuring correlation, and techniques of covariance mapping may be useful to produce distinct signatures of special nuclear materials (SNM). A covariance measurement can also be used to filter data streams because uncorrelated signals are largely rejected. The technique is generally more effective than a coincidence measurement. In this poster, we concentrate on cascades and the covariance filtering problem.
Using Incidence Sampling to Estimate Covariances.
ERIC Educational Resources Information Center
Knapp, Thomas R.
1979-01-01
This paper presents the generalized symmetric means approach to the estimation of population covariances, complete with derivations and examples. Particular attention is paid to the problem of missing data, which is handled very naturally in the incidence sampling framework. (CTM)
Covariation bias in panic-prone individuals.
Pauli, P; Montoya, P; Martz, G E
1996-11-01
Covariation estimates between fear-relevant (FR; emergency situations) or fear-irrelevant (FI; mushrooms and nudes) stimuli and an aversive outcome (electrical shock) were examined in 10 high-fear (panic-prone) and 10 low-fear respondents. When the relation between slide category and outcome was random (illusory correlation), only high-fear participants markedly overestimated the contingency between FR slides and shocks. However, when there was a high contingency of shocks following FR stimuli (83%) and a low contingency of shocks following FI stimuli (17%), the group difference vanished. Reversal of contingencies back to random induced a covariation bias for FR slides in high- and low-fear respondents. Results indicate that panic-prone respondents show a covariation bias for FR stimuli and that the experience of a high contingency between FR slides and aversive outcomes may foster such a covariation bias even in low-fear respondents.
Conformally covariant parametrizations for relativistic initial data
NASA Astrophysics Data System (ADS)
Delay, Erwann
2017-01-01
We revisit the Lichnerowicz-York method, and an alternative method of York, in order to obtain some conformally covariant systems. This type of parametrization is certainly more natural for non constant mean curvature initial data.
Nonparametric autocovariance estimation from censored time series by Gaussian imputation.
Park, Jung Wook; Genton, Marc G; Ghosh, Sujit K
2009-02-01
One of the most frequently used methods to model the autocovariance function of a second-order stationary time series is to use the parametric framework of autoregressive and moving average models developed by Box and Jenkins. However, such parametric models, though very flexible, may not always be adequate to model autocovariance functions with sharp changes. Furthermore, if the data do not follow the parametric model and are censored at a certain value, the estimation results may not be reliable. We develop a Gaussian imputation method to estimate an autocovariance structure via nonparametric estimation of the autocovariance function in order to address both censoring and incorrect model specification. We demonstrate the effectiveness of the technique in terms of bias and efficiency with simulations under various rates of censoring and underlying models. We describe its application to a time series of silicon concentrations in the Arctic.
Nonparametric supervised learning by linear interpolation with maximum entropy.
Gupta, Maya R; Gray, Robert M; Olshen, Richard A
2006-05-01
Nonparametric neighborhood methods for learning entail estimation of class conditional probabilities based on relative frequencies of samples that are "near-neighbors" of a test point. We propose and explore the behavior of a learning algorithm that uses linear interpolation and the principle of maximum entropy (LIME). We consider some theoretical properties of the LIME algorithm: LIME weights have exponential form; the estimates are consistent; and the estimates are robust to additive noise. In relation to bias reduction, we show that near-neighbors contain a test point in their convex hull asymptotically. The common linear interpolation solution used for regression on grids or look-up-tables is shown to solve a related maximum entropy problem. LIME simulation results support use of the method, and performance on a pipeline integrity classification problem demonstrates that the proposed algorithm has practical value.
Nonparametric Model of Smooth Muscle Force Production During Electrical Stimulation.
Cole, Marc; Eikenberry, Steffen; Kato, Takahide; Sandler, Roman A; Yamashiro, Stanley M; Marmarelis, Vasilis Z
2017-03-01
A nonparametric model of smooth muscle tension response to electrical stimulation was estimated using the Laguerre expansion technique of nonlinear system kernel estimation. The experimental data consisted of force responses of smooth muscle to energy-matched alternating single pulse and burst current stimuli. The burst stimuli led to at least a 10-fold increase in peak force in smooth muscle from Mytilus edulis, despite the constant energy constraint. A linear model did not fit the data. However, a second-order model fit the data accurately, so the higher-order models were not required to fit the data. Results showed that smooth muscle force response is not linearly related to the stimulation power.
Binary Classifier Calibration Using a Bayesian Non-Parametric Approach.
Naeini, Mahdi Pakdaman; Cooper, Gregory F; Hauskrecht, Milos
Learning probabilistic predictive models that are well calibrated is critical for many prediction and decision-making tasks in Data mining. This paper presents two new non-parametric methods for calibrating outputs of binary classification models: a method based on the Bayes optimal selection and a method based on the Bayesian model averaging. The advantage of these methods is that they are independent of the algorithm used to learn a predictive model, and they can be applied in a post-processing step, after the model is learned. This makes them applicable to a wide variety of machine learning models and methods. These calibration methods, as well as other methods, are tested on a variety of datasets in terms of both discrimination and calibration performance. The results show the methods either outperform or are comparable in performance to the state-of-the-art calibration methods.
Analyzing information flow in brain networks with nonparametric Granger causality.
Dhamala, Mukeshwar; Rangarajan, Govindan; Ding, Mingzhou
2008-06-01
Multielectrode neurophysiological recording and high-resolution neuroimaging generate multivariate data that are the basis for understanding the patterns of neural interactions. How to extract directions of information flow in brain networks from these data remains a key challenge. Research over the last few years has identified Granger causality as a statistically principled technique to furnish this capability. The estimation of Granger causality currently requires autoregressive modeling of neural data. Here, we propose a nonparametric approach based on widely used Fourier and wavelet transforms to estimate both pairwise and conditional measures of Granger causality, eliminating the need of explicit autoregressive data modeling. We demonstrate the effectiveness of this approach by applying it to synthetic data generated by network models with known connectivity and to local field potentials recorded from monkeys performing a sensorimotor task.
Pointwise nonparametric maximum likelihood estimator of stochastically ordered survivor functions.
Park, Yongseok; Taylor, Jeremy M G; Kalbfleisch, John D
2012-06-01
In this paper, we consider estimation of survivor functions from groups of observations with right-censored data when the groups are subject to a stochastic ordering constraint. Many methods and algorithms have been proposed to estimate distribution functions under such restrictions, but none have completely satisfactory properties when the observations are censored. We propose a pointwise constrained nonparametric maximum likelihood estimator, which is defined at each time t by the estimates of the survivor functions subject to constraints applied at time t only. We also propose an efficient method to obtain the estimator. The estimator of each constrained survivor function is shown to be nonincreasing in t, and its consistency and asymptotic distribution are established. A simulation study suggests better small and large sample properties than for alternative estimators. An example using prostate cancer data illustrates the method.
A New Powerful Nonparametric Rank Test for Ordered Alternative Problem
Shan, Guogen; Young, Daniel; Kang, Le
2014-01-01
We propose a new nonparametric test for ordered alternative problem based on the rank difference between two observations from different groups. These groups are assumed to be independent from each other. The exact mean and variance of the test statistic under the null distribution are derived, and its asymptotic distribution is proven to be normal. Furthermore, an extensive power comparison between the new test and other commonly used tests shows that the new test is generally more powerful than others under various conditions, including the same type of distribution, and mixed distributions. A real example from an anti-hypertensive drug trial is provided to illustrate the application of the tests. The new test is therefore recommended for use in practice due to easy calculation and substantial power gain. PMID:25405757
Analyzing Information Flow in Brain Networks with Nonparametric Granger Causality
Dhamala, Mukeshwar; Rangarajan, Govindan; Ding, Mingzhou
2009-01-01
Multielectrode neurophysiological recording and high-resolution neuroimaging generate multivariate data that are the basis for understanding the patterns of neural interactions. How to extract directions of information flow in brain networks from these data remains a key challenge. Research over the last few years has identified Granger causality as a statistically principled technique to furnish this capability. The estimation of Granger causality currently requires autoregressive modeling of neural data. Here, we propose a nonparametric approach based on widely used Fourier and wavelet transforms to estimate Granger causality, eliminating the need of explicit autoregressive data modeling. We demonstrate the effectiveness of this approach by applying it to synthetic data generated by network models with known connectivity and to local field potentials recorded from monkeys performing a sensorimotor task. PMID:18394927
Analyzing Single-Molecule Time Series via Nonparametric Bayesian Inference
Hines, Keegan E.; Bankston, John R.; Aldrich, Richard W.
2015-01-01
The ability to measure the properties of proteins at the single-molecule level offers an unparalleled glimpse into biological systems at the molecular scale. The interpretation of single-molecule time series has often been rooted in statistical mechanics and the theory of Markov processes. While existing analysis methods have been useful, they are not without significant limitations including problems of model selection and parameter nonidentifiability. To address these challenges, we introduce the use of nonparametric Bayesian inference for the analysis of single-molecule time series. These methods provide a flexible way to extract structure from data instead of assuming models beforehand. We demonstrate these methods with applications to several diverse settings in single-molecule biophysics. This approach provides a well-constrained and rigorously grounded method for determining the number of biophysical states underlying single-molecule data. PMID:25650922
Non-parametric estimation of spatial variation in relative risk.
Kelsall, J E; Diggle, P J
We consider the problem of estimating the spatial variation in relative risks of two diseases, say, over a geographical region. Using an underlying Poisson point process model, we approach the problem as one of density ratio estimation implemented with a non-parametric kernel smoothing method. In order to assess the significance of any local peaks or troughs in the estimated risk surface, we introduce pointwise tolerance contours which can enhance a greyscale image plot of the estimate. We also propose a Monte Carlo test of the null hypothesis of constant risk over the whole region, to avoid possible over-interpretation of the estimated risk surface. We illustrate the capabilities of the methodology with two epidemiological examples.
A nonparametric stochastic optimizer for TDMA-based neuronal signaling.
Suzuki, Junichi; Phan, Dũng H; Budiman, Harry
2014-09-01
This paper considers neurons as a physical communication medium for intrabody networks of nano/micro-scale machines and formulates a noisy multiobjective optimization problem for a Time Division Multiple Access (TDMA) communication protocol atop the physical layer. The problem is to find the Pareto-optimal TDMA configurations that maximize communication performance (e.g., latency) by multiplexing a given neuronal network to parallelize signal transmissions while maximizing communication robustness (i.e., unlikeliness of signal interference) against noise in neuronal signaling. Using a nonparametric significance test, the proposed stochastic optimizer is designed to statistically determine the superior-inferior relationship between given two solution candidates and seek the optimal trade-offs among communication performance and robustness objectives. Simulation results show that the proposed optimizer efficiently obtains quality TDMA configurations in noisy environments and outperforms existing noise-aware stochastic optimizers.
Bayesian Nonparametric Shrinkage Applied to Cepheid Star Oscillations.
Berger, James; Jefferys, William; Müller, Peter
2012-01-01
Bayesian nonparametric regression with dependent wavelets has dual shrinkage properties: there is shrinkage through a dependent prior put on functional differences, and shrinkage through the setting of most of the wavelet coefficients to zero through Bayesian variable selection methods. The methodology can deal with unequally spaced data and is efficient because of the existence of fast moves in model space for the MCMC computation. The methodology is illustrated on the problem of modeling the oscillations of Cepheid variable stars; these are a class of pulsating variable stars with the useful property that their periods of variability are strongly correlated with their absolute luminosity. Once this relationship has been calibrated, knowledge of the period gives knowledge of the luminosity. This makes these stars useful as "standard candles" for estimating distances in the universe.
Analyzing multiple spike trains with nonparametric Granger causality.
Nedungadi, Aatira G; Rangarajan, Govindan; Jain, Neeraj; Ding, Mingzhou
2009-08-01
Simultaneous recordings of spike trains from multiple single neurons are becoming commonplace. Understanding the interaction patterns among these spike trains remains a key research area. A question of interest is the evaluation of information flow between neurons through the analysis of whether one spike train exerts causal influence on another. For continuous-valued time series data, Granger causality has proven an effective method for this purpose. However, the basis for Granger causality estimation is autoregressive data modeling, which is not directly applicable to spike trains. Various filtering options distort the properties of spike trains as point processes. Here we propose a new nonparametric approach to estimate Granger causality directly from the Fourier transforms of spike train data. We validate the method on synthetic spike trains generated by model networks of neurons with known connectivity patterns and then apply it to neurons simultaneously recorded from the thalamus and the primary somatosensory cortex of a squirrel monkey undergoing tactile stimulation.
Breeding curvature from extended gauge covariance
NASA Astrophysics Data System (ADS)
Aldrovandi, R.
1991-05-01
Independence between spacetime and “internal” space in gauge theories is related to the adjoint-covariant behaviour of the gauge potential. The usual gauge scheme is modified to allow a coupling between both spaces. Gauging spacetime translations produce field equations similar to Einstein equations. A curvature-like quantity of mixed differential-algebraic character emerges. Enlarged conservation laws are present, pointing to the presence of an covariance.
Covariate analysis of bivariate survival data
Bennett, L.E.
1992-01-01
The methods developed are used to analyze the effects of covariates on bivariate survival data when censoring and ties are present. The proposed method provides models for bivariate survival data that include differential covariate effects and censored observations. The proposed models are based on an extension of the univariate Buckley-James estimators which replace censored data points by their expected values, conditional on the censoring time and the covariates. For the bivariate situation, it is necessary to determine the expectation of the failure times for one component conditional on the failure or censoring time of the other component. Two different methods have been developed to estimate these expectations. In the semiparametric approach these expectations are determined from a modification of Burke's estimate of the bivariate empirical survival function. In the parametric approach censored data points are also replaced by their conditional expected values where the expected values are determined from a specified parametric distribution. The model estimation will be based on the revised data set, comprised of uncensored components and expected values for the censored components. The variance-covariance matrix for the estimated covariate parameters has also been derived for both the semiparametric and parametric methods. Data from the Demographic and Health Survey was analyzed by these methods. The two outcome variables are post-partum amenorrhea and breastfeeding; education and parity were used as the covariates. Both the covariate parameter estimates and the variance-covariance estimates for the semiparametric and parametric models will be compared. In addition, a multivariate test statistic was used in the semiparametric model to examine contrasts. The significance of the statistic was determined from a bootstrap distribution of the test statistic.
Noncommutative Gauge Theory with Covariant Star Product
Zet, G.
2010-08-04
We present a noncommutative gauge theory with covariant star product on a space-time with torsion. In order to obtain the covariant star product one imposes some restrictions on the connection of the space-time. Then, a noncommutative gauge theory is developed applying this product to the case of differential forms. Some comments on the advantages of using a space-time with torsion to describe the gravitational field are also given.
Covariant action for type IIB supergravity
NASA Astrophysics Data System (ADS)
Sen, Ashoke
2016-07-01
Taking clues from the recent construction of the covariant action for type II and heterotic string field theories, we construct a manifestly Lorentz covariant action for type IIB supergravity, and discuss its gauge fixing maintaining manifest Lorentz invariance. The action contains a (non-gravitating) free 4-form field besides the usual fields of type IIB supergravity. This free field, being completely decoupled from the interacting sector, has no physical consequence.
Phase-covariant quantum cloning of qudits
Fan Heng; Imai, Hiroshi; Matsumoto, Keiji; Wang, Xiang-Bin
2003-02-01
We study the phase-covariant quantum cloning machine for qudits, i.e., the input states in a d-level quantum system have complex coefficients with arbitrary phase but constant module. A cloning unitary transformation is proposed. After optimizing the fidelity between input state and single qudit reduced density operator of output state, we obtain the optimal fidelity for 1 to 2 phase-covariant quantum cloning of qudits and the corresponding cloning transformation.
Non-parametric transient classification using adaptive wavelets
NASA Astrophysics Data System (ADS)
Varughese, Melvin M.; von Sachs, Rainer; Stephanou, Michael; Bassett, Bruce A.
2015-11-01
Classifying transients based on multiband light curves is a challenging but crucial problem in the era of GAIA and Large Synoptic Sky Telescope since the sheer volume of transients will make spectroscopic classification unfeasible. We present a non-parametric classifier that predicts the transient's class given training data. It implements two novel components: the use of the BAGIDIS wavelet methodology - a characterization of functional data using hierarchical wavelet coefficients - as well as the introduction of a ranked probability classifier on the wavelet coefficients that handles both the heteroscedasticity of the data in addition to the potential non-representativity of the training set. The classifier is simple to implement while a major advantage of the BAGIDIS wavelets is that they are translation invariant. Hence, BAGIDIS does not need the light curves to be aligned to extract features. Further, BAGIDIS is non-parametric so it can be used effectively in blind searches for new objects. We demonstrate the effectiveness of our classifier against the Supernova Photometric Classification Challenge to correctly classify supernova light curves as Type Ia or non-Ia. We train our classifier on the spectroscopically confirmed subsample (which is not representative) and show that it works well for supernova with observed light-curve time spans greater than 100 d (roughly 55 per cent of the data set). For such data, we obtain a Ia efficiency of 80.5 per cent and a purity of 82.4 per cent, yielding a highly competitive challenge score of 0.49. This indicates that our `model-blind' approach may be particularly suitable for the general classification of astronomical transients in the era of large synoptic sky surveys.
Lorentz covariance of loop quantum gravity
NASA Astrophysics Data System (ADS)
Rovelli, Carlo; Speziale, Simone
2011-05-01
The kinematics of loop gravity can be given a manifestly Lorentz-covariant formulation: the conventional SU(2)-spin-network Hilbert space can be mapped to a space K of SL(2,C) functions, where Lorentz covariance is manifest. K can be described in terms of a certain subset of the projected spin networks studied by Livine, Alexandrov and Dupuis. It is formed by SL(2,C) functions completely determined by their restriction on SU(2). These are square-integrable in the SU(2) scalar product, but not in the SL(2,C) one. Thus, SU(2)-spin-network states can be represented by Lorentz-covariant SL(2,C) functions, as two-component photons can be described in the Lorentz-covariant Gupta-Bleuler formalism. As shown by Wolfgang Wieland in a related paper, this manifestly Lorentz-covariant formulation can also be directly obtained from canonical quantization. We show that the spinfoam dynamics of loop quantum gravity is locally SL(2,C)-invariant in the bulk, and yields states that are precisely in K on the boundary. This clarifies how the SL(2,C) spinfoam formalism yields an SU(2) theory on the boundary. These structures define a tidy Lorentz-covariant formalism for loop gravity.
Low-dimensional Representation of Error Covariance
NASA Technical Reports Server (NTRS)
Tippett, Michael K.; Cohn, Stephen E.; Todling, Ricardo; Marchesin, Dan
2000-01-01
Ensemble and reduced-rank approaches to prediction and assimilation rely on low-dimensional approximations of the estimation error covariances. Here stability properties of the forecast/analysis cycle for linear, time-independent systems are used to identify factors that cause the steady-state analysis error covariance to admit a low-dimensional representation. A useful measure of forecast/analysis cycle stability is the bound matrix, a function of the dynamics, observation operator and assimilation method. Upper and lower estimates for the steady-state analysis error covariance matrix eigenvalues are derived from the bound matrix. The estimates generalize to time-dependent systems. If much of the steady-state analysis error variance is due to a few dominant modes, the leading eigenvectors of the bound matrix approximate those of the steady-state analysis error covariance matrix. The analytical results are illustrated in two numerical examples where the Kalman filter is carried to steady state. The first example uses the dynamics of a generalized advection equation exhibiting nonmodal transient growth. Failure to observe growing modes leads to increased steady-state analysis error variances. Leading eigenvectors of the steady-state analysis error covariance matrix are well approximated by leading eigenvectors of the bound matrix. The second example uses the dynamics of a damped baroclinic wave model. The leading eigenvectors of a lowest-order approximation of the bound matrix are shown to approximate well the leading eigenvectors of the steady-state analysis error covariance matrix.
Dwivedi, Alok Kumar; Mallawaarachchi, Indika; Alvarado, Luis A
2017-03-09
Experimental studies in biomedical research frequently pose analytical problems related to small sample size. In such studies, there are conflicting findings regarding the choice of parametric and nonparametric analysis, especially with non-normal data. In such instances, some methodologists questioned the validity of parametric tests and suggested nonparametric tests. In contrast, other methodologists found nonparametric tests to be too conservative and less powerful and thus preferred using parametric tests. Some researchers have recommended using a bootstrap test; however, this method also has small sample size limitation. We used a pooled method in nonparametric bootstrap test that may overcome the problem related with small samples in hypothesis testing. The present study compared nonparametric bootstrap test with pooled resampling method corresponding to parametric, nonparametric, and permutation tests through extensive simulations under various conditions and using real data examples. The nonparametric pooled bootstrap t-test provided equal or greater power for comparing two means as compared with unpaired t-test, Welch t-test, Wilcoxon rank sum test, and permutation test while maintaining type I error probability for any conditions except for Cauchy and extreme variable lognormal distributions. In such cases, we suggest using an exact Wilcoxon rank sum test. Nonparametric bootstrap paired t-test also provided better performance than other alternatives. Nonparametric bootstrap test provided benefit over exact Kruskal-Wallis test. We suggest using nonparametric bootstrap test with pooled resampling method for comparing paired or unpaired means and for validating the one way analysis of variance test results for non-normal data in small sample size studies. Copyright © 2017 John Wiley & Sons, Ltd.
Structuring feature space: a non-parametric method for volumetric transfer function generation.
Maciejewski, Ross; Woo, Insoo; Chen, Wei; Ebert, David S
2009-01-01
The use of multi-dimensional transfer functions for direct volume rendering has been shown to be an effective means of extracting materials and their boundaries for both scalar and multivariate data. The most common multi-dimensional transfer function consists of a two-dimensional (2D) histogram with axes representing a subset of the feature space (e.g., value vs. value gradient magnitude), with each entry in the 2D histogram being the number of voxels at a given feature space pair. Users then assign color and opacity to the voxel distributions within the given feature space through the use of interactive widgets (e.g., box, circular, triangular selection). Unfortunately, such tools lead users through a trial-and-error approach as they assess which data values within the feature space map to a given area of interest within the volumetric space. In this work, we propose the addition of non-parametric clustering within the transfer function feature space in order to extract patterns and guide transfer function generation. We apply a non-parametric kernel density estimation to group voxels of similar features within the 2D histogram. These groups are then binned and colored based on their estimated density, and the user may interactively grow and shrink the binned regions to explore feature boundaries and extract regions of interest. We also extend this scheme to temporal volumetric data in which time steps of 2D histograms are composited into a histogram volume. A three-dimensional (3D) density estimation is then applied, and users can explore regions within the feature space across time without adjusting the transfer function at each time step. Our work enables users to effectively explore the structures found within a feature space of the volume and provide a context in which the user can understand how these structures relate to their volumetric data. We provide tools for enhanced exploration and manipulation of the transfer function, and we show that the initial
Kovanda, Laura L; Desai, Amit V; Lu, Qiaoyang; Townsend, Robert W; Akhtar, Shahzad; Bonate, Peter; Hope, William W
2016-08-01
Isavuconazonium sulfate (Cresemba; Astellas Pharma Inc.), a water-soluble prodrug of the triazole antifungal agent isavuconazole, is available for the treatment of invasive aspergillosis (IA) and invasive mucormycosis. A population pharmacokinetic (PPK) model was constructed using nonparametric estimation to compare the pharmacokinetic (PK) behaviors of isavuconazole in patients treated in the phase 3 VITAL open-label clinical trial, which evaluated the efficacy and safety of the drug for treatment of renally impaired IA patients and patients with invasive fungal disease (IFD) caused by emerging molds, yeasts, and dimorphic fungi. Covariates examined were body mass index (BMI), weight, race, impact of estimated glomerular filtration rate (eGFR) on clearance (CL), and impact of weight on volume. PK parameters were compared based on IFD type and other patient characteristics. Simulations were performed to describe the MICs covered by the clinical dosing regimen. Concentrations (n = 458) from 136 patients were used to construct a 2-compartment model (first-order absorption compartment and central compartment). Weight-related covariates affected clearance, but eGFR did not. PK parameters and intersubject variability of CL were similar across different IFD groups and populations. Target attainment analyses demonstrated that the clinical dosing regimen would be sufficient for total drug area under the concentration-time curve (AUC)/MIC targets ranging from 50.5 for Aspergillus spp. (up to the CLSI MIC of 0.5 mg/liter) to 270 and 5,053 for Candida albicans (up to MICs of 0.125 and 0.004 mg/liter, respectively) and 312 for non-albicans Candida spp. (up to a MIC of 0.125 mg/liter). The estimations for Candida spp. were exploratory considering that no patients with Candida infections were included in the current analyses. (The VITAL trial is registered at ClinicalTrials.gov under number NCT00634049.).
Kovanda, Laura L.; Desai, Amit V.; Lu, Qiaoyang; Townsend, Robert W.; Akhtar, Shahzad; Bonate, Peter
2016-01-01
Isavuconazonium sulfate (Cresemba; Astellas Pharma Inc.), a water-soluble prodrug of the triazole antifungal agent isavuconazole, is available for the treatment of invasive aspergillosis (IA) and invasive mucormycosis. A population pharmacokinetic (PPK) model was constructed using nonparametric estimation to compare the pharmacokinetic (PK) behaviors of isavuconazole in patients treated in the phase 3 VITAL open-label clinical trial, which evaluated the efficacy and safety of the drug for treatment of renally impaired IA patients and patients with invasive fungal disease (IFD) caused by emerging molds, yeasts, and dimorphic fungi. Covariates examined were body mass index (BMI), weight, race, impact of estimated glomerular filtration rate (eGFR) on clearance (CL), and impact of weight on volume. PK parameters were compared based on IFD type and other patient characteristics. Simulations were performed to describe the MICs covered by the clinical dosing regimen. Concentrations (n = 458) from 136 patients were used to construct a 2-compartment model (first-order absorption compartment and central compartment). Weight-related covariates affected clearance, but eGFR did not. PK parameters and intersubject variability of CL were similar across different IFD groups and populations. Target attainment analyses demonstrated that the clinical dosing regimen would be sufficient for total drug area under the concentration-time curve (AUC)/MIC targets ranging from 50.5 for Aspergillus spp. (up to the CLSI MIC of 0.5 mg/liter) to 270 and 5,053 for Candida albicans (up to MICs of 0.125 and 0.004 mg/liter, respectively) and 312 for non-albicans Candida spp. (up to a MIC of 0.125 mg/liter). The estimations for Candida spp. were exploratory considering that no patients with Candida infections were included in the current analyses. (The VITAL trial is registered at ClinicalTrials.gov under number NCT00634049.) PMID:27185799
Testing interaction between treatment and high-dimensional covariates in randomized clinical trials.
Callegaro, Andrea; Spiessens, Bart; Dizier, Benjamin; Montoya, Fernando U; van Houwelingen, Hans C
2016-10-20
In this paper, we considered different methods to test the interaction between treatment and a potentially large number (p) of covariates in randomized clinical trials. The simplest approach was to fit univariate (marginal) models and to combine the univariate statistics or p-values (e.g., minimum p-value). Another possibility was to reduce the dimension of the covariates using the principal components (PCs) and to test the interaction between treatment and PCs. Finally, we considered the Goeman global test applied to the high-dimensional interaction matrix, adjusted for the main (treatment and covariates) effects. These tests can be used for personalized medicine to test if a large set of biomarkers can be useful to identify a subset of patients who may be more responsive to treatment. We evaluated the performance of these methods on simulated data and we applied them on data from two early phases oncology clinical trials.
Impacts of data covariances on the calculated breeding ratio for CRBRP
Liaw, J.R.; Collins, P.J.; Henryson, H. II; Shenter, R.E.
1983-01-01
In order to establish confidence on the data adjustment methodology as applied to LMFBR design, and to estimate the importance of data correlations in that respect, an investigation was initiated on the impacts of data covariances on the calculated reactor performance parameters. This paper summarizes the results and findings of such an effort specifically related to the calculation of breeding ratio for CRBRP as an illustration. Thirty-nine integral parameters and their covariances, including k/sub eff/ and various capture and fission reaction rate ratios, from the ZEBRA-8 series and four ZPR physics benchmark assemblies were used in the least-squares fitting processes. Multigroup differential data and the sensitivity coefficients of those 39 integral parameters were generated by standard 2-D diffusion theory neutronic calculational modules at ANL. Three differential data covariance libraries, all based on ENDF/B-V evaluations, were tested in this study.
McCandless, Lawrence C; Gustafson, Paul; Austin, Peter C; Levy, Adrian R
2009-09-10
Regression adjustment for the propensity score is a statistical method that reduces confounding from measured variables in observational data. A Bayesian propensity score analysis extends this idea by using simultaneous estimation of the propensity scores and the treatment effect. In this article, we conduct an empirical investigation of the performance of Bayesian propensity scores in the context of an observational study of the effectiveness of beta-blocker therapy in heart failure patients. We study the balancing properties of the estimated propensity scores. Traditional Frequentist propensity scores focus attention on balancing covariates that are strongly associated with treatment. In contrast, we demonstrate that Bayesian propensity scores can be used to balance the association between covariates and the outcome. This balancing property has the effect of reducing confounding bias because it reduces the degree to which covariates are outcome risk factors.
A Covariance Generation Methodology for Fission Product Yields
NASA Astrophysics Data System (ADS)
Terranova, N.; Serot, O.; Archier, P.; Vallet, V.; De Saint Jean, C.; Sumini, M.
2016-03-01
Recent safety and economical concerns for modern nuclear reactor applications have fed an outstanding interest in basic nuclear data evaluation improvement and completion. It has been immediately clear that the accuracy of our predictive simulation models was strongly affected by our knowledge on input data. Therefore strong efforts have been made to improve nuclear data and to generate complete and reliable uncertainty information able to yield proper uncertainty propagation on integral reactor parameters. Since in modern nuclear data banks (such as JEFF-3.1.1 and ENDF/BVII.1) no correlations for fission yields are given, in the present work we propose a covariance generation methodology for fission product yields. The main goal is to reproduce the existing European library and to add covariance information to allow proper uncertainty propagation in depletion and decay heat calculations. To do so, we adopted the Generalized Least Square Method (GLSM) implemented in CONRAD (COde for Nuclear Reaction Analysis and Data assimilation), developed at CEA-Cadarache. Theoretical values employed in the Bayesian parameter adjustment are delivered thanks to a convolution of different models, representing several quantities in fission yield calculations: the Brosa fission modes for pre-neutron mass distribution, a simplified Gaussian model for prompt neutron emission probability, theWahl systematics for charge distribution and the Madland-England model for the isomeric ratio. Some results will be presented for the thermal fission of U-235, Pu-239 and Pu-241.
SCALE-6 Sensitivity/Uncertainty Methods and Covariance Data
Williams, Mark L; Rearden, Bradley T
2008-01-01
Computational methods and data used for sensitivity and uncertainty analysis within the SCALE nuclear analysis code system are presented. The methodology used to calculate sensitivity coefficients and similarity coefficients and to perform nuclear data adjustment is discussed. A description is provided of the SCALE-6 covariance library based on ENDF/B-VII and other nuclear data evaluations, supplemented by 'low-fidelity' approximate covariances. SCALE (Standardized Computer Analyses for Licensing Evaluation) is a modular code system developed by Oak Ridge National Laboratory (ORNL) to perform calculations for criticality safety, reactor physics, and radiation shielding applications. SCALE calculations typically use sequences that execute a predefined series of executable modules to compute particle fluxes and responses like the critical multiplication factor. SCALE also includes modules for sensitivity and uncertainty (S/U) analysis of calculated responses. The S/U codes in SCALE are collectively referred to as TSUNAMI (Tools for Sensitivity and UNcertainty Analysis Methodology Implementation). SCALE-6-scheduled for release in 2008-contains significant new capabilities, including important enhancements in S/U methods and data. The main functions of TSUNAMI are to (a) compute nuclear data sensitivity coefficients and response uncertainties, (b) establish similarity between benchmark experiments and design applications, and (c) reduce uncertainty in calculated responses by consolidating integral benchmark experiments. TSUNAMI includes easy-to-use graphical user interfaces for defining problem input and viewing three-dimensional (3D) geometries, as well as an integrated plotting package.
Convex Banding of the Covariance Matrix.
Bien, Jacob; Bunea, Florentina; Xiao, Luo
2016-01-01
We introduce a new sparse estimator of the covariance matrix for high-dimensional models in which the variables have a known ordering. Our estimator, which is the solution to a convex optimization problem, is equivalently expressed as an estimator which tapers the sample covariance matrix by a Toeplitz, sparsely-banded, data-adaptive matrix. As a result of this adaptivity, the convex banding estimator enjoys theoretical optimality properties not attained by previous banding or tapered estimators. In particular, our convex banding estimator is minimax rate adaptive in Frobenius and operator norms, up to log factors, over commonly-studied classes of covariance matrices, and over more general classes. Furthermore, it correctly recovers the bandwidth when the true covariance is exactly banded. Our convex formulation admits a simple and efficient algorithm. Empirical studies demonstrate its practical effectiveness and illustrate that our exactly-banded estimator works well even when the true covariance matrix is only close to a banded matrix, confirming our theoretical results. Our method compares favorably with all existing methods, in terms of accuracy and speed. We illustrate the practical merits of the convex banding estimator by showing that it can be used to improve the performance of discriminant analysis for classifying sound recordings.
A sparse Ising model with covariates.
Cheng, Jie; Levina, Elizaveta; Wang, Pei; Zhu, Ji
2014-12-01
There has been a lot of work fitting Ising models to multivariate binary data in order to understand the conditional dependency relationships between the variables. However, additional covariates are frequently recorded together with the binary data, and may influence the dependence relationships. Motivated by such a dataset on genomic instability collected from tumor samples of several types, we propose a sparse covariate dependent Ising model to study both the conditional dependency within the binary data and its relationship with the additional covariates. This results in subject-specific Ising models, where the subject's covariates influence the strength of association between the genes. As in all exploratory data analysis, interpretability of results is important, and we use ℓ1 penalties to induce sparsity in the fitted graphs and in the number of selected covariates. Two algorithms to fit the model are proposed and compared on a set of simulated data, and asymptotic results are established. The results on the tumor dataset and their biological significance are discussed in detail.
Upper and lower covariance bounds for perturbed linear systems
NASA Technical Reports Server (NTRS)
Xu, J.-H.; Skelton, R. E.; Zhu, G.
1990-01-01
Both upper and lower bounds are established for state covariance matrices under parameter perturbations of the plant. The motivation for this study lies in the fact that many robustness properties of linear systems are given explicitly in terms of the state covariance matrix. Moreover, there exists a theory for control by covariance assignment. The results provide robustness properties of these covariance controllers.
Yuan, Ke-Hai; Hayashi, Kentaro; Yanagihara, Hirokazu
2007-01-01
Model evaluation in covariance structure analysis is critical before the results can be trusted. Due to finite sample sizes and unknown distributions of real data, existing conclusions regarding a particular statistic may not be applicable in practice. The bootstrap procedure automatically takes care of the unknown distribution and, for a given sample size, also provides more accurate results than those based on standard asymptotics. But the procedure needs a matrix to play the role of the population covariance matrix. The closer the matrix is to the true population covariance matrix, the more valid the bootstrap inference is. The current paper proposes a class of covariance matrices by combining theory and data. Thus, a proper matrix from this class is closer to the true population covariance matrix than those constructed by any existing methods. Each of the covariance matrices is easy to generate and also satisfies several desired properties. An example with nine cognitive variables and a confirmatory factor model illustrates the details for creating population covariance matrices with different misspecifications. When evaluating the substantive model, bootstrap or simulation procedures based on these matrices will lead to more accurate conclusion than that based on artificial covariance matrices.
Progress on Nuclear Data Covariances: AFCI-1.2 Covariance Library
Oblozinsky,P.; Oblozinsky,P.; Mattoon,C.M.; Herman,M.; Mughabghab,S.F.; Pigni,M.T.; Talou,P.; Hale,G.M.; Kahler,A.C.; Kawano,T.; Little,R.C.; Young,P.G
2009-09-28
Improved neutron cross section covariances were produced for 110 materials including 12 light nuclei (coolants and moderators), 78 structural materials and fission products, and 20 actinides. Improved covariances were organized into AFCI-1.2 covariance library in 33-energy groups, from 10{sup -5} eV to 19.6 MeV. BNL contributed improved covariance data for the following materials: {sup 23}Na and {sup 55}Mn where more detailed evaluation was done; improvements in major structural materials {sup 52}Cr, {sup 56}Fe and {sup 58}Ni; improved estimates for remaining structural materials and fission products; improved covariances for 14 minor actinides, and estimates of mubar covariances for {sup 23}Na and {sup 56}Fe. LANL contributed improved covariance data for {sup 235}U and {sup 239}Pu including prompt neutron fission spectra and completely new evaluation for {sup 240}Pu. New R-matrix evaluation for {sup 16}O including mubar covariances is under completion. BNL assembled the library and performed basic testing using improved procedures including inspection of uncertainty and correlation plots for each material. The AFCI-1.2 library was released to ANL and INL in August 2009.
ERIC Educational Resources Information Center
Zeytun, Aysel Sen; Cetinkaya, Bulent; Erbas, Ayhan Kursat
2010-01-01
Various studies suggest that covariational reasoning plays an important role on understanding the fundamental ideas of calculus and modeling dynamic functional events. The purpose of this study was to investigate a group of mathematics teachers' covariational reasoning abilities and predictions about their students. Data were collected through…
NASA Astrophysics Data System (ADS)
Hui, Yi; Law, Siu Seong; Ku, Chiu Jen
2017-02-01
Covariance of the auto/cross-covariance matrix based method is studied for the damage identification of a structure with illustrations on its advantages and limitations. The original method is extended for structures under direct white noise excitations. The auto/cross-covariance function of the measured acceleration and its corresponding derivatives are formulated analytically, and the method is modified in two new strategies to enable successful identification with much fewer sensors. Numerical examples are adopted to illustrate the improved method, and the effects of sampling frequency and sampling duration are discussed. Results show that the covariance of covariance calculated from responses of higher order modes of a structure play an important role to the accurate identification of local damage in a structure.
FAST NEUTRON COVARIANCES FOR EVALUATED DATA FILES.
HERMAN, M.; OBLOZINSKY, P.; ROCHMAN, D.; KAWANO, T.; LEAL, L.
2006-06-05
We describe implementation of the KALMAN code in the EMPIRE system and present first covariance data generated for Gd and Ir isotopes. A complete set of covariances, in the full energy range, was produced for the chain of 8 Gadolinium isotopes for total, elastic, capture, total inelastic (MT=4), (n,2n), (n,p) and (n,alpha) reactions. Our correlation matrices, based on combination of model calculations and experimental data, are characterized by positive mid-range and negative long-range correlations. They differ from the model-generated covariances that tend to show strong positive long-range correlations and those determined solely from experimental data that result in nearly diagonal matrices. We have studied shapes of correlation matrices obtained in the calculations and interpreted them in terms of the underlying reaction models. An important result of this study is the prediction of narrow energy ranges with extremely small uncertainties for certain reactions (e.g., total and elastic).
Bayesian nonparametric clustering in phylogenetics: modeling antigenic evolution in influenza.
Cybis, Gabriela B; Sinsheimer, Janet S; Bedford, Trevor; Rambaut, Andrew; Lemey, Philippe; Suchard, Marc A
2017-01-18
Influenza is responsible for up to 500,000 deaths every year, and antigenic variability represents much of its epidemiological burden. To visualize antigenic differences across many viral strains, antigenic cartography methods use multidimensional scaling on binding assay data to map influenza antigenicity onto a low-dimensional space. Analysis of such assay data ideally leads to natural clustering of influenza strains of similar antigenicity that correlate with sequence evolution. To understand the dynamics of these antigenic groups, we present a framework that jointly models genetic and antigenic evolution by combining multidimensional scaling of binding assay data, Bayesian phylogenetic machinery and nonparametric clustering methods. We propose a phylogenetic Chinese restaurant process that extends the current process to incorporate the phylogenetic dependency structure between strains in the modeling of antigenic clusters. With this method, we are able to use the genetic information to better understand the evolution of antigenicity throughout epidemics, as shown in applications of this model to H1N1 influenza. Copyright © 2017 John Wiley & Sons, Ltd.
Nonparametric Bayes Classification and Hypothesis Testing on Manifolds
Bhattacharya, Abhishek; Dunson, David
2012-01-01
Our first focus is prediction of a categorical response variable using features that lie on a general manifold. For example, the manifold may correspond to the surface of a hypersphere. We propose a general kernel mixture model for the joint distribution of the response and predictors, with the kernel expressed in product form and dependence induced through the unknown mixing measure. We provide simple sufficient conditions for large support and weak and strong posterior consistency in estimating both the joint distribution of the response and predictors and the conditional distribution of the response. Focusing on a Dirichlet process prior for the mixing measure, these conditions hold using von Mises-Fisher kernels when the manifold is the unit hypersphere. In this case, Bayesian methods are developed for efficient posterior computation using slice sampling. Next we develop Bayesian nonparametric methods for testing whether there is a difference in distributions between groups of observations on the manifold having unknown densities. We prove consistency of the Bayes factor and develop efficient computational methods for its calculation. The proposed classification and testing methods are evaluated using simulation examples and applied to spherical data applications. PMID:22754028
Strategies for conditional two-locus nonparametric linkage analysis.
Angquist, Lars; Hössjer, Ola; Groop, Leif
2008-01-01
In this article we deal with two-locus nonparametric linkage (NPL) analysis, mainly in the context of conditional analysis. This means that one incorporates single-locus analysis information through conditioning when performing a two-locus analysis. Here we describe different strategies for using this approach. Cox et al. [Nat Genet 1999;21:213-215] implemented this as follows: (i) Calculate the one-locus NPL process over the included genome region(s). (ii) Weight the individual pedigree NPL scores using a weighting function depending on the NPL scores for the corresponding pedigrees at speci fi c conditioning loci. We generalize this by conditioning with respect to the inheritance vector rather than the NPL score and by separating between the case of known (prede fi ned) and unknown (estimated) conditioning loci. In the latter case we choose conditioning locus, or loci, according to prede fi ned criteria. The most general approach results in a random number of selected loci, depending on the results from the previous one-locus analysis. Major topics in this article include discussions on optimal score functions with respect to the noncentrality parameter (NCP), and how to calculate adequate p values and perform power calculations. We also discuss issues related to multiple tests which arise from the two-step procedure with several conditioning loci as well as from the genome-wide tests.
Nonparametric reconstruction of the dark energy equation of state
Holsclaw, Tracy; Sanso, Bruno; Lee, Herbert; Alam, Ujjaini; Heitmann, Katrin; Habib, Salman; Higdon, David
2010-11-15
A basic aim of ongoing and upcoming cosmological surveys is to unravel the mystery of dark energy. In the absence of a compelling theory to test, a natural approach is to better characterize the properties of dark energy in search of clues that can lead to a more fundamental understanding. One way to view this characterization is the improved determination of the redshift-dependence of the dark energy equation of state parameter, w(z). To do this requires a robust and bias-free method for reconstructing w(z) from data that does not rely on restrictive expansion schemes or assumed functional forms for w(z). We present a new nonparametric reconstruction method that solves for w(z) as a statistical inverse problem, based on a Gaussian process representation. This method reliably captures nontrivial behavior of w(z) and provides controlled error bounds. We demonstrate the power of the method on different sets of simulated supernova data; the approach can be easily extended to include diverse cosmological probes.
Nonparametric identification of structural modifications in Laplace domain
NASA Astrophysics Data System (ADS)
Suwała, G.; Jankowski, Ł.
2017-02-01
This paper proposes and experimentally verifies a Laplace-domain method for identification of structural modifications, which (1) unlike time-domain formulations, allows the identification to be focused on these parts of the frequency spectrum that have a high signal-to-noise ratio, and (2) unlike frequency-domain formulations, decreases the influence of numerical artifacts related to the particular choice of the FFT exponential window decay. In comparison to the time-domain approach proposed earlier, advantages of the proposed method are smaller computational cost and higher accuracy, which leads to reliable performance in more difficult identification cases. Analytical formulas for the first- and second-order sensitivity analysis are derived. The approach is based on a reduced nonparametric model, which has the form of a set of selected structural impulse responses. Such a model can be collected purely experimentally, which obviates the need for design and laborious updating of a parametric model, such as a finite element model. The approach is verified experimentally using a 26-node lab 3D truss structure and 30 identification cases of a single mass modification or two concurrent mass modifications.
Transition redshift: new constraints from parametric and nonparametric methods
Rani, Nisha; Mahajan, Shobhit; Mukherjee, Amitabha; Jain, Deepak; Pires, Nilza E-mail: djain@ddu.du.ac.in E-mail: amimukh@gmail.com
2015-12-01
In this paper, we use the cosmokinematics approach to study the accelerated expansion of the Universe. This is a model independent approach and depends only on the assumption that the Universe is homogeneous and isotropic and is described by the FRW metric. We parametrize the deceleration parameter, q(z), to constrain the transition redshift (z{sub t}) at which the expansion of the Universe goes from a decelerating to an accelerating phase. We use three different parametrizations of q(z) namely, q{sub I}(z)=q{sub 1}+q{sub 2}z, q{sub II} (z) = q{sub 3} + q{sub 4} ln (1 + z) and q{sub III} (z)=½+q{sub 5}/(1+z){sup 2}. A joint analysis of the age of galaxies, strong lensing and supernovae Ia data indicates that the transition redshift is less than unity i.e. z{sub t} < 1. We also use a nonparametric approach (LOESS+SIMEX) to constrain z{sub t}. This too gives z{sub t} < 1 which is consistent with the value obtained by the parametric approach.
Non-parametric reconstruction of cosmological matter perturbations
González, J.E.; Alcaniz, J.S.; Carvalho, J.C. E-mail: alcaniz@on.br
2016-04-01
Perturbative quantities, such as the growth rate (f) and index (γ), are powerful tools to distinguish different dark energy models or modified gravity theories even if they produce the same cosmic expansion history. In this work, without any assumption about the dynamics of the Universe, we apply a non-parametric method to current measurements of the expansion rate H(z) from cosmic chronometers and high-z quasar data and reconstruct the growth factor and rate of linearised density perturbations in the non-relativistic matter component. Assuming realistic values for the matter density parameter Ω{sub m0}, as provided by current CMB experiments, we also reconstruct the evolution of the growth index γ with redshift. We show that the reconstruction of current H(z) data constrains the growth index to γ=0.56 ± 0.12 (2σ) at z = 0.09, which is in full agreement with the prediction of the ΛCDM model and some of its extensions.
Non-parametric and least squares Langley plot methods
NASA Astrophysics Data System (ADS)
Kiedron, P. W.; Michalsky, J. J.
2016-01-01
Langley plots are used to calibrate sun radiometers primarily for the measurement of the aerosol component of the atmosphere that attenuates (scatters and absorbs) incoming direct solar radiation. In principle, the calibration of a sun radiometer is a straightforward application of the Bouguer-Lambert-Beer law V = V0e-τ ṡ m, where a plot of ln(V) voltage vs. m air mass yields a straight line with intercept ln(V0). This ln(V0) subsequently can be used to solve for τ for any measurement of V and calculation of m. This calibration works well on some high mountain sites, but the application of the Langley plot calibration technique is more complicated at other, more interesting, locales. This paper is concerned with ferreting out calibrations at difficult sites and examining and comparing a number of conventional and non-conventional methods for obtaining successful Langley plots. The 11 techniques discussed indicate that both least squares and various non-parametric techniques produce satisfactory calibrations with no significant differences among them when the time series of ln(V0)'s are smoothed and interpolated with median and mean moving window filters.
Non-parametric and least squares Langley plot methods
NASA Astrophysics Data System (ADS)
Kiedron, P. W.; Michalsky, J. J.
2015-04-01
Langley plots are used to calibrate sun radiometers primarily for the measurement of the aerosol component of the atmosphere that attenuates (scatters and absorbs) incoming direct solar radiation. In principle, the calibration of a sun radiometer is a straightforward application of the Bouguer-Lambert-Beer law V=V>/i>0e-τ ·m, where a plot of ln (V) voltage vs. m air mass yields a straight line with intercept ln (V0). This ln (V0) subsequently can be used to solve for τ for any measurement of V and calculation of m. This calibration works well on some high mountain sites, but the application of the Langley plot calibration technique is more complicated at other, more interesting, locales. This paper is concerned with ferreting out calibrations at difficult sites and examining and comparing a number of conventional and non-conventional methods for obtaining successful Langley plots. The eleven techniques discussed indicate that both least squares and various non-parametric techniques produce satisfactory calibrations with no significant differences among them when the time series of ln (V0)'s are smoothed and interpolated with median and mean moving window filters.
A nonparametric Bayesian framework for constructing flexible feature representations.
Austerweil, Joseph L; Griffiths, Thomas L
2013-10-01
Representations are a key explanatory device used by cognitive psychologists to account for human behavior. Understanding the effects of context and experience on the representations people use is essential, because if two people encode the same stimulus using different representations, their response to that stimulus may be different. We present a computational framework that can be used to define models that flexibly construct feature representations (where by a feature we mean a part of the image of an object) for a set of observed objects, based on nonparametric Bayesian statistics. Austerweil and Griffiths (2011) presented an initial model constructed in this framework that captures how the distribution of parts affects the features people use to represent a set of objects. We build on this work in three ways. First, although people use features that can be transformed on each observation (e.g., translate on the retinal image), many existing feature learning models can only recognize features that are not transformed (occur identically each time). Consequently, we extend the initial model to infer features that are invariant over a set of transformations, and learn different structures of dependence between feature transformations. Second, we compare two possible methods for capturing the manner that categorization affects feature representations. Finally, we present a model that learns features incrementally, capturing an effect of the order of object presentation on the features people learn. We conclude by considering the implications and limitations of our empirical and theoretical results.
Diffeomorphic demons: efficient non-parametric image registration.
Vercauteren, Tom; Pennec, Xavier; Perchant, Aymeric; Ayache, Nicholas
2009-03-01
We propose an efficient non-parametric diffeomorphic image registration algorithm based on Thirion's demons algorithm. In the first part of this paper, we show that Thirion's demons algorithm can be seen as an optimization procedure on the entire space of displacement fields. We provide strong theoretical roots to the different variants of Thirion's demons algorithm. This analysis predicts a theoretical advantage for the symmetric forces variant of the demons algorithm. We show on controlled experiments that this advantage is confirmed in practice and yields a faster convergence. In the second part of this paper, we adapt the optimization procedure underlying the demons algorithm to a space of diffeomorphic transformations. In contrast to many diffeomorphic registration algorithms, our solution is computationally efficient since in practice it only replaces an addition of displacement fields by a few compositions. Our experiments show that in addition to being diffeomorphic, our algorithm provides results that are similar to the ones from the demons algorithm but with transformations that are much smoother and closer to the gold standard, available in controlled experiments, in terms of Jacobians.
Nonparametric Bayes modeling for case control studies with many predictors.
Zhou, Jing; Herring, Amy H; Bhattacharya, Anirban; Olshan, Andrew F; Dunson, David B
2016-03-01
It is common in biomedical research to run case-control studies involving high-dimensional predictors, with the main goal being detection of the sparse subset of predictors having a significant association with disease. Usual analyses rely on independent screening, considering each predictor one at a time, or in some cases on logistic regression assuming no interactions. We propose a fundamentally different approach based on a nonparametric Bayesian low rank tensor factorization model for the retrospective likelihood. Our model allows a very flexible structure in characterizing the distribution of multivariate variables as unknown and without any linear assumptions as in logistic regression. Predictors are excluded only if they have no impact on disease risk, either directly or through interactions with other predictors. Hence, we obtain an omnibus approach for screening for important predictors. Computation relies on an efficient Gibbs sampler. The methods are shown to have high power and low false discovery rates in simulation studies, and we consider an application to an epidemiology study of birth defects.
Covariance based outlier detection with feature selection.
Zwilling, Chris E; Wang, Michelle Y
2016-08-01
The present covariance based outlier detection algorithm selects from a candidate set of feature vectors that are best at identifying outliers. Features extracted from biomedical and health informatics data can be more informative in disease assessment and there are no restrictions on the nature and number of features that can be tested. But an important challenge for an algorithm operating on a set of features is for it to winnow the effective features from the ineffective ones. The powerful algorithm described in this paper leverages covariance information from the time series data to identify features with the highest sensitivity for outlier identification. Empirical results demonstrate the efficacy of the method.
Sparse Covariance Matrix Estimation With Eigenvalue Constraints.
Liu, Han; Wang, Lie; Zhao, Tuo
2014-04-01
We propose a new approach for estimating high-dimensional, positive-definite covariance matrices. Our method extends the generalized thresholding operator by adding an explicit eigenvalue constraint. The estimated covariance matrix simultaneously achieves sparsity and positive definiteness. The estimator is rate optimal in the minimax sense and we develop an efficient iterative soft-thresholding and projection algorithm based on the alternating direction method of multipliers. Empirically, we conduct thorough numerical experiments on simulated datasets as well as real data examples to illustrate the usefulness of our method. Supplementary materials for the article are available online.
Parametric number covariance in quantum chaotic spectra.
Vinayak; Kumar, Sandeep; Pandey, Akhilesh
2016-03-01
We study spectral parametric correlations in quantum chaotic systems and introduce the number covariance as a measure of such correlations. We derive analytic results for the classical random matrix ensembles using the binary correlation method and obtain compact expressions for the covariance. We illustrate the universality of this measure by presenting the spectral analysis of the quantum kicked rotors for the time-reversal invariant and time-reversal noninvariant cases. A local version of the parametric number variance introduced earlier is also investigated.
Covariance Analysis of Gamma Ray Spectra
Trainham, R.; Tinsley, J.
2013-01-01
The covariance method exploits fluctuations in signals to recover information encoded in correlations which are usually lost when signal averaging occurs. In nuclear spectroscopy it can be regarded as a generalization of the coincidence technique. The method can be used to extract signal from uncorrelated noise, to separate overlapping spectral peaks, to identify escape peaks, to reconstruct spectra from Compton continua, and to generate secondary spectral fingerprints. We discuss a few statistical considerations of the covariance method and present experimental examples of its use in gamma spectroscopy.
Covariance analysis of gamma ray spectra
Trainham, R.; Tinsley, J.
2013-01-15
The covariance method exploits fluctuations in signals to recover information encoded in correlations which are usually lost when signal averaging occurs. In nuclear spectroscopy it can be regarded as a generalization of the coincidence technique. The method can be used to extract signal from uncorrelated noise, to separate overlapping spectral peaks, to identify escape peaks, to reconstruct spectra from Compton continua, and to generate secondary spectral fingerprints. We discuss a few statistical considerations of the covariance method and present experimental examples of its use in gamma spectroscopy.
Blamires, Sean J; Hasemore, Matthew; Martens, Penny J; Kasumovic, Michael M
2017-03-01
The adaptive benefits of extended phenotypic plasticity are imprecisely defined due to a paucity of experiments examining traits that are manipulable and measurable across environments. Spider webs are often used as models to explore the adaptive benefits of variations in extended phenotypes across environments. Nonetheless, our understanding of the adaptive nature of the plastic responses of spider webs is impeded when web architectures and silk physicochemical properties appear to co-vary. An opportunity to examine this co-variation is presented by modifying prey items while measuring web architectures and silk physiochemical properties. Here, we performed two experiments to assess the nature of the association between web architectures and gluey silk properties when the orb web spider Argiope keyserlingi was fed a diet that varied in either mass and energy or prey size and feeding frequency. We found web architectures and gluey silk physicochemical properties to co-vary across treatments in both experiments. Specifically, web capture area co-varied with gluey droplet morphometrics, thread stickiness and salt concentrations when prey mass and energy were manipulated, and spiral spacing co-varied with gluey silk salt concentrations when prey size and feeding frequency were manipulated. We explained our results as A. keyserlingi plastically shifting its foraging strategy as multiple prey parameters simultaneously varied. We confirmed and extended previous work by showing that spiders use a variety of prey cues to concurrently adjust web and silk traits across different feeding regimes.
ERIC Educational Resources Information Center
Samejima, Fumiko
1998-01-01
Introduces and discusses the rationale and procedures of two nonparametric approaches to estimating the operating characteristic of a discrete item response, or the conditional probability, given the latent trait, that the examinee's response be that specific response. (SLD)
Covariances of Evaluated Nuclear Cross Section Data for (232)Th, (180,182,183,184,186)W and (55)Mn
Trkov, A.; Capote, R.; Soukhovitskii, E; Leal, Luiz C; Sin, M; Kodeli, I.; Muir, D W
2011-01-01
The EMPIRE code system is a versatile package for nuclear model calculations that is often used for nuclear data evaluation. Its capabilities include random sampling of model parameters, which can be utilized to generate a full covariance matrix of all scattering cross sections, including cross-reaction correlations. The EMPIRE system was used to prepare the prior covariance matrices of reaction cross sections of (232)Th, (180,182,183,184,186)W and (55)Mn nuclei for incident neutron energies up to 60 MeV. The obtained modeling prior was fed to the GANDR system, which is a package for a global assessment of nuclear data, based on the Generalized Least-Squares method. By introducing experimental data from the EXFOR database into GANDR, the constrained covariance matrices and cross section adjustment functions were obtained. Applying the correction functions on the cross sections and formatting the covariance matrices, the final evaluations in ENDF-6 format including covariances were derived. In the resonance energy range, separate analyses were performed to determine the resonance parameters with their respective covariances. The data files thus obtained were then subjected to detailed testing and validation. Described evaluations with covariances of (232)Th, (180,182,183,184,186)W and (55)Mn nuclei are included into the ENDF/B-VII.1 library release.
Covariances of Evaluated Nuclear Cross Section Data for 232Th, 180,182,183,184,186W and 55Mn
NASA Astrophysics Data System (ADS)
Trkov, A.; Capote, R.; Soukhovitskii, E. Sh.; Leal, L. C.; Sin, M.; Kodeli, I.; Muir, D. W.
2011-12-01
The EMPIRE code system is a versatile package for nuclear model calculations that is often used for nuclear data evaluation. Its capabilities include random sampling of model parameters, which can be utilised to generate a full covariance matrix of all scattering cross sections, including cross-reaction correlations. The EMPIRE system was used to prepare the prior covariance matrices of reaction cross sections of 232Th, 180,182,183,184,186W and 55Mn nuclei for incident neutron energies up to 60 MeV. The obtained modelling prior was fed to the GANDR system, which is a package for a global assessment of nuclear data, based on the Generalised Least-Squares method. By introducing experimental data from the EXFOR database into GANDR, the constrained covariance matrices and cross section adjustment functions were obtained. Applying the correction functions on the cross sections and formatting the covariance matrices, the final evaluations in ENDF-6 format including covariances were derived. In the resonance energy range, separate analyses were performed to determine the resonance parameters with their respective covariances. The data files thus obtained were then subjected to detailed testing and validation. Described evaluations with covariances of 232Th, 180,182,183,184,186W and 55Mn nuclei are included into the ENDF/B-VII.1 library release.
Brier, Matthew R; Mitra, Anish; McCarthy, John E; Ances, Beau M; Snyder, Abraham Z
2015-11-01
Functional connectivity refers to shared signals among brain regions and is typically assessed in a task free state. Functional connectivity commonly is quantified between signal pairs using Pearson correlation. However, resting-state fMRI is a multivariate process exhibiting a complicated covariance structure. Partial covariance assesses the unique variance shared between two brain regions excluding any widely shared variance, hence is appropriate for the analysis of multivariate fMRI datasets. However, calculation of partial covariance requires inversion of the covariance matrix, which, in most functional connectivity studies, is not invertible owing to rank deficiency. Here we apply Ledoit-Wolf shrinkage (L2 regularization) to invert the high dimensional BOLD covariance matrix. We investigate the network organization and brain-state dependence of partial covariance-based functional connectivity. Although RSNs are conventionally defined in terms of shared variance, removal of widely shared variance, surprisingly, improved the separation of RSNs in a spring embedded graphical model. This result suggests that pair-wise unique shared variance plays a heretofore unrecognized role in RSN covariance organization. In addition, application of partial correlation to fMRI data acquired in the eyes open vs. eyes closed states revealed focal changes in uniquely shared variance between the thalamus and visual cortices. This result suggests that partial correlation of resting state BOLD time series reflect functional processes in addition to structural connectivity.
Brier, Matthew R.; Mitra, Anish; McCarthy, John E.; Ances, Beau M.; Snyder, Abraham Z.
2015-01-01
Functional connectivity refers to shared signals among brain regions and is typically assessed in a task free state. Functional connectivity commonly is quantified between signal pairs using Pearson correlation. However, resting-state fMRI is a multivariate process exhibiting a complicated covariance structure. Partial covariance assesses the unique variance shared between two brain regions excluding any widely shared variance, hence is appropriate for the analysis of multivariate fMRI datasets. However, calculation of partial covariance requires inversion of the covariance matrix, which, in most functional connectivity studies, is not invertible owing to rank deficiency. Here we apply Ledoit-Wolf shrinkage (L2 regularization) to invert the high dimensional BOLD covariance matrix. We investigate the network organization and brain-state dependence of partial covariance-based functional connectivity. Although RSNs are conventionally defined in terms of shared variance, removal of widely shared variance, surprisingly, improved the separation of RSNs in a spring embedded graphical model. This result suggests that pair-wise unique shared variance plays a heretofore unrecognized role in RSN covariance organization. In addition, application of partial correlation to fMRI data acquired in the eyes open vs. eyes closed states revealed focal changes in uniquely shared variance between the thalamus and visual cortices. This result suggests that partial correlation of resting state BOLD time series reflect functional processes in addition to structural connectivity. PMID:26208872
Nonparametric analysis of Minnesota spruce and aspen tree data and LANDSAT data
NASA Technical Reports Server (NTRS)
Scott, D. W.; Jee, R.
1984-01-01
The application of nonparametric methods in data-intensive problems faced by NASA is described. The theoretical development of efficient multivariate density estimators and the novel use of color graphics workstations are reviewed. The use of nonparametric density estimates for data representation and for Bayesian classification are described and illustrated. Progress in building a data analysis system in a workstation environment is reviewed and preliminary runs presented.
Fast Nonparametric Machine Learning Algorithms for High-Dimensional Massive Data and Applications
2006-03-01
Mapreduce : Simplified data processing on large clusters . In Symposium on Operating System Design and Implementation, 2004. 6.3.2 S. C. Deerwester, S. T...Fast Nonparametric Machine Learning Algorithms for High-dimensional Massive Data and Applications Ting Liu CMU-CS-06-124 March 2006 School of...4. TITLE AND SUBTITLE Fast Nonparametric Machine Learning Algorithms for High-dimensional Massive Data and Applications 5a. CONTRACT NUMBER 5b
Jung, S H; Su, J Q
1995-02-15
We propose a non-parametric method to calculate a confidence interval for the difference or ratio of two median failure times for paired observations with censoring. The new method is simple to calculate, does not involve non-parametric density estimates, and is valid asymptotically even when the two underlying distribution functions differ in shape. The method also allows missing observations. We report numerical studies to examine the performance of the new method for practical sample sizes.
Observed Score Linear Equating with Covariates
ERIC Educational Resources Information Center
Branberg, Kenny; Wiberg, Marie
2011-01-01
This paper examined observed score linear equating in two different data collection designs, the equivalent groups design and the nonequivalent groups design, when information from covariates (i.e., background variables correlated with the test scores) was included. The main purpose of the study was to examine the effect (i.e., bias, variance, and…
Economical phase-covariant cloning of qudits
Buscemi, Francesco; D'Ariano, Giacomo Mauro; Macchiavello, Chiara
2005-04-01
We derive the optimal N{yields}M phase-covariant quantum cloning for equatorial states in dimension d with M=kd+N, k integer. The cloning maps are optimal for both global and single-qudit fidelity. The map is achieved by an 'economical' cloning machine, which works without ancilla.
Wynant, Willy; Abrahamowicz, Michal
2014-08-30
Cox's proportional hazards (PH) model assumes constant-over-time covariate effects. Furthermore, most applications assume linear effects of continuous covariates on the logarithm of the hazard. Yet, many prognostic factors have time-dependent (TD) and/or nonlinear (NL) effects, that is, violate these conventional assumptions. Detection of such complex effects could affect prognosis and clinical decisions. However, assessing the effects of each of the multiple, often correlated, covariates in flexible multivariable analyses is challenging. In simulations, we investigated the impact of the approach used to build the flexible multivariable model on inference about the TD and NL covariate effects. Results demonstrate that the conclusions regarding the statistical significance of the TD/NL effects depend heavily on the strategy used to decide which effects of the other covariates should be adjusted for. Both a failure to adjust for true TD and NL effects of relevant covariates and inclusion of spurious effects of covariates that conform to the PH and linearity assumptions increase the risk of incorrect conclusions regarding other covariates. In this context, iterative backward elimination of nonsignificant NL and TD effects from the multivariable model, which initially includes all these effects, may help discriminate between true and spurious effects. The practical importance of these issues was illustrated in an example that reassessed the predictive ability of selected biomarkers for survival in advanced non-small-cell lung cancer. In conclusion, a careful model-building strategy and flexible modeling of multivariable survival data can yield new insights about predictors' roles and improve the validity of analyses.
A novel nonparametric confidence interval for differences of proportions for correlated binary data.
Duan, Chongyang; Cao, Yingshu; Zhou, Lizhi; Tan, Ming T; Chen, Pingyan
2016-11-16
Various confidence interval estimators have been developed for differences in proportions resulted from correlated binary data. However, the width of the mostly recommended Tango's score confidence interval tends to be wide, and the computing burden of exact methods recommended for small-sample data is intensive. The recently proposed rank-based nonparametric method by treating proportion as special areas under receiver operating characteristic provided a new way to construct the confidence interval for proportion difference on paired data, while the complex computation limits its application in practice. In this article, we develop a new nonparametric method utilizing the U-statistics approach for comparing two or more correlated areas under receiver operating characteristics. The new confidence interval has a simple analytic form with a new estimate of the degrees of freedom of n - 1. It demonstrates good coverage properties and has shorter confidence interval widths than that of Tango. This new confidence interval with the new estimate of degrees of freedom also leads to coverage probabilities that are an improvement on the rank-based nonparametric confidence interval. Comparing with the approximate exact unconditional method, the nonparametric confidence interval demonstrates good coverage properties even in small samples, and yet they are very easy to implement computationally. This nonparametric procedure is evaluated using simulation studies and illustrated with three real examples. The simplified nonparametric confidence interval is an appealing choice in practice for its ease of use and good performance.
A covariance NMR toolbox for MATLAB and OCTAVE.
Short, Timothy; Alzapiedi, Leigh; Brüschweiler, Rafael; Snyder, David
2011-03-01
The Covariance NMR Toolbox is a new software suite that provides a streamlined implementation of covariance-based analysis of multi-dimensional NMR data. The Covariance NMR Toolbox uses the MATLAB or, alternatively, the freely available GNU OCTAVE computer language, providing a user-friendly environment in which to apply and explore covariance techniques. Covariance methods implemented in the toolbox described here include direct and indirect covariance processing, 4D covariance, generalized indirect covariance (GIC), and Z-matrix transform. In order to provide compatibility with a wide variety of spectrometer and spectral analysis platforms, the Covariance NMR Toolbox uses the NMRPipe format for both input and output files. Additionally, datasets small enough to fit in memory are stored as arrays that can be displayed and further manipulated in a versatile manner within MATLAB or OCTAVE.
A robust nonparametric method for quantifying undetected extinctions.
Chisholm, Ryan A; Giam, Xingli; Sadanandan, Keren R; Fung, Tak; Rheindt, Frank E
2016-06-01
How many species have gone extinct in modern times before being described by science? To answer this question, and thereby get a full assessment of humanity's impact on biodiversity, statistical methods that quantify undetected extinctions are required. Such methods have been developed recently, but they are limited by their reliance on parametric assumptions; specifically, they assume the pools of extant and undetected species decay exponentially, whereas real detection rates vary temporally with survey effort and real extinction rates vary with the waxing and waning of threatening processes. We devised a new, nonparametric method for estimating undetected extinctions. As inputs, the method requires only the first and last date at which each species in an ensemble was recorded. As outputs, the method provides estimates of the proportion of species that have gone extinct, detected, or undetected and, in the special case where the number of undetected extant species in the present day is assumed close to zero, of the absolute number of undetected extinct species. The main assumption of the method is that the per-species extinction rate is independent of whether a species has been detected or not. We applied the method to the resident native bird fauna of Singapore. Of 195 recorded species, 58 (29.7%) have gone extinct in the last 200 years. Our method projected that an additional 9.6 species (95% CI 3.4, 19.8) have gone extinct without first being recorded, implying a true extinction rate of 33.0% (95% CI 31.0%, 36.2%). We provide R code for implementing our method. Because our method does not depend on strong assumptions, we expect it to be broadly useful for quantifying undetected extinctions.
Nonparametric Bayesian inference of the microcanonical stochastic block model
NASA Astrophysics Data System (ADS)
Peixoto, Tiago P.
2017-01-01
A principled approach to characterize the hidden modular structure of networks is to formulate generative models and then infer their parameters from data. When the desired structure is composed of modules or "communities," a suitable choice for this task is the stochastic block model (SBM), where nodes are divided into groups, and the placement of edges is conditioned on the group memberships. Here, we present a nonparametric Bayesian method to infer the modular structure of empirical networks, including the number of modules and their hierarchical organization. We focus on a microcanonical variant of the SBM, where the structure is imposed via hard constraints, i.e., the generated networks are not allowed to violate the patterns imposed by the model. We show how this simple model variation allows simultaneously for two important improvements over more traditional inference approaches: (1) deeper Bayesian hierarchies, with noninformative priors replaced by sequences of priors and hyperpriors, which not only remove limitations that seriously degrade the inference on large networks but also reveal structures at multiple scales; (2) a very efficient inference algorithm that scales well not only for networks with a large number of nodes and edges but also with an unlimited number of modules. We show also how this approach can be used to sample modular hierarchies from the posterior distribution, as well as to perform model selection. We discuss and analyze the differences between sampling from the posterior and simply finding the single parameter estimate that maximizes it. Furthermore, we expose a direct equivalence between our microcanonical approach and alternative derivations based on the canonical SBM.
kdetrees: non-parametric estimation of phylogenetic tree distributions
Weyenberg, Grady; Huggins, Peter M.; Schardl, Christopher L.; Howe, Daniel K.; Yoshida, Ruriko
2014-01-01
Motivation: Although the majority of gene histories found in a clade of organisms are expected to be generated by a common process (e.g. the coalescent process), it is well known that numerous other coexisting processes (e.g. horizontal gene transfers, gene duplication and subsequent neofunctionalization) will cause some genes to exhibit a history distinct from those of the majority of genes. Such ‘outlying’ gene trees are considered to be biologically interesting, and identifying these genes has become an important problem in phylogenetics. Results: We propose and implement kdetrees, a non-parametric method for estimating distributions of phylogenetic trees, with the goal of identifying trees that are significantly different from the rest of the trees in the sample. Our method compares favorably with a similar recently published method, featuring an improvement of one polynomial order of computational complexity (to quadratic in the number of trees analyzed), with simulation studies suggesting only a small penalty to classification accuracy. Application of kdetrees to a set of Apicomplexa genes identified several unreliable sequence alignments that had escaped previous detection, as well as a gene independently reported as a possible case of horizontal gene transfer. We also analyze a set of Epichloë genes, fungi symbiotic with grasses, successfully identifying a contrived instance of paralogy. Availability and implementation: Our method for estimating tree distributions and identifying outlying trees is implemented as the R package kdetrees and is available for download from CRAN. Contact: ruriko.yoshida@uky.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24764459
Economic decision making and the application of nonparametric prediction models
Attanasi, E.D.; Coburn, T.C.; Freeman, P.A.
2007-01-01
Sustained increases in energy prices have focused attention on gas resources in low permeability shale or in coals that were previously considered economically marginal. Daily well deliverability is often relatively small, although the estimates of the total volumes of recoverable resources in these settings are large. Planning and development decisions for extraction of such resources must be area-wide because profitable extraction requires optimization of scale economies to minimize costs and reduce risk. For an individual firm the decision to enter such plays depends on reconnaissance level estimates of regional recoverable resources and on cost estimates to develop untested areas. This paper shows how simple nonparametric local regression models, used to predict technically recoverable resources at untested sites, can be combined with economic models to compute regional scale cost functions. The context of the worked example is the Devonian Antrim shale gas play, Michigan Basin. One finding relates to selection of the resource prediction model to be used with economic models. Models which can best predict aggregate volume over larger areas (many hundreds of sites) may lose granularity in the distribution of predicted volumes at individual sites. This loss of detail affects the representation of economic cost functions and may affect economic decisions. Second, because some analysts consider unconventional resources to be ubiquitous, the selection and order of specific drilling sites may, in practice, be determined by extraneous factors. The paper also shows that when these simple prediction models are used to strategically order drilling prospects, the gain in gas volume over volumes associated with simple random site selection amounts to 15 to 20 percent. It also discusses why the observed benefit of updating predictions from results of new drilling, as opposed to following static predictions, is somewhat smaller. Copyright 2007, Society of Petroleum Engineers.
Economic decision making and the application of nonparametric prediction models
Attanasi, E.D.; Coburn, T.C.; Freeman, P.A.
2008-01-01
Sustained increases in energy prices have focused attention on gas resources in low-permeability shale or in coals that were previously considered economically marginal. Daily well deliverability is often relatively small, although the estimates of the total volumes of recoverable resources in these settings are often large. Planning and development decisions for extraction of such resources must be areawide because profitable extraction requires optimization of scale economies to minimize costs and reduce risk. For an individual firm, the decision to enter such plays depends on reconnaissance-level estimates of regional recoverable resources and on cost estimates to develop untested areas. This paper shows how simple nonparametric local regression models, used to predict technically recoverable resources at untested sites, can be combined with economic models to compute regional-scale cost functions. The context of the worked example is the Devonian Antrim-shale gas play in the Michigan basin. One finding relates to selection of the resource prediction model to be used with economic models. Models chosen because they can best predict aggregate volume over larger areas (many hundreds of sites) smooth out granularity in the distribution of predicted volumes at individual sites. This loss of detail affects the representation of economic cost functions and may affect economic decisions. Second, because some analysts consider unconventional resources to be ubiquitous, the selection and order of specific drilling sites may, in practice, be determined arbitrarily by extraneous factors. The analysis shows a 15-20% gain in gas volume when these simple models are applied to order drilling prospects strategically rather than to choose drilling locations randomly. Copyright ?? 2008 Society of Petroleum Engineers.
A non-parametric model for the cosmic velocity field
NASA Astrophysics Data System (ADS)
Branchini, E.; Teodoro, L.; Frenk, C. S.; Schmoldt, I.; Efstathiou, G.; White, S. D. M.; Saunders, W.; Sutherland, W.; Rowan-Robinson, M.; Keeble, O.; Tadros, H.; Maddox, S.; Oliver, S.
1999-09-01
We present a self-consistent non-parametric model of the local cosmic velocity field derived from the distribution of IRAS galaxies in the PSCz redshift survey. The survey has been analysed using two independent methods, both based on the assumptions of gravitational instability and linear biasing. The two methods, which give very similar results, have been tested and calibrated on mock PSCz catalogues constructed from cosmological N-body simulations. The denser sampling provided by the PSCz survey compared with previous IRAS galaxy surveys allows an improved reconstruction of the density and velocity fields out to large distances. The most striking feature of the model velocity field is a coherent large-scale streaming motion along the baseline connecting Perseus-Pisces, the Local Supercluster, the Great Attractor and the Shapley Concentration. We find no evidence for back-infall on to the Great Attractor. Instead, material behind and around the Great Attractor is inferred to be streaming towards the Shapley Concentration, aided by the compressional push of two large nearby underdensities. The PSCz model velocities compare well with those predicted from the 1.2-Jy redshift survey of IRAS galaxies and, perhaps surprisingly, with those predicted from the distribution of Abell/ACO clusters, out to 140h^-1Mpc. Comparison of the real-space density fields (or, alternatively, the peculiar velocity fields) inferred from the PSCz and cluster catalogues gives a relative (linear) bias parameter between clusters and IRAS galaxies of b_c=4.4+/-0.6. Finally, we implement a likelihood analysis that uses all the available information on peculiar velocities in our local Universe to estimate beta_Omega 0 0.6 b_0.6 -0.15 +0.22 (1sigma), where b is the bias parameter for IRAS galaxies.
ERIC Educational Resources Information Center
Loehlin, John C.; Neiderhiser, Jenae M.; Reiss, David
2005-01-01
Adolescent adjustment measures may be related to each other and to the social environment in various ways. Are these relationships similar in genetic and environmental sources of covariation, or different? A multivariate behaviorgenetic analysis was made of 6 adjustment and 3 treatment composites from the study Nonshared Environment in Adolescent…
Construction of Covariance Functions with Variable Length Fields
NASA Technical Reports Server (NTRS)
Gaspari, Gregory; Cohn, Stephen E.; Guo, Jing; Pawson, Steven
2005-01-01
This article focuses on construction, directly in physical space, of three-dimensional covariance functions parametrized by a tunable length field, and on an application of this theory to reproduce the Quasi-Biennial Oscillation (QBO) in the Goddard Earth Observing System, Version 4 (GEOS-4) data assimilation system. These Covariance models are referred to as multi-level or nonseparable, to associate them with the application where a multi-level covariance with a large troposphere to stratosphere length field gradient is used to reproduce the QBO from sparse radiosonde observations in the tropical lower stratosphere. The multi-level covariance functions extend well-known single level covariance functions depending only on a length scale. Generalizations of the first- and third-order autoregressive covariances in three dimensions are given, providing multi-level covariances with zero and three derivatives at zero separation, respectively. Multi-level piecewise rational covariances with two continuous derivatives at zero separation are also provided. Multi-level powerlaw covariances are constructed with continuous derivatives of all orders. Additional multi-level covariance functions are constructed using the Schur product of single and multi-level covariance functions. A multi-level powerlaw covariance used to reproduce the QBO in GEOS-4 is described along with details of the assimilation experiments. The new covariance model is shown to represent the vertical wind shear associated with the QBO much more effectively than in the baseline GEOS-4 system.
Lorentz Covariant Distributions with Spectral Conditions
Zinoviev, Yury M.
2007-11-14
The properties of the vacuum expectation values of products of the quantum fields are formulated in the book [1]. The vacuum expectation values of quantum fields products would be the Fourier transforms of the Lorentz covariant tempered distributions with supports in the product of the closed upper light cones. Lorentz invariant distributions are studied in the papers [2]--[4]. The authors of these papers wanted to describe Lorentz invariant distributions in terms of distributions given on the Lorentz group orbit space. This orbit space has a complicated structure. It is noted [5] that a tempered distribution with support in the closed upper light cone may be represented as the action of the wave operator in some power on a differentiable function with support in the closed upper light cone. For the description of the Lorentz covariant differentiable functions the boundary of the closed upper light cone is not important. The measure of this boundary is zero.
RNA sequence analysis using covariance models.
Eddy, S R; Durbin, R
1994-01-01
We describe a general approach to several RNA sequence analysis problems using probabilistic models that flexibly describe the secondary structure and primary sequence consensus of an RNA sequence family. We call these models 'covariance models'. A covariance model of tRNA sequences is an extremely sensitive and discriminative tool for searching for additional tRNAs and tRNA-related sequences in sequence databases. A model can be built automatically from an existing sequence alignment. We also describe an algorithm for learning a model and hence a consensus secondary structure from initially unaligned example sequences and no prior structural information. Models trained on unaligned tRNA examples correctly predict tRNA secondary structure and produce high-quality multiple alignments. The approach may be applied to any family of small RNA sequences. Images PMID:8029015
Chiral four-dimensional heterotic covariant lattices
NASA Astrophysics Data System (ADS)
Beye, Florian
2014-11-01
In the covariant lattice formalism, chiral four-dimensional heterotic string vacua are obtained from certain even self-dual lattices which completely decompose into a left-mover and a right-mover lattice. The main purpose of this work is to classify all right-mover lattices that can appear in such a chiral model, and to study the corresponding left-mover lattices using the theory of lattice genera. In particular, the Smith-Minkowski-Siegel mass formula is employed to calculate a lower bound on the number of left-mover lattices. Also, the known relationship between asymmetric orbifolds and covariant lattices is considered in the context of our classification.
On covariance structure in noisy, big data
NASA Astrophysics Data System (ADS)
Paffenroth, Randy C.; Nong, Ryan; Du Toit, Philip C.
2013-09-01
Herein we describe theory and algorithms for detecting covariance structures in large, noisy data sets. Our work uses ideas from matrix completion and robust principal component analysis to detect the presence of low-rank covariance matrices, even when the data is noisy, distorted by large corruptions, and only partially observed. In fact, the ability to handle partial observations combined with ideas from randomized algorithms for matrix decomposition enables us to produce asymptotically fast algorithms. Herein we will provide numerical demonstrations of the methods and their convergence properties. While such methods have applicability to many problems, including mathematical finance, crime analysis, and other large-scale sensor fusion problems, our inspiration arises from applying these methods in the context of cyber network intrusion detection.
Torsion and geometrostasis in covariant superstrings
Zachos, C.
1985-01-01
The covariant action for freely propagating heterotic superstrings consists of a metric and a torsion term with a special relative strength. It is shown that the strength for which torsion flattens the underlying 10-dimensional superspace geometry is precisely that which yields free oscillators on the light cone. This is in complete analogy with the geometrostasis of two-dimensional sigma-models with Wess-Zumino interactions. 13 refs.
Discrete symmetries in covariant loop quantum gravity
NASA Astrophysics Data System (ADS)
Rovelli, Carlo; Wilson-Ewing, Edward
2012-09-01
We study time-reversal and parity—on the physical manifold and in internal space—in covariant loop gravity. We consider a minor modification of the Holst action which makes it transform coherently under such transformations. The classical theory is not affected but the quantum theory is slightly different. In particular, the simplicity constraints are slightly modified and this restricts orientation flips in a spin foam to occur only across degenerate regions, thus reducing the sources of potential divergences.
Covariance expressions for eigenvalue and eigenvector problems
NASA Astrophysics Data System (ADS)
Liounis, Andrew J.
There are a number of important scientific and engineering problems whose solutions take the form of an eigenvalue--eigenvector problem. Some notable examples include solutions to linear systems of ordinary differential equations, controllability of linear systems, finite element analysis, chemical kinetics, fitting ellipses to noisy data, and optimal estimation of attitude from unit vectors. In many of these problems, having knowledge of the eigenvalue and eigenvector Jacobians is either necessary or is nearly as important as having the solution itself. For instance, Jacobians are necessary to find the uncertainty in a computed eigenvalue or eigenvector estimate. This uncertainty, which is usually represented as a covariance matrix, has been well studied for problems similar to the eigenvalue and eigenvector problem, such as singular value decomposition. There has been substantially less research on the covariance of an optimal estimate originating from an eigenvalue-eigenvector problem. In this thesis we develop two general expressions for the Jacobians of eigenvalues and eigenvectors with respect to the elements of their parent matrix. The expressions developed make use of only the parent matrix and the eigenvalue and eigenvector pair under consideration. In addition, they are applicable to any general matrix (including complex valued matrices, eigenvalues, and eigenvectors) as long as the eigenvalues are simple. Alongside this, we develop expressions that determine the uncertainty in a vector estimate obtained from an eigenvalue-eigenvector problem given the uncertainty of the terms of the matrix. The Jacobian expressions developed are numerically validated with forward finite, differencing and the covariance expressions are validated using Monte Carlo analysis. Finally, the results from this work are used to determine covariance expressions for a variety of estimation problem examples and are also applied to the design of a dynamical system.
Linear Covariance Analysis for a Lunar Lander
NASA Technical Reports Server (NTRS)
Jang, Jiann-Woei; Bhatt, Sagar; Fritz, Matthew; Woffinden, David; May, Darryl; Braden, Ellen; Hannan, Michael
2017-01-01
A next-generation lunar lander Guidance, Navigation, and Control (GNC) system, which includes a state-of-the-art optical sensor suite, is proposed in a concept design cycle. The design goal is to allow the lander to softly land within the prescribed landing precision. The achievement of this precision landing requirement depends on proper selection of the sensor suite. In this paper, a robust sensor selection procedure is demonstrated using a Linear Covariance (LinCov) analysis tool developed by Draper.
Inverse covariance simplification for efficient uncertainty management
NASA Astrophysics Data System (ADS)
Jalobeanu, A.; Gutiérrez, J. A.
2007-11-01
When it comes to manipulating uncertain knowledge such as noisy observations of physical quantities, one may ask how to do it in a simple way. Processing corrupted signals or images always propagates the uncertainties from the data to the final results, whether these errors are explicitly computed or not. When such error estimates are provided, it is crucial to handle them in such a way that their interpretation, or their use in subsequent processing steps, remain user-friendly and computationally tractable. A few authors follow a Bayesian approach and provide uncertainties as an inverse covariance matrix. Despite its apparent sparsity, this matrix contains many small terms that carry little information. Methods have been developed to select the most significant entries, through the use of information-theoretic tools for instance. One has to find a Gaussian pdf that is close enough to the posterior pdf, and with a small number of non-zero coefficients in the inverse covariance matrix. We propose to restrict the search space to Markovian models (where only neighbors can interact), well-suited to signals or images. The originality of our approach is in conserving the covariances between neighbors while setting to zero the entries of the inverse covariance matrix for all other variables. This fully constrains the solution, and the computation is performed via a fast, alternate minimization scheme involving quadratic forms. The Markovian structure advantageously reduces the complexity of Bayesian updating (where the simplified pdf is used as a prior). Moreover, uncertainties exhibit the same temporal or spatial structure as the data.
Covariant quantization of the CBS superparticle
NASA Astrophysics Data System (ADS)
Grassi, P. A.; Policastro, G.; Porrati, M.
2001-07-01
The quantization of the Casalbuoni-Brink-Schwarz superparticle is performed in an explicitly covariant way using the antibracket formalism. Since an infinite number of ghost fields are required, within a suitable off-shell twistor-like formalism, we are able to fix the gauge of each ghost sector without modifying the physical content of the theory. The computation reveals that the antibracket cohomology contains only the physical degrees of freedom.
Twisted covariant noncommutative self-dual gravity
Estrada-Jimenez, S.; Garcia-Compean, H.; Obregon, O.; Ramirez, C.
2008-12-15
A twisted covariant formulation of noncommutative self-dual gravity is presented. The formulation for constructing twisted noncommutative Yang-Mills theories is used. It is shown that the noncommutative torsion is solved at any order of the {theta} expansion in terms of the tetrad and some extra fields of the theory. In the process the first order expansion in {theta} for the Plebanski action is explicitly obtained.
Development of covariance capabilities in EMPIRE code
Herman,M.; Pigni, M.T.; Oblozinsky, P.; Mughabghab, S.F.; Mattoon, C.M.; Capote, R.; Cho, Young-Sik; Trkov, A.
2008-06-24
The nuclear reaction code EMPIRE has been extended to provide evaluation capabilities for neutron cross section covariances in the thermal, resolved resonance, unresolved resonance and fast neutron regions. The Atlas of Neutron Resonances by Mughabghab is used as a primary source of information on uncertainties at low energies. Care is taken to ensure consistency among the resonance parameter uncertainties and those for thermal cross sections. The resulting resonance parameter covariances are formatted in the ENDF-6 File 32. In the fast neutron range our methodology is based on model calculations with the code EMPIRE combined with experimental data through several available approaches. The model-based covariances can be obtained using deterministic (Kalman) or stochastic (Monte Carlo) propagation of model parameter uncertainties. We show that these two procedures yield comparable results. The Kalman filter and/or the generalized least square fitting procedures are employed to incorporate experimental information. We compare the two approaches analyzing results for the major reaction channels on {sup 89}Y. We also discuss a long-standing issue of unreasonably low uncertainties and link it to the rigidity of the model.
Using Covariance Analysis to Assess Pointing Performance
NASA Technical Reports Server (NTRS)
Bayard, David; Kang, Bryan
2009-01-01
A Pointing Covariance Analysis Tool (PCAT) has been developed for evaluating the expected performance of the pointing control system for NASA s Space Interferometry Mission (SIM). The SIM pointing control system is very complex, consisting of multiple feedback and feedforward loops, and operating with multiple latencies and data rates. The SIM pointing problem is particularly challenging due to the effects of thermomechanical drifts in concert with the long camera exposures needed to image dim stars. Other pointing error sources include sensor noises, mechanical vibrations, and errors in the feedforward signals. PCAT models the effects of finite camera exposures and all other error sources using linear system elements. This allows the pointing analysis to be performed using linear covariance analysis. PCAT propagates the error covariance using a Lyapunov equation associated with time-varying discrete and continuous-time system matrices. Unlike Monte Carlo analysis, which could involve thousands of computational runs for a single assessment, the PCAT analysis performs the same assessment in a single run. This capability facilitates the analysis of parametric studies, design trades, and "what-if" scenarios for quickly evaluating and optimizing the control system architecture and design.
Shrinkage covariance matrix approach for microarray data
NASA Astrophysics Data System (ADS)
Karjanto, Suryaefiza; Aripin, Rasimah
2013-04-01
Microarray technology was developed for the purpose of monitoring the expression levels of thousands of genes. A microarray data set typically consists of tens of thousands of genes (variables) from just dozens of samples due to various constraints including the high cost of producing microarray chips. As a result, the widely used standard covariance estimator is not appropriate for this purpose. One such technique is the Hotelling's T2 statistic which is a multivariate test statistic for comparing means between two groups. It requires that the number of observations (n) exceeds the number of genes (p) in the set but in microarray studies it is common that n < p. This leads to a biased estimate of the covariance matrix. In this study, the Hotelling's T2 statistic with the shrinkage approach is proposed to estimate the covariance matrix for testing differential gene expression. The performance of this approach is then compared with other commonly used multivariate tests using a widely analysed diabetes data set as illustrations. The results across the methods are consistent, implying that this approach provides an alternative to existing techniques.
All covariance controllers for linear discrete-time systems
NASA Technical Reports Server (NTRS)
Hsieh, Chen; Skelton, Robert E.
1990-01-01
The set of covariances that a linear discrete-time plant with a specified-order controller can have is characterized. The controllers that assign such covariances to any linear discrete-time system are given explicitly in closed form. The freedom in these covariance controllers is explicit and is parameterized by two orthogonal matrices. By appropriately choosing these free parameters, additional system objectives can be achieved without altering the state covariance, and the stability of the closed-loop system is guaranteed.
Factorization of the Discrete Noise Covariance Matrix for Plans,
1991-02-01
rapport prdsente la formulation exacte de la matrice de covariance Qk necessaire pour la propagation de la matrice de covariance du filtre Kalman ...approximation la d6composition necessaire pour utiliser la formulation Biermann-Agee-Turner du filtre Kalman . Cette decomposition approximative est...form of the discrete driving noise covariance matrix Qk which is needed to propagate the covariance matrix in the Kalman filter used by PLANS. It is
Hidden Covariation Detection Produces Faster, Not Slower, Social Judgments
ERIC Educational Resources Information Center
Barker, Lynne A.; Andrade, Jackie
2006-01-01
In P. Lewicki's (1986b) demonstration of hidden covariation detection (HCD), responses of participants were slower to faces that corresponded with a covariation encountered previously than to faces with novel covariations. This slowing contrasts with the typical finding that priming leads to faster responding and suggests that HCD is a unique type…
Earth Observation System Flight Dynamics System Covariance Realism
NASA Technical Reports Server (NTRS)
Zaidi, Waqar H.; Tracewell, David
2016-01-01
This presentation applies a covariance realism technique to the National Aeronautics and Space Administration (NASA) Earth Observation System (EOS) Aqua and Aura spacecraft based on inferential statistics. The technique consists of three parts: collection calculation of definitive state estimates through orbit determination, calculation of covariance realism test statistics at each covariance propagation point, and proper assessment of those test statistics.
Covariate Selection in Propensity Scores Using Outcome Proxies
ERIC Educational Resources Information Center
Kelcey, Ben
2011-01-01
This study examined the practical problem of covariate selection in propensity scores (PSs) given a predetermined set of covariates. Because the bias reduction capacity of a confounding covariate is proportional to the concurrent relationships it has with the outcome and treatment, particular focus is set on how we might approximate…
Hoglund, Wendy L G; Klingle, Kirsten E; Hosan, Naheed E
2015-10-01
The current paper presents two related sets of findings on the classroom context in high needs elementary schools. First, we investigated change over one school term in teacher burnout (emotional exhaustion, depersonalization, personal accomplishment) and classroom quality (emotional and instructional support, organization) and assessed the degree to which burnout and classroom quality co-varied over the term with each other and with aggregate externalizing behaviors (average child externalizing behaviors in the classroom). These analyses describe the classroom context in which the children are nested. Second, we examined change over one school term in children's social adjustment (relationship quality with teachers and friends) and academic adjustment (school engagement, literacy skills) and assessed how adjustment co-varied over time with child externalizing behaviors and was predicted by teacher burnout, classroom quality and aggregate externalizing behaviors. These models were tested with a sample of low-income, ethnically diverse children in kindergarten to grade 3 and their teachers. The children and teachers were assessed three times over one school term. Personal accomplishment co-varied positively with overall classroom quality. Reciprocally, classroom organization co-varied positively with overall teacher burnout. Aggregate externalizing behaviors co-varied positively with depersonalization and negatively with personal accomplishment and overall classroom quality, including emotional support and organization. In turn, teacher burnout interacted with aggregate externalizing behaviors to predict change in child social and academic adjustment. Alternatively, classroom quality interacted with aggregate and child externalizing behaviors to predict change in child social and academic adjustment.
A Hybrid Index for Characterizing Drought Based on a Nonparametric Kernel Estimator
Huang, Shengzhi; Huang, Qiang; Leng, Guoyong; Chang, Jianxia
2016-06-01
This study develops a nonparametric multivariate drought index, namely, the Nonparametric Multivariate Standardized Drought Index (NMSDI), by considering the variations of both precipitation and streamflow. Building upon previous efforts in constructing Nonparametric Multivariate Drought Index, we use the nonparametric kernel estimator to derive the joint distribution of precipitation and streamflow, thus providing additional insights in drought index development. The proposed NMSDI are applied in the Wei River Basin (WRB), based on which the drought evolution characteristics are investigated. Results indicate: (1) generally, NMSDI captures the drought onset similar to Standardized Precipitation Index (SPI) and drought termination and persistence similar to Standardized Streamflow Index (SSFI). The drought events identified by NMSDI match well with historical drought records in the WRB. The performances are also consistent with that by an existing Multivariate Standardized Drought Index (MSDI) at various timescales, confirming the validity of the newly constructed NMSDI in drought detections (2) An increasing risk of drought has been detected for the past decades, and will be persistent to a certain extent in future in most areas of the WRB; (3) the identified change points of annual NMSDI are mainly concentrated in the early 1970s and middle 1990s, coincident with extensive water use and soil reservation practices. This study highlights the nonparametric multivariable drought index, which can be used for drought detections and predictions efficiently and comprehensively.
NASA Astrophysics Data System (ADS)
Berge, Léonie; Litaize, Olivier; Serot, Olivier; Archier, Pascal; De Saint Jean, Cyrille; Pénéliau, Yannick; Regnier, David
2016-02-01
As the need for precise handling of nuclear data covariances grows ever stronger, no information about covariances of prompt fission neutron spectra (PFNS) are available in the evaluated library JEFF-3.2, although present in ENDF/B-VII.1 and JENDL-4.0 libraries for the main fissile isotopes. The aim of this work is to provide an estimation of covariance matrices related to PFNS, in the frame of some commonly used models for the evaluated files, such as the Maxwellian spectrum, the Watt spectrum, or the Madland-Nix spectrum. The evaluation of PFNS through these models involves an adjustment of model parameters to available experimental data, and the calculation of the spectrum variance-covariance matrix arising from experimental uncertainties. We present the results for thermal neutron induced fission of 235U. The systematic experimental uncertainties are propagated via the marginalization technique available in the CONRAD code. They are of great influence on the final covariance matrix, and therefore, on the spectrum uncertainty band width. In addition to this covariance estimation work, we have also investigated the importance on a reactor calculation of the fission spectrum model choice. A study of the vessel fluence depending on the PFNS model is presented. This is done through the propagation of neutrons emitted from a fission source in a simplified PWR using the TRIPOLI-4® code. This last study includes thermal fission spectra from the FIFRELIN Monte-Carlo code dedicated to the simulation of prompt particles emission during fission.
USE OF HISTORICAL CONTROLS TO ADJUST FOR COVARIATES IN TREND TESTS FOR BINARY DATA. (R824757)
The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Concl...
Arai, Kaori; Nakagawa, Yui; Iwata, Toyoto; Horiguchi, Hyogo; Murata, Katsuyuki
2013-01-01
To clarify the links between ECG QT-related parameters and heart rate variability (HRV) and the covariates possibly distorting them, the averaged RR and QT intervals in a single lead ECG were measured for 64 male and 86 female subjects aged 18-26. The QT index, defined by Rautaharju et al., in the young adults was not significantly related to any HRV parameters nor heart rate, but the Bazett's corrected QT (QTc) interval was associated negatively with the parasympathetic activity and positively with heart rate. No significant differences in the QTc interval, QT index or heart rate were seen between the men and women, but they significantly differed between both sexes after adjustment for possible covariates such as age and body mass index (BMI). Significant sex differences in parasympathetic parameters of the HRV were unchanged before and after the adjustment, but significant differences observed in the unadjusted sympathetic parameters disappeared after adjusting for covariates. Age, BMI and body fat percentage also were significant covariates affecting these ECG parameters. Consequently, QT index, unaffected by heart rate and HRV parameters, appears to be a more useful indicator than the QTc interval. Instead, the QT index and HRV parameters are recommended to be simultaneously measured in epidemiological research because they are probably complementary in assessing autonomic nervous function. Also, these parameters should be analyzed in men and women separately.
Non-parametric seismic hazard analysis in the presence of incomplete data
NASA Astrophysics Data System (ADS)
Yazdani, Azad; Mirzaei, Sajjad; Dadkhah, Koroush
2017-01-01
The distribution of earthquake magnitudes plays a crucial role in the estimation of seismic hazard parameters. Due to the complexity of earthquake magnitude distribution, non-parametric approaches are recommended over classical parametric methods. The main deficiency of the non-parametric approach is the lack of complete magnitude data in almost all cases. This study aims to introduce an imputation procedure for completing earthquake catalog data that will allow the catalog to be used for non-parametric density estimation. Using a Monte Carlo simulation, the efficiency of introduced approach is investigated. This study indicates that when a magnitude catalog is incomplete, the imputation procedure can provide an appropriate tool for seismic hazard assessment. As an illustration, the imputation procedure was applied to estimate earthquake magnitude distribution in Tehran, the capital city of Iran.
Mathematical models for non-parametric inferences from line transect data
Burnham, K.P.; Anderson, D.R.
1976-01-01
A general mathematical theory of line transects is developed which supplies a framework for nonparametric density estimation based on either right angle or sighting distances. The probability of observing a point given its right angle distance (y) from the line is generalized to an arbitrary function g(y). Given only that g(0) = 1, it is shown there are nonparametric approaches to density estimation using the observed right angle distances. The model is then generalized to include sighting distances (r). Let f(y I r) be the conditional distribution of right angle distance given sighting distance. It is shown that nonparametric estimation based only on sighting distances requires we know the transformation of r given by f(0 I r).
Nonparametric Simulation of Signal Transduction Networks with Semi-Synchronized Update
Nassiri, Isar; Masoudi-Nejad, Ali; Jalili, Mahdi; Moeini, Ali
2012-01-01
Simulating signal transduction in cellular signaling networks provides predictions of network dynamics by quantifying the changes in concentration and activity-level of the individual proteins. Since numerical values of kinetic parameters might be difficult to obtain, it is imperative to develop non-parametric approaches that combine the connectivity of a network with the response of individual proteins to signals which travel through the network. The activity levels of signaling proteins computed through existing non-parametric modeling tools do not show significant correlations with the observed values in experimental results. In this work we developed a non-parametric computational framework to describe the profile of the evolving process and the time course of the proportion of active form of molecules in the signal transduction networks. The model is also capable of incorporating perturbations. The model was validated on four signaling networks showing that it can effectively uncover the activity levels and trends of response during signal transduction process. PMID:22737250
A computer program for linear nonparametric and parametric identification of biological data.
Werness, S A; Anderson, D J
1984-01-01
A computer program package for parametric ad nonparametric linear system identification of both static and dynamic biological data, written for an LSI-11 minicomputer with 28 K of memory, is described. The program has 11 possible commands including an instructional help command. A user can perform nonparametric spectral analysis and estimation of autocorrelation and partial autocorrelation functions of univariate data and estimate nonparametrically the transfer function and possibly an associated noise series of bivariate data. In addition, the commands provide the user the means to derive a parametric autoregressive moving average model for univariate data, to derive a parametric transfer function and noise model for bivariate data, and to perform several model evaluation tests such as pole-zero cancellation, examination of residual whiteness and uncorrelatedness with the input. The program, consisting of a main program and driver subroutine as well as six overlay segments, may be run interactively or automatically.
Lasso adjustments of treatment effect estimates in randomized experiments
Bloniarz, Adam; Liu, Hanzhong; Zhang, Cun-Hui; Sekhon, Jasjeet S.; Yu, Bin
2016-01-01
We provide a principled way for investigators to analyze randomized experiments when the number of covariates is large. Investigators often use linear multivariate regression to analyze randomized experiments instead of simply reporting the difference of means between treatment and control groups. Their aim is to reduce the variance of the estimated treatment effect by adjusting for covariates. If there are a large number of covariates relative to the number of observations, regression may perform poorly because of overfitting. In such cases, the least absolute shrinkage and selection operator (Lasso) may be helpful. We study the resulting Lasso-based treatment effect estimator under the Neyman–Rubin model of randomized experiments. We present theoretical conditions that guarantee that the estimator is more efficient than the simple difference-of-means estimator, and we provide a conservative estimator of the asymptotic variance, which can yield tighter confidence intervals than the difference-of-means estimator. Simulation and data examples show that Lasso-based adjustment can be advantageous even when the number of covariates is less than the number of observations. Specifically, a variant using Lasso for selection and ordinary least squares (OLS) for estimation performs particularly well, and it chooses a smoothing parameter based on combined performance of Lasso and OLS. PMID:27382153
On regression adjustment for the propensity score.
Vansteelandt, S; Daniel, R M
2014-10-15
Propensity scores are widely adopted in observational research because they enable adjustment for high-dimensional confounders without requiring models for their association with the outcome of interest. The results of statistical analyses based on stratification, matching or inverse weighting by the propensity score are therefore less susceptible to model extrapolation than those based solely on outcome regression models. This is attractive because extrapolation in outcome regression models may be alarming, yet difficult to diagnose, when the exposed and unexposed individuals have very different covariate distributions. Standard regression adjustment for the propensity score forms an alternative to the aforementioned propensity score methods, but the benefits of this are less clear because it still involves modelling the outcome in addition to the propensity score. In this article, we develop novel insights into the properties of this adjustment method. We demonstrate that standard tests of the null hypothesis of no exposure effect (based on robust variance estimators), as well as particular standardised effects obtained from such adjusted regression models, are robust against misspecification of the outcome model when a propensity score model is correctly specified; they are thus not vulnerable to the aforementioned problem of extrapolation. We moreover propose efficient estimators for these standardised effects, which retain a useful causal interpretation even when the propensity score model is misspecified, provided the outcome regression model is correctly specified.
Linear Covariance Analysis and Epoch State Estimators
NASA Technical Reports Server (NTRS)
Markley, F. Landis; Carpenter, J. Russell
2012-01-01
This paper extends in two directions the results of prior work on generalized linear covariance analysis of both batch least-squares and sequential estimators. The first is an improved treatment of process noise in the batch, or epoch state, estimator with an epoch time that may be later than some or all of the measurements in the batch. The second is to account for process noise in specifying the gains in the epoch state estimator. We establish the conditions under which the latter estimator is equivalent to the Kalman filter.
Cosmology of a covariant Galilean field.
De Felice, Antonio; Tsujikawa, Shinji
2010-09-10
We study the cosmology of a covariant scalar field respecting a Galilean symmetry in flat space-time. We show the existence of a tracker solution that finally approaches a de Sitter fixed point responsible for cosmic acceleration today. The viable region of model parameters is clarified by deriving conditions under which ghosts and Laplacian instabilities of scalar and tensor perturbations are absent. The field equation of state exhibits a peculiar phantomlike behavior along the tracker, which allows a possibility to observationally distinguish the Galileon gravity from the cold dark matter model with a cosmological constant.
Minimal covariant observables identifying all pure states
NASA Astrophysics Data System (ADS)
Carmeli, Claudio; Heinosaari, Teiko; Toigo, Alessandro
2013-09-01
It has been recently shown by Heinosaari, Mazzarella and Wolf (2013) [1] that an observable that identifies all pure states of a d-dimensional quantum system has minimally 4d-4 outcomes or slightly less (the exact number depending on d). However, no simple construction of this type of minimal observable is known. We investigate covariant observables that identify all pure states and have minimal number of outcomes. It is shown that the existence of this kind of observables depends on the dimension of the Hilbert space.
Linear Covariance Analysis and Epoch State Estimators
NASA Technical Reports Server (NTRS)
Markley, F. Landis; Carpenter, J. Russell
2014-01-01
This paper extends in two directions the results of prior work on generalized linear covariance analysis of both batch least-squares and sequential estimators. The first is an improved treatment of process noise in the batch, or epoch state, estimator with an epoch time that may be later than some or all of the measurements in the batch. The second is to account for process noise in specifying the gains in the epoch state estimator. We establish the conditions under which the latter estimator is equivalent to the Kalman filter.
Covariant harmonic oscillators and coupled harmonic oscillators
NASA Technical Reports Server (NTRS)
Han, Daesoo; Kim, Young S.; Noz, Marilyn E.
1995-01-01
It is shown that the system of two coupled harmonic oscillators shares the basic symmetry properties with the covariant harmonic oscillator formalism which provides a concise description of the basic features of relativistic hadronic features observed in high-energy laboratories. It is shown also that the coupled oscillator system has the SL(4,r) symmetry in classical mechanics, while the present formulation of quantum mechanics can accommodate only the Sp(4,r) portion of the SL(4,r) symmetry. The possible role of the SL(4,r) symmetry in quantum mechanics is discussed.
Covariant change of signature in classical relativity
NASA Astrophysics Data System (ADS)
Ellis, G. F. R.
1992-10-01
This paper gives a covariant formalism enabling investigation of the possibility of change of signature in classical General Relativity, when the geometry is that of a Robertson-Walker universe. It is shown that such changes are compatible with the Einstein field equations, both in the case of a barotropic fluid and of a scalar field. A criterion is given for when such a change of signature should take place in the scalar field case. Some examples show the kind of resulting exact solutions of the field equations.
Zhao, Zhibiao
2011-06-01
We address the nonparametric model validation problem for hidden Markov models with partially observable variables and hidden states. We achieve this goal by constructing a nonparametric simultaneous confidence envelope for transition density function of the observable variables and checking whether the parametric density estimate is contained within such an envelope. Our specification test procedure is motivated by a functional connection between the transition density of the observable variables and the Markov transition kernel of the hidden states. Our approach is applicable for continuous time diffusion models, stochastic volatility models, nonlinear time series models, and models with market microstructure noise.
Nonparametric maximum likelihood estimation of probability densities by penalty function methods
NASA Technical Reports Server (NTRS)
Demontricher, G. F.; Tapia, R. A.; Thompson, J. R.
1974-01-01
When it is known a priori exactly to which finite dimensional manifold the probability density function gives rise to a set of samples, the parametric maximum likelihood estimation procedure leads to poor estimates and is unstable; while the nonparametric maximum likelihood procedure is undefined. A very general theory of maximum penalized likelihood estimation which should avoid many of these difficulties is presented. It is demonstrated that each reproducing kernel Hilbert space leads, in a very natural way, to a maximum penalized likelihood estimator and that a well-known class of reproducing kernel Hilbert spaces gives polynomial splines as the nonparametric maximum penalized likelihood estimates.
Smooth Nonparametric Estimation of the Failure Rate Function and its First Two Derivatives
NASA Astrophysics Data System (ADS)
Koshkin, G. M.
2016-10-01
The class of nonparametric estimators of kernel type is considered for the unknown failure rate function and its derivatives. The convergence of the suggested estimations in distribution and in the mean square sense to the unknown failure rate function and its derivatives is proved. The interval estimator of the failure rate function is constructed. Advantages of the nonparametric estimators in comparison with the parametric algorithms are discussed. The suggested estimators of the failure rate function can be used to solve problems of exploitation reliability of complex physical, technical, and software systems under uncertainty conditions.
Nonparametric model validations for hidden Markov models with applications in financial econometrics
Zhao, Zhibiao
2011-01-01
We address the nonparametric model validation problem for hidden Markov models with partially observable variables and hidden states. We achieve this goal by constructing a nonparametric simultaneous confidence envelope for transition density function of the observable variables and checking whether the parametric density estimate is contained within such an envelope. Our specification test procedure is motivated by a functional connection between the transition density of the observable variables and the Markov transition kernel of the hidden states. Our approach is applicable for continuous time diffusion models, stochastic volatility models, nonlinear time series models, and models with market microstructure noise. PMID:21750601
Bayesian non-parametric inference for stochastic epidemic models using Gaussian Processes
Xu, Xiaoguang; Kypraios, Theodore; O'Neill, Philip D.
2016-01-01
This paper considers novel Bayesian non-parametric methods for stochastic epidemic models. Many standard modeling and data analysis methods use underlying assumptions (e.g. concerning the rate at which new cases of disease will occur) which are rarely challenged or tested in practice. To relax these assumptions, we develop a Bayesian non-parametric approach using Gaussian Processes, specifically to estimate the infection process. The methods are illustrated with both simulated and real data sets, the former illustrating that the methods can recover the true infection process quite well in practice, and the latter illustrating that the methods can be successfully applied in different settings. PMID:26993062
Spatiotemporal noise covariance estimation from limited empirical magnetoencephalographic data.
Jun, Sung C; Plis, Sergey M; Ranken, Doug M; Schmidt, David M
2006-11-07
The performance of parametric magnetoencephalography (MEG) and electroencephalography (EEG) source localization approaches can be degraded by the use of poor background noise covariance estimates. In general, estimation of the noise covariance for spatiotemporal analysis is difficult mainly due to the limited noise information available. Furthermore, its estimation requires a large amount of storage and a one-time but very large (and sometimes intractable) calculation or its inverse. To overcome these difficulties, noise covariance models consisting of one pair or a sum of multi-pairs of Kronecker products of spatial covariance and temporal covariance have been proposed. However, these approaches cannot be applied when the noise information is very limited, i.e., the amount of noise information is less than the degrees of freedom of the noise covariance models. A common example of this is when only averaged noise data are available for a limited prestimulus region (typically at most a few hundred milliseconds duration). For such cases, a diagonal spatiotemporal noise covariance model consisting of sensor variances with no spatial or temporal correlation has been the common choice for spatiotemporal analysis. In this work, we propose a different noise covariance model which consists of diagonal spatial noise covariance and Toeplitz temporal noise covariance. It can easily be estimated from limited noise information, and no time-consuming optimization and data-processing are required. Thus, it can be used as an alternative choice when one-pair or multi-pair noise covariance models cannot be estimated due to lack of noise information. To verify its capability we used Bayesian inference dipole analysis and a number of simulated and empirical datasets. We compared this covariance model with other existing covariance models such as conventional diagonal covariance, one-pair and multi-pair noise covariance models, when noise information is sufficient to estimate them. We
High-resolution cortical dipole layer imaging based on noise covariance matrix.
Hori, Junichi; Watanabe, Satoru
2009-01-01
We have investigated the suitable spatial filters for inverse estimation of cortical dipole imaging from the scalp electroencephalogram. The effects of incorporating statistical information of noise into inverse procedures were examined by computer simulations and experimental studies. The parametric projection filter (PPF) was applied to an inhomogeneous three-sphere volume conductor head model. The noise covariance matrix was estimated by applying independent component analysis (ICA) to the scalp potentials. Moreover, the sampling method of the noise information was examined for calculating the noise covariance matrix. The simulation results suggest that the spatial resolution was improved while the effect of noise was suppressed by including the separated noise at the time instant of imaging and by adjusting the number of samples according to the signal to noise ratio.
Noisy covariance matrices and portfolio optimization
NASA Astrophysics Data System (ADS)
Pafka, S.; Kondor, I.
2002-05-01
According to recent findings [#!bouchaud!#,#!stanley!#], empirical covariance matrices deduced from financial return series contain such a high amount of noise that, apart from a few large eigenvalues and the corresponding eigenvectors, their structure can essentially be regarded as random. In [#!bouchaud!#], e.g., it is reported that about 94% of the spectrum of these matrices can be fitted by that of a random matrix drawn from an appropriately chosen ensemble. In view of the fundamental role of covariance matrices in the theory of portfolio optimization as well as in industry-wide risk management practices, we analyze the possible implications of this effect. Simulation experiments with matrices having a structure such as described in [#!bouchaud!#,#!stanley!#] lead us to the conclusion that in the context of the classical portfolio problem (minimizing the portfolio variance under linear constraints) noise has relatively little effect. To leading order the solutions are determined by the stable, large eigenvalues, and the displacement of the solution (measured in variance) due to noise is rather small: depending on the size of the portfolio and on the length of the time series, it is of the order of 5 to 15%. The picture is completely different, however, if we attempt to minimize the variance under non-linear constraints, like those that arise e.g. in the problem of margin accounts or in international capital adequacy regulation. In these problems the presence of noise leads to a serious instability and a high degree of degeneracy of the solutions.
Covariant perturbations in a multifluid cosmological medium
NASA Astrophysics Data System (ADS)
Dunsby, Peter K. S.; Bruni, Marco; Ellis, George F. R.
1992-08-01
In a series of recent papers, a new covariant formalism was introduced to treat inhomogeneities in any spacetime. The variables introduced in these papers are gauge-invariant with respect to a Robertson-Walker background spacetime because they vanish identically in such models, and they have a transparent physical meaning. Exact evolution equations were found for these variables, and the linearized form of these equations were obtained, showing that they give the standard results for a barotropic perfect fluid. In this paper we extend this formalism to the general case of multicomponent fluid sources with interactions between them. We show, using the tilted formalism of King and Ellis, (1973) that choosing either the energy frame or the particle frame gives rise to a set of physically well-defined covariant and gauge-invariant variables which describe density and velocity perturbations, both for the total fluid and its constituent components. We then derive a complete set of equations for these variables and show, through harmonic analysis, that they are equivalent to those of Bardeen (1980) and of Kodama and Sasaki (1984). We discuss a number of interesting applications, including the case where the universe is filled with a mixture of baryons and radiation, coupled through Thomson scattering, and we derive solutions for the density and velocity perturbations in the large-scale limit. We also correct a number of errors in the previous literature.
Modeling Covariance Matrices via Partial Autocorrelations
Daniels, M.J.; Pourahmadi, M.
2009-01-01
Summary We study the role of partial autocorrelations in the reparameterization and parsimonious modeling of a covariance matrix. The work is motivated by and tries to mimic the phenomenal success of the partial autocorrelations function (PACF) in model formulation, removing the positive-definiteness constraint on the autocorrelation function of a stationary time series and in reparameterizing the stationarity-invertibility domain of ARMA models. It turns out that once an order is fixed among the variables of a general random vector, then the above properties continue to hold and follows from establishing a one-to-one correspondence between a correlation matrix and its associated matrix of partial autocorrelations. Connections between the latter and the parameters of the modified Cholesky decomposition of a covariance matrix are discussed. Graphical tools similar to partial correlograms for model formulation and various priors based on the partial autocorrelations are proposed. We develop frequentist/Bayesian procedures for modelling correlation matrices, illustrate them using a real dataset, and explore their properties via simulations. PMID:20161018
On-line estimation of error covariance parameters for atmospheric data assimilation
NASA Technical Reports Server (NTRS)
Dee, Dick P.
1995-01-01
A simple scheme is presented for on-line estimation of covariance parameters in statistical data assimilation systems. The scheme is based on a maximum-likelihood approach in which estimates are produced on the basis of a single batch of simultaneous observations. Simple-sample covariance estimation is reasonable as long as the number of available observations exceeds the number of tunable parameters by two or three orders of magnitude. Not much is known at present about model error associated with actual forecast systems. Our scheme can be used to estimate some important statistical model error parameters such as regionally averaged variances or characteristic correlation length scales. The advantage of the single-sample approach is that it does not rely on any assumptions about the temporal behavior of the covariance parameters: time-dependent parameter estimates can be continuously adjusted on the basis of current observations. This is of practical importance since it is likely to be the case that both model error and observation error strongly depend on the actual state of the atmosphere. The single-sample estimation scheme can be incorporated into any four-dimensional statistical data assimilation system that involves explicit calculation of forecast error covariances, including optimal interpolation (OI) and the simplified Kalman filter (SKF). The computational cost of the scheme is high but not prohibitive; on-line estimation of one or two covariance parameters in each analysis box of an operational bozed-OI system is currently feasible. A number of numerical experiments performed with an adaptive SKF and an adaptive version of OI, using a linear two-dimensional shallow-water model and artificially generated model error are described. The performance of the nonadaptive versions of these methods turns out to depend rather strongly on correct specification of model error parameters. These parameters are estimated under a variety of conditions, including
ERIC Educational Resources Information Center
Kantabutra, Sangchan
2009-01-01
This paper examines urban-rural effects on public upper-secondary school efficiency in northern Thailand. In the study, efficiency was measured by a nonparametric technique, data envelopment analysis (DEA). Urban-rural effects were examined through a Mann-Whitney nonparametric statistical test. Results indicate that urban schools appear to have…
1986-03-01
GROUP M/G/00 queueing model, simulation, Parametric estimation , Nonparametric estimati on, Paired-jackknife, Separated- jackknife, Bootstrap, 9...12 III. PARAMETRIC ESTIMATION METHOD .... ........... .14 A. DESCRIPTION ...... ................. 14 B. EXPONENTIAL SERVICE TIME...shall call parametric estimation " and "nonparametric estimation". In the parametric estimation case, a particular probabilistic model is specified for the
Palmiotti, Giuseppe; Salvatores, Massimo
2014-04-01
The Working Party on International Nuclear Data Evaluation Cooperation (WPEC) of the Nuclear Science Committee under the Nuclear Energy Agency (NEA/OECD) established a Subgroup (called “Subgroup 33”) in 2009 on “Methods and issues for the combined use of integral experiments and covariance data.” The first stage was devoted to producing the description of different adjustment methodologies and assessing their merits. A detailed document related to this first stage has been issued. Nine leading organizations (often with a long and recognized expertise in the field) have contributed: ANL, CEA, INL, IPPE, JAEA, JSI, NRG, IRSN and ORNL. In the second stage a practical benchmark exercise was defined in order to test the reliability of the nuclear data adjustment methodology. A comparison of the results obtained by the participants and major lessons learned in the exercise are discussed in the present paper that summarizes individual contributions which often include several original developments not reported separately. The paper provides the analysis of the most important results of the adjustment of the main nuclear data of 11 major isotopes in a 33-group energy structure. This benchmark exercise was based on a set of 20 well defined integral parameters from 7 fast assembly experiments. The exercise showed that using a common shared set of integral experiments but different starting evaluated libraries and/or different covariance matrices, there is a good convergence of trends for adjustments. Moreover, a significant reduction of the original uncertainties is often observed. Using the a–posteriori covariance data, there is a strong reduction of the uncertainties of integral parameters for reference reactor designs, mainly due to the new correlations in the a–posteriori covariance matrix. Furthermore, criteria have been proposed and applied to verify the consistency of differential and integral data used in the adjustment. Finally, recommendations are given
A Class of Population Covariance Matrices in the Bootstrap Approach to Covariance Structure Analysis
ERIC Educational Resources Information Center
Yuan, Ke-Hai; Hayashi, Kentaro; Yanagihara, Hirokazu
2007-01-01
Model evaluation in covariance structure analysis is critical before the results can be trusted. Due to finite sample sizes and unknown distributions of real data, existing conclusions regarding a particular statistic may not be applicable in practice. The bootstrap procedure automatically takes care of the unknown distribution and, for a given…
Gaskins, J T; Daniels, M J
2016-01-02
The estimation of the covariance matrix is a key concern in the analysis of longitudinal data. When data consists of multiple groups, it is often assumed the covariance matrices are either equal across groups or are completely distinct. We seek methodology to allow borrowing of strength across potentially similar groups to improve estimation. To that end, we introduce a covariance partition prior which proposes a partition of the groups at each measurement time. Groups in the same set of the partition share dependence parameters for the distribution of the current measurement given the preceding ones, and the sequence of partitions is modeled as a Markov chain to encourage similar structure at nearby measurement times. This approach additionally encourages a lower-dimensional structure of the covariance matrices by shrinking the parameters of the Cholesky decomposition toward zero. We demonstrate the performance of our model through two simulation studies and the analysis of data from a depression study. This article includes Supplementary Material available online.
Evaluating covariance in prognostic and system health management applications
NASA Astrophysics Data System (ADS)
Menon, Sandeep; Jin, Xiaohang; Chow, Tommy W. S.; Pecht, Michael
2015-06-01
Developing a diagnostic and prognostic health management system involves analyzing system parameters monitored during the lifetime of the system. This data analysis may involve multiple steps, including data reduction, feature extraction, clustering and classification, building control charts, identification of anomalies, and modeling and predicting parameter degradation in order to evaluate the state of health for the system under investigation. Evaluating the covariance between the monitored system parameters allows for better understanding of the trends in monitored system data, and therefore it is an integral part of the data analysis. Typically, a sample covariance matrix is used to evaluate the covariance between monitored system parameters. The monitored system data are often sensor data, which are inherently noisy. The noise in sensor data can lead to inaccurate evaluation of the covariance in data using a sample covariance matrix. This paper examines approaches to evaluate covariance, including the minimum volume ellipsoid, the minimum covariance determinant, and the nearest neighbor variance estimation. When the performance of these approaches was evaluated on datasets with increasing percentage of Gaussian noise, it was observed that the nearest neighbor variance estimation exhibited the most stable estimates of covariance. To improve the accuracy of covariance estimates using nearest neighbor-based methodology, a modified approach for the nearest neighbor variance estimation technique is developed in this paper. Case studies based on data analysis steps involved in prognostic solutions are developed in order to compare the performance of the covariance estimation methodologies discussed in the paper.
Impact of the 235U Covariance Data in Benchmark Calculations
Leal, Luiz C; Mueller, Don; Arbanas, Goran; Wiarda, Dorothea; Derrien, Herve
2008-01-01
The error estimation for calculated quantities relies on nuclear data uncertainty information available in the basic nuclear data libraries such as the U.S. Evaluated Nuclear Data File (ENDF/B). The uncertainty files (covariance matrices) in the ENDF/B library are generally obtained from analysis of experimental data. In the resonance region, the computer code SAMMY is used for analyses of experimental data and generation of resonance parameters. In addition to resonance parameters evaluation, SAMMY also generates resonance parameter covariance matrices (RPCM). SAMMY uses the generalized least-squares formalism (Bayes method) together with the resonance formalism (R-matrix theory) for analysis of experimental data. Two approaches are available for creation of resonance-parameter covariance data. (1) During the data-evaluation process, SAMMY generates both a set of resonance parameters that fit the experimental data and the associated resonance-parameter covariance matrix. (2) For existing resonance-parameter evaluations for which no resonance-parameter covariance data are available, SAMMY can retroactively create a resonance-parameter covariance matrix. The retroactive method was used to generate covariance data for 235U. The resulting 235U covariance matrix was then used as input to the PUFF-IV code, which processed the covariance data into multigroup form, and to the TSUNAMI code, which calculated the uncertainty in the multiplication factor due to uncertainty in the experimental cross sections. The objective of this work is to demonstrate the use of the 235U covariance data in calculations of critical benchmark systems.
Sartori, E.
1992-12-31
This paper presents a brief review of computer codes concerned with checking, plotting, processing and using of covariances of neutron cross-section data. It concentrates on those available from the computer code information centers of the United States and the OECD/Nuclear Energy Agency. Emphasis will be placed also on codes using covariances for specific applications such as uncertainty analysis, data adjustment and data consistency analysis. Recent evaluations contain neutron cross section covariance information for all isotopes of major importance for technological applications of nuclear energy. It is therefore important that the available software tools needed for taking advantage of this information are widely known as hey permit the determination of better safety margins and allow the optimization of more economic, I designs of nuclear energy systems.
The Impact of Covariate Measurement Error on Risk Prediction
Khudyakov, Polyna; Gorfine, Malka; Zucker, David; Spiegelman, Donna
2015-01-01
In the development of risk prediction models, predictors are often measured with error. In this paper, we investigate the impact of covariate measurement error on risk prediction. We compare the prediction performance using a costly variable measured without error, along with error-free covariates, to that of a model based on an inexpensive surrogate along with the error-free covariates. We consider continuous error-prone covariates with homoscedastic and heteroscedastic errors, and also a discrete misclassified covariate. Prediction performance is evaluated by the area under the receiver operating characteristic curve (AUC), the Brier score (BS), and the ratio of the observed to the expected number of events (calibration). In an extensive numerical study, we show that (i) the prediction model with the error-prone covariate is very well calibrated, even when it is mis-specified; (ii) using the error-prone covariate instead of the true covariate can reduce the AUC and increase the BS dramatically; (iii) adding an auxiliary variable, which is correlated with the error-prone covariate but conditionally independent of the outcome given all covariates in the true model, can improve the AUC and BS substantially. We conclude that reducing measurement error in covariates will improve the ensuing risk prediction, unless the association between the error-free and error-prone covariates is very high. Finally, we demonstrate how a validation study can be used to assess the effect of mismeasured covariates on risk prediction. These concepts are illustrated in a breast cancer risk prediction model developed in the Nurses’ Health Study. PMID:25865315
Least squares regression methods for clustered ROC data with discrete covariates.
Tang, Liansheng Larry; Zhang, Wei; Li, Qizhai; Ye, Xuan; Chan, Leighton
2016-07-01
The receiver operating characteristic (ROC) curve is a popular tool to evaluate and compare the accuracy of diagnostic tests to distinguish the diseased group from the nondiseased group when test results from tests are continuous or ordinal. A complicated data setting occurs when multiple tests are measured on abnormal and normal locations from the same subject and the measurements are clustered within the subject. Although least squares regression methods can be used for the estimation of ROC curve from correlated data, how to develop the least squares methods to estimate the ROC curve from the clustered data has not been studied. Also, the statistical properties of the least squares methods under the clustering setting are unknown. In this article, we develop the least squares ROC methods to allow the baseline and link functions to differ, and more importantly, to accommodate clustered data with discrete covariates. The methods can generate smooth ROC curves that satisfy the inherent continuous property of the true underlying curve. The least squares methods are shown to be more efficient than the existing nonparametric ROC methods under appropriate model assumptions in simulation studies. We apply the methods to a real example in the detection of glaucomatous deterioration. We also derive the asymptotic properties of the proposed methods.
Nonparametric Estimation of the Plausibility Functions of the Distractors of Vocabulary Test Items.
ERIC Educational Resources Information Center
Samejima, Fumiko
1994-01-01
The Level-11 vocabulary subtest of the Iowa Tests of Basic Skills was analyzed using a two-stage latent trait approach and data set of 2,356 examinees, approximately 11 years of age. It is concluded that the nonparametric approach leads to efficient estimation of the latent trait. (SLD)
Increasing the Power of Nonparametric Tests by Detecting and Downweighting Outliers.
ERIC Educational Resources Information Center
Zimmerman, Donald W.
1995-01-01
It is argued that outlier-prone distributions reduce the power of nonparametric tests, but power can be restored through procedures usually associated with parametric tests. Computer simulation is used to show how an outlier detection and downweighting procedure augments the power of the t-test and the Wilcoxon-Mann-Whitney test. (SLD)
Comparison of nonparametric trend analysis according to the types of time series data
NASA Astrophysics Data System (ADS)
Heo, J.; Shin, H.; Kim, T.; Jang, H.; Kim, H.
2013-12-01
In the analysis of hydrological data, the determination of the existence of overall trend due to climate change has been a major concern and the important part of design and management of water resources for the future. The existence of trend could be identified by plotting hydrologic time series. However, statistical methods are more accurate and objective tools to perform trend analysis. Statistical methods divided into parametric and nonparametric methods. In the case of parametric method, the population should be assumed to be normally distributed. However, most of hydrological data tend to be represented by non-normal distribution, then the nonparametric method considered more suitable than parametric method. In this study, simulations were performed with different types of time series data and four nonparametric methods (Mann-Kendall test, Spearman's rho test, SEN test, and Hotelling-Pabst test) generally used in trend analysis were applied to assess the power of each trend analysis. The time series data were classified into three types which are Trend+Random, Trend+Cycle+Random, and Trend+Non-random. In order to add a change to the data, 11 kinds of different slopes were overlapped at each simulation. As the results, nonparametric methods have almost similar power for Trend+random type and Trend+Non-random series. On the other hand, Mann-Kendall and SEN tests have slightly higher power than Spearman's rho and Hotelling-Pabst tests for Trend+Cycle+Random series.
A Comparison of Methods for Nonparametric Estimation of Item Characteristic Curves for Binary Items
ERIC Educational Resources Information Center
Lee, Young-Sun
2007-01-01
This study compares the performance of three nonparametric item characteristic curve (ICC) estimation procedures: isotonic regression, smoothed isotonic regression, and kernel smoothing. Smoothed isotonic regression, employed along with an appropriate kernel function, provides better estimates and also satisfies the assumption of strict…
Measurement Error in Nonparametric Item Response Curve Estimation. Research Report. ETS RR-11-28
ERIC Educational Resources Information Center
Guo, Hongwen; Sinharay, Sandip
2011-01-01
Nonparametric, or kernel, estimation of item response curve (IRC) is a concern theoretically and operationally. Accuracy of this estimation, often used in item analysis in testing programs, is biased when the observed scores are used as the regressor because the observed scores are contaminated by measurement error. In this study, we investigate…
Quintela-del-Río, Alejandro; Francisco-Fernández, Mario
2011-02-01
The study of extreme values and prediction of ozone data is an important topic of research when dealing with environmental problems. Classical extreme value theory is usually used in air-pollution studies. It consists in fitting a parametric generalised extreme value (GEV) distribution to a data set of extreme values, and using the estimated distribution to compute return levels and other quantities of interest. Here, we propose to estimate these values using nonparametric functional data methods. Functional data analysis is a relatively new statistical methodology that generally deals with data consisting of curves or multi-dimensional variables. In this paper, we use this technique, jointly with nonparametric curve estimation, to provide alternatives to the usual parametric statistical tools. The nonparametric estimators are applied to real samples of maximum ozone values obtained from several monitoring stations belonging to the Automatic Urban and Rural Network (AURN) in the UK. The results show that nonparametric estimators work satisfactorily, outperforming the behaviour of classical parametric estimators. Functional data analysis is also used to predict stratospheric ozone concentrations. We show an application, using the data set of mean monthly ozone concentrations in Arosa, Switzerland, and the results are compared with those obtained by classical time series (ARIMA) analysis.
Nonparametric estimation of population density for line transect sampling using FOURIER series
Crain, B.R.; Burnham, K.P.; Anderson, D.R.; Lake, J.L.
1979-01-01
A nonparametric, robust density estimation method is explored for the analysis of right-angle distances from a transect line to the objects sighted. The method is based on the FOURIER series expansion of a probability density function over an interval. With only mild assumptions, a general population density estimator of wide applicability is obtained.
Investigation of a Nonparametric Procedure for Assessing Goodness-of-Fit in Item Response Theory
ERIC Educational Resources Information Center
Wells, Craig S.; Bolt, Daniel M.
2008-01-01
Tests of model misfit are often performed to validate the use of a particular model in item response theory. Douglas and Cohen (2001) introduced a general nonparametric approach for detecting misfit under the two-parameter logistic model. However, the statistical properties of their approach, and empirical comparisons to other methods, have not…
Nonparametric Trajectory Analysis (NTA), a receptor-oriented model, was used to assess the impact of local sources of air pollution at monitoring sites located adjacent to highway I-15 in Las Vegas, NV. Measurements of black carbon, carbon monoxide, nitrogen oxides, and sulfur di...
Testing the Hypothesis of a Homoscedastic Error Term in Simple, Nonparametric Regression
ERIC Educational Resources Information Center
Wilcox, Rand R.
2006-01-01
Consider the nonparametric regression model Y = m(X)+ [tau](X)[epsilon], where X and [epsilon] are independent random variables, [epsilon] has a median of zero and variance [sigma][squared], [tau] is some unknown function used to model heteroscedasticity, and m(X) is an unknown function reflecting some conditional measure of location associated…
ERIC Educational Resources Information Center
Ferrer, Alvaro J. Arce; Wang, Lin
This study compared the classification performance among parametric discriminant analysis, nonparametric discriminant analysis, and logistic regression in a two-group classification application. Field data from an organizational survey were analyzed and bootstrapped for additional exploration. The data were observed to depart from multivariate…
Does Private Tutoring Work? The Effectiveness of Private Tutoring: A Nonparametric Bounds Analysis
ERIC Educational Resources Information Center
Hof, Stefanie
2014-01-01
Private tutoring has become popular throughout the world. However, evidence for the effect of private tutoring on students' academic outcome is inconclusive; therefore, this paper presents an alternative framework: a nonparametric bounds method. The present examination uses, for the first time, a large representative data-set in a European setting…
Technology Transfer Automated Retrieval System (TEKTRAN)
Parametric non-linear regression (PNR) techniques commonly are used to develop weed seedling emergence models. Such techniques, however, require statistical assumptions that are difficult to meet. To examine and overcome these limitations, we compared PNR with a nonparametric estimation technique. F...
ERIC Educational Resources Information Center
Park, Jungkyu; Yu, Hsiu-Ting
2016-01-01
The multilevel latent class model (MLCM) is a multilevel extension of a latent class model (LCM) that is used to analyze nested structure data structure. The nonparametric version of an MLCM assumes a discrete latent variable at a higher-level nesting structure to account for the dependency among observations nested within a higher-level unit. In…
A Comparative Study of Test Data Dimensionality Assessment Procedures Under Nonparametric IRT Models
ERIC Educational Resources Information Center
van Abswoude, Alexandra A. H.; van der Ark, L. Andries; Sijtsma, Klaas
2004-01-01
In this article, an overview of nonparametric item response theory methods for determining the dimensionality of item response data is provided. Four methods were considered: MSP, DETECT, HCA/CCPROX, and DIMTEST. First, the methods were compared theoretically. Second, a simulation study was done to compare the effectiveness of MSP, DETECT, and…
Measuring Youth Development: A Nonparametric Cross-Country "Youth Welfare Index"
ERIC Educational Resources Information Center
Chaaban, Jad M.
2009-01-01
This paper develops an empirical methodology for the construction of a synthetic multi-dimensional cross-country comparison of the performance of governments around the world in improving the livelihood of their younger population. The devised "Youth Welfare Index" is based on the nonparametric Data Envelopment Analysis (DEA) methodology and…
A Unified Nonparametric IRT Model for "d"-Dimensional Psychological Test Data ("d"-Isop)
ERIC Educational Resources Information Center
Scheiblechner, Hartmann
2007-01-01
The (univariate) isotonic psychometric (ISOP) model (Scheiblechner, 1995) is a nonparametric IRT model for dichotomous and polytomous (rating scale) psychological test data. A weak subject independence axiom W1 postulates that the subjects are ordered in the same way except for ties (i.e., similarly or isotonically) by all items of a psychological…
A Monte Carlo Comparison of Parametric and Nonparametric Polytomous DIF Detection Methods.
ERIC Educational Resources Information Center
Bolt, Daniel M.
2002-01-01
Compared two parametric procedures for detecting differential item functioning (DIF) using the graded response model (GRM), the GRM-likelihood ratio test and the GRM-differential functioning of items and tests, with a nonparametric DIF detection procedure, Poly-SIBTEST. Monte Carlo simulation results show that Poly-SIBTEST showed the least amount…
Wey, Andrew; Connett, John; Rudser, Kyle
2015-07-01
For estimating conditional survival functions, non-parametric estimators can be preferred to parametric and semi-parametric estimators due to relaxed assumptions that enable robust estimation. Yet, even when misspecified, parametric and semi-parametric estimators can possess better operating characteristics in small sample sizes due to smaller variance than non-parametric estimators. Fundamentally, this is a bias-variance trade-off situation in that the sample size is not large enough to take advantage of the low bias of non-parametric estimation. Stacked survival models estimate an optimally weighted combination of models that can span parametric, semi-parametric, and non-parametric models by minimizing prediction error. An extensive simulation study demonstrates that stacked survival models consistently perform well across a wide range of scenarios by adaptively balancing the strengths and weaknesses of individual candidate survival models. In addition, stacked survival models perform as well as or better than the model selected through cross-validation. Finally, stacked survival models are applied to a well-known German breast cancer study.
Nonparametric Hammerstein model based model predictive control for heart rate regulation.
Su, Steven W; Huang, Shoudong; Wang, Lu; Celler, Branko G; Savkin, Andrey V; Guo, Ying; Cheng, Teddy
2007-01-01
This paper proposed a novel nonparametric model based model predictive control approach for the regulation of heart rate during treadmill exercise. As the model structure of human cardiovascular system is often hard to determine, nonparametric modelling is a more realistic manner to describe complex behaviours of cardiovascular system. This paper presents a new nonparametric Hammerstein model identification approach for heart rate response modelling. Based on the pseudo-random binary sequence experiment data, we decouple the identification of linear dynamic part and input nonlinearity of the Hammerstein system. Correlation analysis is applied to acquire step response of linear dynamic component. Support Vector Regression is adopted to obtain a nonparametric description of the inverse of input static nonlinearity that is utilized to form an approximate linear model of the Hammerstein system. Based on the established model, a model predictive controller under predefined speed and acceleration constraints is designed to achieve safer treadmill exercise. Simulation results show that the proposed control algorithm can achieve optimal heart rate tracking performance under predefined constraints.
The Probability of Exceedance as a Nonparametric Person-Fit Statistic for Tests of Moderate Length
ERIC Educational Resources Information Center
Tendeiro, Jorge N.; Meijer, Rob R.
2013-01-01
To classify an item score pattern as not fitting a nonparametric item response theory (NIRT) model, the probability of exceedance (PE) of an observed response vector x can be determined as the sum of the probabilities of all response vectors that are, at most, as likely as x, conditional on the test's total score. Vector x is to be considered…
An Assessment of the Nonparametric Approach for Evaluating the Fit of Item Response Models
ERIC Educational Resources Information Center
Liang, Tie; Wells, Craig S.; Hambleton, Ronald K.
2014-01-01
As item response theory has been more widely applied, investigating the fit of a parametric model becomes an important part of the measurement process. There is a lack of promising solutions to the detection of model misfit in IRT. Douglas and Cohen introduced a general nonparametric approach, RISE (Root Integrated Squared Error), for detecting…
Identifying sources of uncertainty using covariance analysis
NASA Astrophysics Data System (ADS)
Hyslop, N. P.; White, W. H.
2010-12-01
Atmospheric aerosol monitoring often includes performing multiple analyses on a collected sample. Some common analyses resolve suites of elements or compounds (e.g., spectrometry, chromatography). Concentrations are determined through multi-step processes involving sample collection, physical or chemical analysis, and data reduction. Uncertainties in the individual steps propagate into uncertainty in the calculated concentration. The assumption in most treatments of measurement uncertainty is that errors in the various species concentrations measured in a sample are random and therefore independent of each other. This assumption is often not valid in speciated aerosol data because some errors can be common to multiple species. For example, an error in the sample volume will introduce a common error into all species concentrations determined in the sample, and these errors will correlate with each other. Measurement programs often use paired (collocated) measurements to characterize the random uncertainty in their measurements. Suites of paired measurements provide an opportunity to go beyond the characterization of measurement uncertainties in individual species to examine correlations amongst the measurement uncertainties in multiple species. This additional information can be exploited to distinguish sources of uncertainty that affect all species from those that only affect certain subsets or individual species. Data from the Interagency Monitoring of Protected Visual Environments (IMPROVE) program are used to illustrate these ideas. Nine analytes commonly detected in the IMPROVE network were selected for this analysis. The errors in these analytes can be reasonably modeled as multiplicative, and the natural log of the ratio of concentrations measured on the two samplers provides an approximation of the error. Figure 1 shows the covariation of these log ratios among the different analytes for one site. Covariance is strongest amongst the dust element (Fe, Ca, and
Noisy covariance matrices and portfolio optimization II
NASA Astrophysics Data System (ADS)
Pafka, Szilárd; Kondor, Imre
2003-03-01
Recent studies inspired by results from random matrix theory (Galluccio et al.: Physica A 259 (1998) 449; Laloux et al.: Phys. Rev. Lett. 83 (1999) 1467; Risk 12 (3) (1999) 69; Plerou et al.: Phys. Rev. Lett. 83 (1999) 1471) found that covariance matrices determined from empirical financial time series appear to contain such a high amount of noise that their structure can essentially be regarded as random. This seems, however, to be in contradiction with the fundamental role played by covariance matrices in finance, which constitute the pillars of modern investment theory and have also gained industry-wide applications in risk management. Our paper is an attempt to resolve this embarrassing paradox. The key observation is that the effect of noise strongly depends on the ratio r= n/ T, where n is the size of the portfolio and T the length of the available time series. On the basis of numerical experiments and analytic results for some toy portfolio models we show that for relatively large values of r (e.g. 0.6) noise does, indeed, have the pronounced effect suggested by Galluccio et al. (1998), Laloux et al. (1999) and Plerou et al. (1999) and illustrated later by Laloux et al. (Int. J. Theor. Appl. Finance 3 (2000) 391), Plerou et al. (Phys. Rev. E, e-print cond-mat/0108023) and Rosenow et al. (Europhys. Lett., e-print cond-mat/0111537) in a portfolio optimization context, while for smaller r (around 0.2 or below), the error due to noise drops to acceptable levels. Since the length of available time series is for obvious reasons limited in any practical application, any bound imposed on the noise-induced error translates into a bound on the size of the portfolio. In a related set of experiments we find that the effect of noise depends also on whether the problem arises in asset allocation or in a risk measurement context: if covariance matrices are used simply for measuring the risk of portfolios with a fixed composition rather than as inputs to optimization, the
Spatially covariant theories of a transverse, traceless graviton: Formalism
NASA Astrophysics Data System (ADS)
Khoury, Justin; Miller, Godfrey E. J.; Tolley, Andrew J.
2012-04-01
General relativity is a generally covariant, locally Lorentz covariant theory of two transverse, traceless graviton degrees of freedom. According to a theorem of Hojman, Kuchař, and Teitelboim, modifications of general relativity must either introduce new degrees of freedom or violate the principle of local Lorentz covariance. In this paper, we explore modifications of general relativity that retain the same graviton degrees of freedom, and therefore explicitly break Lorentz covariance. Motivated by cosmology, the modifications of interest maintain explicit spatial covariance. In spatially covariant theories of the graviton, the physical Hamiltonian density obeys an analogue of the renormalization group equation which encodes invariance under flow through the space of conformally equivalent spatial metrics. This paper is dedicated to setting up the formalism of our approach and applying it to a realistic class of theories. Forthcoming work will apply the formalism more generally.
O'Sullivan, Finbarr; Muzi, Mark; Spence, Alexander M.; Mankoff, David M.; O'Sullivan, Janet N.; Fitzgerald, Niall; Newman, George C.; Krohn, Kenneth A.
2009-01-01
Kinetic analysis is used to extract metabolic information from dynamic positron emission tomography (PET) uptake data. The theory of indicator dilutions, developed in the seminal work of Meier and Zierler (1954), provides a probabilistic framework for representation of PET tracer uptake data in terms of a convolution between an arterial input function and a tissue residue. The residue is a scaled survival function associated with tracer residence in the tissue. Nonparametric inference for the residue, a deconvolution problem, provides a novel approach to kinetic analysis—critically one that is not reliant on specific compartmental modeling assumptions. A practical computational technique based on regularized cubic B-spline approximation of the residence time distribution is proposed. Nonparametric residue analysis allows formal statistical evaluation of specific parametric models to be considered. This analysis needs to properly account for the increased flexibility of the nonparametric estimator. The methodology is illustrated using data from a series of cerebral studies with PET and fluorodeoxyglucose (FDG) in normal subjects. Comparisons are made between key functionals of the residue, tracer flux, flow, etc., resulting from a parametric (the standard two-compartment of Phelps et al. 1979) and a nonparametric analysis. Strong statistical evidence against the compartment model is found. Primarily these differences relate to the representation of the early temporal structure of the tracer residence—largely a function of the vascular supply network. There are convincing physiological arguments against the representations implied by the compartmental approach but this is the first time that a rigorous statistical confirmation using PET data has been reported. The compartmental analysis produces suspect values for flow but, notably, the impact on the metabolic flux, though statistically significant, is limited to deviations on the order of 3%–4%. The general
Power series evaluation of transition and covariance matrices.
NASA Technical Reports Server (NTRS)
Bierman, G. J.
1972-01-01
Reexamination power series solutions to the matrix covariance differential equation and the transition differential equation. Truncation error bounds are derived which are computationally attractive and which extend previous results. Polynomial approximations are obtained by exploiting the functional equations satisfied by the transition and covariance matrices. The series-functional equation propagation technique represents a fast and accurate alternative to the numerical integration of the time-invariant transition and covariance equations.
Bayesian hierarchical model for large-scale covariance matrix estimation.
Zhu, Dongxiao; Hero, Alfred O
2007-12-01
Many bioinformatics problems implicitly depend on estimating large-scale covariance matrix. The traditional approaches tend to give rise to high variance and low accuracy due to "overfitting." We cast the large-scale covariance matrix estimation problem into the Bayesian hierarchical model framework, and introduce dependency between covariance parameters. We demonstrate the advantages of our approaches over the traditional approaches using simulations and OMICS data analysis.
A statistical test for the equality of differently adjusted incidence rate ratios.
Hoffmann, Kurt; Pischon, Tobias; Schulz, Mandy; Schulze, Matthias B; Ray, Jennifer; Boeing, Heiner
2008-03-01
An incidence rate ratio (IRR) is a meaningful effect measure in epidemiology if it is adjusted for all important confounders. For evaluation of the impact of adjustment, adjusted IRRs should be compared with crude IRRs. The aim of this methodological study was to present a statistical approach for testing the equality of adjusted and crude IRRs and to derive a confidence interval for the ratio of the two IRRs. The method can be extended to compare two differently adjusted IRRs and, thus, to evaluate the effect of additional adjustment. The method runs immediately on existing software. To illustrate the application of this approach, the authors studied adjusted IRRs for two risk factors of type 2 diabetes using data from the European Prospective Investigation into Cancer and Nutrition-Potsdam Study from 2005. The statistical method described may be helpful as an additional tool for analyzing epidemiologic cohort data and for interpreting results obtained from Cox regression models with adjustment for different covariates.
EMPIRE ULTIMATE EXPANSION: RESONANCES AND COVARIANCES.
HERMAN,M.; MUGHABGHAB, S.F.; OBLOZINSKY, P.; ROCHMAN, D.; PIGNI, M.T.; KAWANO, T.; CAPOTE, R.; ZERKIN, V.; TRKOV, A.; SIN, M.; CARSON, B.V.; WIENKE, H. CHO, Y.-S.
2007-04-22
The EMPIRE code system is being extended to cover the resolved and unresolved resonance region employing proven methodology used for the production of new evaluations in the recent Atlas of Neutron Resonances. Another directions of Empire expansion are uncertainties and correlations among them. These include covariances for cross sections as well as for model parameters. In this presentation we concentrate on the KALMAN method that has been applied in EMPIRE to the fast neutron range as well as to the resonance region. We also summarize role of the EMPIRE code in the ENDF/B-VII.0 development. Finally, large scale calculations and their impact on nuclear model parameters are discussed along with the exciting perspectives offered by the parallel supercomputing.
Covariance of lucky images: performance analysis
NASA Astrophysics Data System (ADS)
Cagigal, Manuel P.; Valle, Pedro J.; Cagigas, Miguel A.; Villó-Pérez, Isidro; Colodro-Conde, Carlos; Ginski, C.; Mugrauer, M.; Seeliger, M.
2017-01-01
The covariance of ground-based lucky images is a robust and easy-to-use algorithm that allows us to detect faint companions surrounding a host star. In this paper, we analyse the relevance of the number of processed frames, the frames' quality, the atmosphere conditions and the detection noise on the companion detectability. This analysis has been carried out using both experimental and computer-simulated imaging data. Although the technique allows us the detection of faint companions, the camera detection noise and the use of a limited number of frames reduce the minimum detectable companion intensity to around 1000 times fainter than that of the host star when placed at an angular distance corresponding to the few first Airy rings. The reachable contrast could be even larger when detecting companions with the assistance of an adaptive optics system.
Conformal killing tensors and covariant Hamiltonian dynamics
Cariglia, M.; Gibbons, G. W.; Holten, J.-W. van; Horvathy, P. A.; Zhang, P.-M.
2014-12-15
A covariant algorithm for deriving the conserved quantities for natural Hamiltonian systems is combined with the non-relativistic framework of Eisenhart, and of Duval, in which the classical trajectories arise as geodesics in a higher dimensional space-time, realized by Brinkmann manifolds. Conserved quantities which are polynomial in the momenta can be built using time-dependent conformal Killing tensors with flux. The latter are associated with terms proportional to the Hamiltonian in the lower dimensional theory and with spectrum generating algebras for higher dimensional quantities of order 1 and 2 in the momenta. Illustrations of the general theory include the Runge-Lenz vector for planetary motion with a time-dependent gravitational constant G(t), motion in a time-dependent electromagnetic field of a certain form, quantum dots, the Hénon-Heiles and Holt systems, respectively, providing us with Killing tensors of rank that ranges from one to six.
Covariant generalization of cosmological perturbation theory
Enqvist, Kari; Hoegdahl, Janne; Nurmi, Sami; Vernizzi, Filippo
2007-01-15
We present an approach to cosmological perturbations based on a covariant perturbative expansion between two worldlines in the real inhomogeneous universe. As an application, at an arbitrary order we define an exact scalar quantity which describes the inhomogeneities in the number of e-folds on uniform density hypersurfaces and which is conserved on all scales for a barotropic ideal fluid. We derive a compact form for its conservation equation at all orders and assign it a simple physical interpretation. To make a comparison with the standard perturbation theory, we develop a method to construct gauge-invariant quantities in a coordinate system at arbitrary order, which we apply to derive the form of the nth order perturbation in the number of e-folds on uniform density hypersurfaces and its exact evolution equation. On large scales, this provides the gauge-invariant expression for the curvature perturbation on uniform density hypersurfaces and its evolution equation at any order.
Covariates of Craving in Actively Drinking Alcoholics
Chakravorty, Subhajit; Kuna, Samuel T.; Zaharakis, Nikola; O’Brien, Charles P.; Kampman, Kyle M.; Oslin, David
2010-01-01
The goal of this cross-sectional study was to assess the relationship of alcohol craving with biopsychosocial and addiction factors that are clinically pertinent to alcoholism treatment. Alcohol craving was assessed in 315 treatment-seeking, alcohol dependent subjects using the PACS questionnaire. Standard validated questionnaires were used to evaluate a variety of biological, addiction, psychological, psychiatric, and social factors. Individual covariates of craving included age, race, problematic consequences of drinking, heavy drinking, motivation for change, mood disturbance, sleep problems, and social supports. In a multivariate analysis (R2 = .34), alcohol craving was positively associated with mood disturbance, heavy drinking, readiness for change, and negatively associated with age. The results from this study suggest that alcohol craving is a complex phenomenon influenced by multiple factors. PMID:20716308
Control Systems with Normalized and Covariance Adaptation by Optimal Control Modification
NASA Technical Reports Server (NTRS)
Nguyen, Nhan T. (Inventor); Burken, John J. (Inventor); Hanson, Curtis E. (Inventor)
2016-01-01
Disclosed is a novel adaptive control method and system called optimal control modification with normalization and covariance adjustment. The invention addresses specifically to current challenges with adaptive control in these areas: 1) persistent excitation, 2) complex nonlinear input-output mapping, 3) large inputs and persistent learning, and 4) the lack of stability analysis tools for certification. The invention has been subject to many simulations and flight testing. The results substantiate the effectiveness of the invention and demonstrate the technical feasibility for use in modern aircraft flight control systems.
Performance of internal covariance estimators for cosmic shear correlation functions
Friedrich, O.; Seitz, S.; Eifler, T. F.; Gruen, D.
2015-12-31
Data re-sampling methods such as the delete-one jackknife are a common tool for estimating the covariance of large scale structure probes. In this paper we investigate the concepts of internal covariance estimation in the context of cosmic shear two-point statistics. We demonstrate how to use log-normal simulations of the convergence field and the corresponding shear field to carry out realistic tests of internal covariance estimators and find that most estimators such as jackknife or sub-sample covariance can reach a satisfactory compromise between bias and variance of the estimated covariance. In a forecast for the complete, 5-year DES survey we show that internally estimated covariance matrices can provide a large fraction of the true uncertainties on cosmological parameters in a 2D cosmic shear analysis. The volume inside contours of constant likelihood in the $\\Omega_m$-$\\sigma_8$ plane as measured with internally estimated covariance matrices is on average $\\gtrsim 85\\%$ of the volume derived from the true covariance matrix. The uncertainty on the parameter combination $\\Sigma_8 \\sim \\sigma_8 \\Omega_m^{0.5}$ derived from internally estimated covariances is $\\sim 90\\%$ of the true uncertainty.
Performance of internal covariance estimators for cosmic shear correlation functions
Friedrich, O.; Seitz, S.; Eifler, T. F.; ...
2015-12-31
Data re-sampling methods such as the delete-one jackknife are a common tool for estimating the covariance of large scale structure probes. In this paper we investigate the concepts of internal covariance estimation in the context of cosmic shear two-point statistics. We demonstrate how to use log-normal simulations of the convergence field and the corresponding shear field to carry out realistic tests of internal covariance estimators and find that most estimators such as jackknife or sub-sample covariance can reach a satisfactory compromise between bias and variance of the estimated covariance. In a forecast for the complete, 5-year DES survey we show that internally estimated covariance matrices can provide a large fraction of the true uncertainties on cosmological parameters in a 2D cosmic shear analysis. The volume inside contours of constant likelihood in themore » $$\\Omega_m$$-$$\\sigma_8$$ plane as measured with internally estimated covariance matrices is on average $$\\gtrsim 85\\%$$ of the volume derived from the true covariance matrix. The uncertainty on the parameter combination $$\\Sigma_8 \\sim \\sigma_8 \\Omega_m^{0.5}$$ derived from internally estimated covariances is $$\\sim 90\\%$$ of the true uncertainty.« less
Remotely Adjustable Hydraulic Pump
NASA Technical Reports Server (NTRS)
Kouns, H. H.; Gardner, L. D.
1987-01-01
Outlet pressure adjusted to match varying loads. Electrohydraulic servo has positioned sleeve in leftmost position, adjusting outlet pressure to maximum value. Sleeve in equilibrium position, with control land covering control port. For lowest pressure setting, sleeve shifted toward right by increased pressure on sleeve shoulder from servovalve. Pump used in aircraft and robots, where hydraulic actuators repeatedly turned on and off, changing pump load frequently and over wide range.
NASA Technical Reports Server (NTRS)
Ashby, George C., Jr.; Robbins, W. Eugene; Horsley, Lewis A.
1991-01-01
Probe readily positionable in core of uniform flow in hypersonic wind tunnel. Formed of pair of mating cylindrical housings: transducer housing and pitot-tube housing. Pitot tube supported by adjustable wedge fairing attached to top of pitot-tube housing with semicircular foot. Probe adjusted both radially and circumferentially. In addition, pressure-sensing transducer cooled internally by water or other cooling fluid passing through annulus of cooling system.
An evaluation of bias in propensity score-adjusted non-linear regression models.
Wan, Fei; Mitra, Nandita
2016-04-19
Propensity score methods are commonly used to adjust for observed confounding when estimating the conditional treatment effect in observational studies. One popular method, covariate adjustment of the propensity score in a regression model, has been empirically shown to be biased in non-linear models. However, no compelling underlying theoretical reason has been presented. We propose a new framework to investigate bias and consistency of propensity score-adjusted treatment effects in non-linear models that uses a simple geometric approach to forge a link between the consistency of the propensity score estimator and the collapsibility of non-linear models. Under this framework, we demonstrate that adjustment of the propensity score in an outcome model results in the decomposition of observed covariates into the propensity score and a remainder term. Omission of this remainder term from a non-collapsible regression model leads to biased estimates of the conditional odds ratio and conditional hazard ratio, but not for the conditional rate ratio. We further show, via simulation studies, that the bias in these propensity score-adjusted estimators increases with larger treatment effect size, larger covariate effects, and increasing dissimilarity between the coefficients of the covariates in the treatment model versus the outcome model.
Weighted triangulation adjustment
Anderson, Walter L.
1969-01-01
The variation of coordinates method is employed to perform a weighted least squares adjustment of horizontal survey networks. Geodetic coordinates are required for each fixed and adjustable station. A preliminary inverse geodetic position computation is made for each observed line. Weights associated with each observed equation for direction, azimuth, and distance are applied in the formation of the normal equations in-the least squares adjustment. The number of normal equations that may be solved is twice the number of new stations and less than 150. When the normal equations are solved, shifts are produced at adjustable stations. Previously computed correction factors are applied to the shifts and a most probable geodetic position is found for each adjustable station. Pinal azimuths and distances are computed. These may be written onto magnetic tape for subsequent computation of state plane or grid coordinates. Input consists of punch cards containing project identification, program options, and position and observation information. Results listed include preliminary and final positions, residuals, observation equations, solution of the normal equations showing magnitudes of shifts, and a plot of each adjusted and fixed station. During processing, data sets containing irrecoverable errors are rejected and the type of error is listed. The computer resumes processing of additional data sets.. Other conditions cause warning-errors to be issued, and processing continues with the current data set.
High-precision covariant one-boson-exchange potentials for np scattering below 350 MeV
Franz Gross; Alfred Stadler
2007-09-10
Using the Covariant Spectator Theory (CST), we have found One-Boson-Exchange (OBE) potentials that fit the 2006 world np data below 350 MeV with a chi2/Ndata very close to 1, for a total of 3788 data. Our potentials have significantly fewer adjustable parameters than previous high-precision potentials, and they also reproduce the experimental triton binding energy without introducing additional irreducible three-nucleon forces.
Conditional Covariance-Based Subtest Selection for DIMTEST
ERIC Educational Resources Information Center
Froelich, Amy G.; Habing, Brian
2008-01-01
DIMTEST is a nonparametric hypothesis-testing procedure designed to test the assumptions of a unidimensional and locally independent item response theory model. Several previous Monte Carlo studies have found that using linear factor analysis to select the assessment subtest for DIMTEST results in a moderate to severe loss of power when the exam…
Alfred Stadler, Franz Gross
2010-10-01
We provide a short overview of the Covariant Spectator Theory and its applications. The basic ideas are introduced through the example of a {phi}{sup 4}-type theory. High-precision models of the two-nucleon interaction are presented and the results of their use in calculations of properties of the two- and three-nucleon systems are discussed. A short summary of applications of this framework to other few-body systems is also presented.
Bryan, M.F.; Piepel, G.F.; Simpson, D.B.
1996-03-01
The high-level waste (HLW) vitrification plant at the Hanford Site was being designed to transuranic and high-level radioactive waste in borosilicate class. Each batch of plant feed material must meet certain requirements related to plant performance, and the resulting class must meet requirements imposed by the Waste Acceptance Product Specifications. Properties of a process batch and the resultlng glass are largely determined by the composition of the feed material. Empirical models are being developed to estimate some property values from data on feed composition. Methods for checking and documenting compliance with feed and glass requirements must account for various types of uncertainties. This document focuses on the estimation. manipulation, and consequences of composition uncertainty, i.e., the uncertainty inherent in estimates of feed or glass composition. Three components of composition uncertainty will play a role in estimating and checking feed and glass properties: batch-to-batch variability, within-batch uncertainty, and analytical uncertainty. In this document, composition uncertainty and its components are treated in terms of variances and variance components or univariate situations, covariance matrices and covariance components for multivariate situations. The importance of variance and covariance components stems from their crucial role in properly estimating uncertainty In values calculated from a set of observations on a process batch. Two general types of methods for estimating uncertainty are discussed: (1) methods based on data, and (2) methods based on knowledge, assumptions, and opinions about the vitrification process. Data-based methods for estimating variances and covariance matrices are well known. Several types of data-based methods exist for estimation of variance components; those based on the statistical method analysis of variance are discussed, as are the strengths and weaknesses of this approach.
Adams, Dean C.; Felice, Ryan N.
2014-01-01
Morphological integration describes the degree to which sets of organismal traits covary with one another. Morphological covariation may be evaluated at various levels of biological organization, but when characterizing such patterns across species at the macroevolutionary level, phylogeny must be taken into account. We outline an analytical procedure based on the evolutionary covariance matrix that allows species-level patterns of morphological integration among structures defined by sets of traits to be evaluated while accounting for the phylogenetic relationships among taxa, providing a flexible and robust complement to related phylogenetic independent contrasts based approaches. Using computer simulations under a Brownian motion model we show that statistical tests based on the approach display appropriate Type I error rates and high statistical power for detecting known levels of integration, and these trends remain consistent for simulations using different numbers of species, and for simulations that differ in the number of trait dimensions. Thus, our procedure provides a useful means of testing hypotheses of morphological integration in a phylogenetic context. We illustrate the utility of this approach by evaluating evolutionary patterns of morphological integration in head shape for a lineage of Plethodon salamanders, and find significant integration between cranial shape and mandible shape. Finally, computer code written in R for implementing the procedure is provided. PMID:24728003
Covariation in stress and immune gene expression in a range expanding bird.
Martin, Lynn B; Liebl, Andrea L; Kilvitis, Holly J
2015-01-15
The enemy release hypothesis (ERH) posits that hosts encounter fewer infectious parasites when they arrive in new areas, so individuals that adjust their immune defenses most effectively should thrive and even expand the range of that species. An important aspect of vertebrate immune defense is inflammation, as it provides rapid defense against diverse parasites. Glucocorticoids (GCs) are integral to the regulation of inflammation, so here we investigated whether and how covariation in the expression of genes affecting the regulation of inflammation and GCs might have impacted the house sparrow (Passer domesticus) invasion of Kenya. Toll-like receptors 2 and 4 (TLRs) detect microbial threats and instigate inflammatory responses, whereas the glucocorticoid receptor (GR) is integral to resolving inflammation via both local and systemic pathways. As with a previous study on circulating leukocytes, we found that splenic TLR-4 and TLR-2 (the latter marginally non-significant) expression was higher in younger than older populations but only when differences in spleen size were considered; birds at the range edge had larger spleens. In regards to covariation, we found that TLR-2, TLR-4 and GR expression were closely inter-related within individuals, but covariation did not differ among populations. Subsequently, our data suggest that house sparrows are using variants of a common stress-immune regulatory mechanism to expand their Kenyan range.
Recirculating valve lash adjuster
Stoody, R.R.
1987-02-24
This patent describes an internal combustion engine with a valve assembly of the type including overhead valves supported by a cylinder head for opening and closing movements in a substantially vertical direction and a rotatable overhead camshaft thereabove lubricated by engine oil pumped by an engine oil pump. A hydraulic lash adjuster with an internal reservoir therein is solely supplied with run-off lubricating oil from the camshaft which oil is pumped into the internal reservoir of the lash adjuster by self-pumping operation of the lash adjuster produced by lateral forces thereon by the rotative operation of the camshaft comprising: a housing of the lash adjuster including an axially extending bore therethrough with a lower wall means of the housing closing the lower end thereof; a first plunger member being closely slidably received in the bore of the housing and having wall means defining a fluid filled power chamber with the lower wall means of the housing; and a second plunger member of the lash adjuster having a portion being loosely slidably received and extending into the bore of the housing for reciprocation therein. Another portion extends upwardly from the housing to operatively receive alternating side-to-side force inputs from operation of the camshaft.
Conditional Covariance Theory and Detect for Polytomous Items
ERIC Educational Resources Information Center
Zhang, Jinming
2007-01-01
This paper extends the theory of conditional covariances to polytomous items. It has been proven that under some mild conditions, commonly assumed in the analysis of response data, the conditional covariance of two items, dichotomously or polytomously scored, given an appropriately chosen composite is positive if, and only if, the two items…
Perturbative approach to covariance matrix of the matter power spectrum
Mohammed, Irshad; Seljak, Uros; Vlah, Zvonimir
2016-06-30
We evaluate the covariance matrix of the matter power spectrum using perturbation theory up to dominant terms at 1-loop order and compare it to numerical simulations. We decompose the covariance matrix into the disconnected (Gaussian) part, trispectrum from the modes outside the survey (beat coupling or super-sample variance), and trispectrum from the modes inside the survey, and show how the different components contribute to the overall covariance matrix. We find the agreement with the simulations is at a 10\\% level up to $k \\sim 1 h {\\rm Mpc^{-1}}$. We show that all the connected components are dominated by the large-scale modes ($k<0.1 h {\\rm Mpc^{-1}}$), regardless of the value of the wavevectors $k,\\, k'$ of the covariance matrix, suggesting that one must be careful in applying the jackknife or bootstrap methods to the covariance matrix. We perform an eigenmode decomposition of the connected part of the covariance matrix, showing that at higher $k$ it is dominated by a single eigenmode. The full covariance matrix can be approximated as the disconnected part only, with the connected part being treated as an external nuisance parameter with a known scale dependence, and a known prior on its variance for a given survey volume. Finally, we provide a prescription for how to evaluate the covariance matrix from small box simulations without the need to simulate large volumes.
Alternative Multiple Imputation Inference for Mean and Covariance Structure Modeling
ERIC Educational Resources Information Center
Lee, Taehun; Cai, Li
2012-01-01
Model-based multiple imputation has become an indispensable method in the educational and behavioral sciences. Mean and covariance structure models are often fitted to multiply imputed data sets. However, the presence of multiple random imputations complicates model fit testing, which is an important aspect of mean and covariance structure…
Covariate-Based Assignment to Treatment Groups: Some Simulation Results.
ERIC Educational Resources Information Center
Jain, Ram B.; Hsu, Tse-Chi
1980-01-01
Six estimators of treatment effect when assignment to treatment groups is based on the covariate are compared in terms of empirical standard errors and percent relative bias. Results show that simple analysis of covariance estimator is not always appropriate. (Author/GK)
Handling Correlations between Covariates and Random Slopes in Multilevel Models
ERIC Educational Resources Information Center
Bates, Michael David; Castellano, Katherine E.; Rabe-Hesketh, Sophia; Skrondal, Anders
2014-01-01
This article discusses estimation of multilevel/hierarchical linear models that include cluster-level random intercepts and random slopes. Viewing the models as structural, the random intercepts and slopes represent the effects of omitted cluster-level covariates that may be correlated with included covariates. The resulting correlations between…
Performance of internal covariance estimators for cosmic shear correlation functions
NASA Astrophysics Data System (ADS)
Friedrich, O.; Seitz, S.; Eifler, T. F.; Gruen, D.
2016-03-01
Data re-sampling methods such as delete-one jackknife, bootstrap or the sub-sample covariance are common tools for estimating the covariance of large-scale structure probes. We investigate different implementations of these methods in the context of cosmic shear two-point statistics. Using lognormal simulations of the convergence field and the corresponding shear field we generate mock catalogues of a known and realistic covariance. For a survey of {˜ } 5000 ° ^2 we find that jackknife, if implemented by deleting sub-volumes of galaxies, provides the most reliable covariance estimates. Bootstrap, in the common implementation of drawing sub-volumes of galaxies, strongly overestimates the statistical uncertainties. In a forecast for the complete 5-yr Dark Energy Survey, we show that internally estimated covariance matrices can provide a large fraction of the true uncertainties on cosmological parameters in a 2D cosmic shear analysis. The volume inside contours of constant likelihood in the Ωm-σ8 plane as measured with internally estimated covariance matrices is on average ≳85 per cent of the volume derived from the true covariance matrix. The uncertainty on the parameter combination Σ _8 ˜ σ _8 Ω _m^{0.5} derived from internally estimated covariances is ˜90 per cent of the true uncertainty.
Covariate Balance in Bayesian Propensity Score Approaches for Observational Studies
ERIC Educational Resources Information Center
Chen, Jianshen; Kaplan, David
2015-01-01
Bayesian alternatives to frequentist propensity score approaches have recently been proposed. However, few studies have investigated their covariate balancing properties. This article compares a recently developed two-step Bayesian propensity score approach to the frequentist approach with respect to covariate balance. The effects of different…
The Use of Covariation as a Principle of Causal Analysis
ERIC Educational Resources Information Center
Shultz, Thomas R.; Mendelson, Rosyln
1975-01-01
This study investigated the use of covariation as a principle of causal analysis in children 3-4, 6-7, and 9-11 years of age. The results indicated that children as young as 3 years were capable of using covariation information in their attributions of simple physical effects. (Author/CS)
Covariation Is a Poor Measure of Molecular Coevolution.
Talavera, David; Lovell, Simon C; Whelan, Simon
2015-09-01
Recent developments in the analysis of amino acid covariation are leading to breakthroughs in protein structure prediction, protein design, and prediction of the interactome. It is assumed that observed patterns of covariation are caused by molecular coevolution, where substitutions at one site affect the evolutionary forces acting at neighboring sites. Our theoretical and empirical results cast doubt on this assumption. We demonstrate that the strongest coevolutionary signal is a decrease in evolutionary rate and that unfeasibly long times are required to produce coordinated substitutions. We find that covarying substitutions are mostly found on different branches of the phylogenetic tree, indicating that they are independent events that may or may not be attributable to coevolution. These observations undermine the hypothesis that molecular coevolution is the primary cause of the covariation signal. In contrast, we find that the pairs of residues with the strongest covariation signal tend to have low evolutionary rates, and that it is this low rate that gives rise to the covariation signal. Slowly evolving residue pairs are disproportionately located in the protein's core, which explains covariation methods' ability to detect pairs of residues that are close in three dimensions. These observations lead us to propose the "coevolution paradox": The strength of coevolution required to cause coordinated changes means the evolutionary rate is so low that such changes are highly unlikely to occur. As modern covariation methods may lead to breakthroughs in structural genomics, it is critical to recognize their biases and limitations.
Empirical Performance of Covariates in Education Observational Studies
ERIC Educational Resources Information Center
Wong, Vivian C.; Valentine, Jeffrey C.; Miller-Bains, Kate
2017-01-01
This article summarizes results from 12 empirical evaluations of observational methods in education contexts. We look at the performance of three common covariate-types in observational studies where the outcome is a standardized reading or math test. They are: pretest measures, local geographic matching, and rich covariate sets with a strong…
Perturbative approach to covariance matrix of the matter power spectrum
NASA Astrophysics Data System (ADS)
Mohammed, Irshad; Seljak, Uroš; Vlah, Zvonimir
2017-04-01
We evaluate the covariance matrix of the matter power spectrum using perturbation theory up to dominant terms at 1-loop order and compare it to numerical simulations. We decompose the covariance matrix into the disconnected (Gaussian) part, trispectrum from the modes outside the survey (supersample variance) and trispectrum from the modes inside the survey, and show how the different components contribute to the overall covariance matrix. We find the agreement with the simulations is at a 10 per cent level up to k ∼ 1 h Mpc-1. We show that all the connected components are dominated by the large-scale modes (k < 0.1 h Mpc-1), regardless of the value of the wave vectors k, k΄ of the covariance matrix, suggesting that one must be careful in applying the jackknife or bootstrap methods to the covariance matrix. We perform an eigenmode decomposition of the connected part of the covariance matrix, showing that at higher k, it is dominated by a single eigenmode. The full covariance matrix can be approximated as the disconnected part only, with the connected part being treated as an external nuisance parameter with a known scale dependence, and a known prior on its variance for a given survey volume. Finally, we provide a prescription for how to evaluate the covariance matrix from small box simulations without the need to simulate large volumes.
HIGH DIMENSIONAL COVARIANCE MATRIX ESTIMATION IN APPROXIMATE FACTOR MODELS
Fan, Jianqing; Liao, Yuan; Mincheva, Martina
2012-01-01
The variance covariance matrix plays a central role in the inferential theories of high dimensional factor models in finance and economics. Popular regularization methods of directly exploiting sparsity are not directly applicable to many financial problems. Classical methods of estimating the covariance matrices are based on the strict factor models, assuming independent idiosyncratic components. This assumption, however, is restrictive in practical applications. By assuming sparse error covariance matrix, we allow the presence of the cross-sectional correlation even after taking out common factors, and it enables us to combine the merits of both methods. We estimate the sparse covariance using the adaptive thresholding technique as in Cai and Liu (2011), taking into account the fact that direct observations of the idiosyncratic components are unavailable. The impact of high dimensionality on the covariance matrix estimation based on the factor structure is then studied. PMID:22661790
HIGH DIMENSIONAL COVARIANCE MATRIX ESTIMATION IN APPROXIMATE FACTOR MODELS.
Fan, Jianqing; Liao, Yuan; Mincheva, Martina
2011-01-01
The variance covariance matrix plays a central role in the inferential theories of high dimensional factor models in finance and economics. Popular regularization methods of directly exploiting sparsity are not directly applicable to many financial problems. Classical methods of estimating the covariance matrices are based on the strict factor models, assuming independent idiosyncratic components. This assumption, however, is restrictive in practical applications. By assuming sparse error covariance matrix, we allow the presence of the cross-sectional correlation even after taking out common factors, and it enables us to combine the merits of both methods. We estimate the sparse covariance using the adaptive thresholding technique as in Cai and Liu (2011), taking into account the fact that direct observations of the idiosyncratic components are unavailable. The impact of high dimensionality on the covariance matrix estimation based on the factor structure is then studied.
UDU/T/ covariance factorization for Kalman filtering
NASA Technical Reports Server (NTRS)
Thornton, C. L.; Bierman, G. J.
1980-01-01
There has been strong motivation to produce numerically stable formulations of the Kalman filter algorithms because it has long been known that the original discrete-time Kalman formulas are numerically unreliable. Numerical instability can be avoided by propagating certain factors of the estimate error covariance matrix rather than the covariance matrix itself. This paper documents filter algorithms that correspond to the covariance factorization P = UDU(T), where U is a unit upper triangular matrix and D is diagonal. Emphasis is on computational efficiency and numerical stability, since these properties are of key importance in real-time filter applications. The history of square-root and U-D covariance filters is reviewed. Simple examples are given to illustrate the numerical inadequacy of the Kalman covariance filter algorithms; these examples show how factorization techniques can give improved computational reliability.
Cross-section adjustment techniques for BWR adaptive simulation
NASA Astrophysics Data System (ADS)
Jessee, Matthew Anderson
Computational capability has been developed to adjust multi-group neutron cross-sections to improve the fidelity of boiling water reactor (BWR) modeling and simulation. The method involves propagating multi-group neutron cross-section uncertainties through BWR computational models to evaluate uncertainties in key core attributes such as core k-effective, nodal power distributions, thermal margins, and in-core detector readings. Uncertainty-based inverse theory methods are then employed to adjust multi-group cross-sections to minimize the disagreement between BWR modeling predictions and measured plant data. For this work, measured plant data were virtually simulated in the form of perturbed 3-D nodal power distributions with discrepancies with predictions of the same order of magnitude as expected from plant data. Using the simulated plant data, multi-group cross-section adjustment reduces the error in core k-effective to less than 0.2% and the RMS error in nodal power to 4% (i.e. the noise level of the in-core instrumentation). To ensure that the adapted BWR model predictions are robust, Tikhonov regularization is utilized to control the magnitude of the cross-section adjustment. In contrast to few-group cross-section adjustment, which was the focus of previous research on BWR adaptive simulation, multigroup cross-section adjustment allows for future fuel cycle design optimization to include the determination of optimal fresh fuel assembly designs using the adjusted multi-group cross-sections. The major focus of this work is to efficiently propagate multi-group neutron cross-section uncertainty through BWR lattice physics calculations. Basic neutron cross-section uncertainties are provided in the form of multi-group cross-section covariance matrices. For energy groups in the resolved resonance energy range, the cross-section uncertainties are computed using an infinitely-dilute approximation of the neutron flux. In order to accurately account for spatial and
Eugster, Patrick; Sennhauser, Michèle; Zweifel, Peter
2010-07-01
When premiums are community-rated, risk adjustment (RA) serves to mitigate competitive insurers' incentive to select favorable risks. However, unless fully prospective, it also undermines their incentives for efficiency. By capping its volume, one may try to counteract this tendency, exposing insurers to some financial risk. This in term runs counter the quest to refine the RA formula, which would increase RA volume. Specifically, the adjuster, "Hospitalization or living in a nursing home during the previous year" will be added in Switzerland starting 2012. This paper investigates how to minimize the opportunity cost of capping RA in terms of increased incentives for risk selection.
Central subspace dimensionality reduction using covariance operators.
Kim, Minyoung; Pavlovic, Vladimir
2011-04-01
We consider the task of dimensionality reduction informed by real-valued multivariate labels. The problem is often treated as Dimensionality Reduction for Regression (DRR), whose goal is to find a low-dimensional representation, the central subspace, of the input data that preserves the statistical correlation with the targets. A class of DRR methods exploits the notion of inverse regression (IR) to discover central subspaces. Whereas most existing IR techniques rely on explicit output space slicing, we propose a novel method called the Covariance Operator Inverse Regression (COIR) that generalizes IR to nonlinear input/output spaces without explicit target slicing. COIR's unique properties make DRR applicable to problem domains with high-dimensional output data corrupted by potentially significant amounts of noise. Unlike recent kernel dimensionality reduction methods that employ iterative nonconvex optimization, COIR yields a closed-form solution. We also establish the link between COIR, other DRR techniques, and popular supervised dimensionality reduction methods, including canonical correlation analysis and linear discriminant analysis. We then extend COIR to semi-supervised settings where many of the input points lack their labels. We demonstrate the benefits of COIR on several important regression problems in both fully supervised and semi-supervised settings.
Generalized Covariant Gyrokinetic Dynamics of Magnetoplasmas
Cremaschini, C.; Tessarotto, M.; Nicolini, P.; Beklemishev, A.
2008-12-31
A basic prerequisite for the investigation of relativistic astrophysical magnetoplasmas, occurring typically in the vicinity of massive stellar objects (black holes, neutron stars, active galactic nuclei, etc.), is the accurate description of single-particle covariant dynamics, based on gyrokinetic theory (Beklemishev et al., 1999-2005). Provided radiation-reaction effects are negligible, this is usually based on the assumption that both the space-time metric and the EM fields (in particular the magnetic field) are suitably prescribed and are considered independent of single-particle dynamics, while allowing for the possible presence of gravitational/EM perturbations driven by plasma collective interactions which may naturally arise in such systems. The purpose of this work is the formulation of a generalized gyrokinetic theory based on the synchronous variational principle recently pointed out (Tessarotto et al., 2007) which permits to satisfy exactly the physical realizability condition for the four-velocity. The theory here developed includes the treatment of nonlinear perturbations (gravitational and/or EM) characterized locally, i.e., in the rest frame of a test particle, by short wavelength and high frequency. Basic feature of the approach is to ensure the validity of the theory both for large and vanishing parallel electric field. It is shown that the correct treatment of EM perturbations occurring in the presence of an intense background magnetic field generally implies the appearance of appropriate four-velocity corrections, which are essential for the description of single-particle gyrokinetic dynamics.
Holographic bound in covariant loop quantum gravity
NASA Astrophysics Data System (ADS)
Tamaki, Takashi
2016-07-01
We investigate puncture statistics based on the covariant area spectrum in loop quantum gravity. First, we consider Maxwell-Boltzmann statistics with a Gibbs factor for punctures. We establish formulas which relate physical quantities such as horizon area to the parameter characterizing holographic degrees of freedom. We also perform numerical calculations and obtain consistency with these formulas. These results tell us that the holographic bound is satisfied in the large area limit and the correction term of the entropy-area law can be proportional to the logarithm of the horizon area. Second, we also consider Bose-Einstein statistics and show that the above formulas are also useful in this case. By applying the formulas, we can understand intrinsic features of Bose-Einstein condensate which corresponds to the case when the horizon area almost consists of punctures in the ground state. When this phenomena occurs, the area is approximately constant against the parameter characterizing the temperature. When this phenomena is broken, the area shows rapid increase which suggests the phase transition from quantum to classical area.
General covariance from the quantum renormalization group
NASA Astrophysics Data System (ADS)
Shyam, Vasudev
2017-03-01
The quantum renormalization group (QRG) is a realization of holography through a coarse-graining prescription that maps the beta functions of a quantum field theory thought to live on the "boundary" of some space to holographic actions in the "bulk" of this space. A consistency condition will be proposed that translates into general covariance of the gravitational theory in the D +1 dimensional bulk. This emerges from the application of the QRG on a planar matrix field theory living on the D dimensional boundary. This will be a particular form of the Wess-Zumino consistency condition that the generating functional of the boundary theory needs to satisfy. In the bulk, this condition forces the Poisson bracket algebra of the scalar and vector constraints of the dual gravitational theory to close in a very specific manner, namely, the manner in which the corresponding constraints of general relativity do. A number of features of the gravitational theory will be fixed as a consequence of this form of the Poisson bracket algebra. In particular, it will require the metric beta function to be of the gradient form.
Frame Indifferent (Truly Covariant) Formulation of Electrodynamics
NASA Astrophysics Data System (ADS)
Christov, Christo
2010-10-01
The Electromagnetic field is considered from the point of view of mechanics of continuum. It is shown that Maxwell's equations are mathematically strict corollaries form the equation of motions of an elastic incompressible liquid. If the concept of frame-indifference (material invariance) is applied to the model of elastic liquid, then the partial time derivatives have to be replaced by the convective time derivative in the momentum equations, and by the Oldroyd upper-convected derivative in the constitutive relation. The convective/convected terms involve the velocity at a point of the field, and as a result, when deriving the Maxwell form of the equations, one arrives at equations which contain both the terms of Maxwell's equation and the so-called laws of motional EMF: Faraday's, Oersted--Ampere's, and the Lorentz-force law. Thus a unification of the electromagnetism is achieved. Since the new model is frame indifferent, it is truly covariant in the sense that the governing system is invariant when changing to a coordinate frame that can accelerate or even deform in time.
CMB lens sample covariance and consistency relations
NASA Astrophysics Data System (ADS)
Motloch, Pavel; Hu, Wayne; Benoit-Lévy, Aurélien
2017-02-01
Gravitational lensing information from the two and higher point statistics of the cosmic microwave background (CMB) temperature and polarization fields are intrinsically correlated because they are lensed by the same realization of structure between last scattering and observation. Using an analytic model for lens sample covariance, we show that there is one mode, separately measurable in the lensed CMB power spectra and lensing reconstruction, that carries most of this correlation. Once these measurements become lens sample variance dominated, this mode should provide a useful consistency check between the observables that is largely free of sampling and cosmological parameter errors. Violations of consistency could indicate systematic errors in the data and lens reconstruction or new physics at last scattering, any of which could bias cosmological inferences and delensing for gravitational waves. A second mode provides a weaker consistency check for a spatially flat universe. Our analysis isolates the additional information supplied by lensing in a model-independent manner but is also useful for understanding and forecasting CMB cosmological parameter errors in the extended Λ cold dark matter parameter space of dark energy, curvature, and massive neutrinos. We introduce and test a simple but accurate forecasting technique for this purpose that neither double counts lensing information nor neglects lensing in the observables.
Nonparametric estimation for length-biased and right-censored data
Huang, Chiung-Yu; Qin, Jing
2011-01-01
Summary This paper considers survival data arising from length-biased sampling, where the survival times are left truncated by uniformly distributed random truncation times. We propose a nonparametric estimator that incorporates the information about the length-biased sampling scheme. The new estimator retains the simplicity of the truncation product-limit estimator with a closed-form expression, and has a small efficiency loss compared with the nonparametric maximum likelihood estimator, which requires an iterative algorithm. Moreover, the asymptotic variance of the proposed estimator has a closed form, and a variance estimator is easily obtained by plug-in methods. Numerical simulation studies with practical sample sizes are conducted to compare the performance of the proposed method with its competitors. A data analysis of the Canadian Study of Health and Aging is conducted to illustrate the methods and theory. PMID:23049126
Bootstrap Prediction Intervals in Non-Parametric Regression with Applications to Anomaly Detection
NASA Technical Reports Server (NTRS)
Kumar, Sricharan; Srivistava, Ashok N.
2012-01-01
Prediction intervals provide a measure of the probable interval in which the outputs of a regression model can be expected to occur. Subsequently, these prediction intervals can be used to determine if the observed output is anomalous or not, conditioned on the input. In this paper, a procedure for determining prediction intervals for outputs of nonparametric regression models using bootstrap methods is proposed. Bootstrap methods allow for a non-parametric approach to computing prediction intervals with no specific assumptions about the sampling distribution of the noise or the data. The asymptotic fidelity of the proposed prediction intervals is theoretically proved. Subsequently, the validity of the bootstrap based prediction intervals is illustrated via simulations. Finally, the bootstrap prediction intervals are applied to the problem of anomaly detection on aviation data.
A Non-parametric Bayesian Approach for Predicting RNA Secondary Structures
NASA Astrophysics Data System (ADS)
Sato, Kengo; Hamada, Michiaki; Mituyama, Toutai; Asai, Kiyoshi; Sakakibara, Yasubumi
Since many functional RNAs form stable secondary structures which are related to their functions, RNA secondary structure prediction is a crucial problem in bioinformatics. We propose a novel model for generating RNA secondary structures based on a non-parametric Bayesian approach, called hierarchical Dirichlet processes for stochastic context-free grammars (HDP-SCFGs). Here non-parametric means that some meta-parameters, such as the number of non-terminal symbols and production rules, do not have to be fixed. Instead their distributions are inferred in order to be adapted (in the Bayesian sense) to the training sequences provided. The results of our RNA secondary structure predictions show that HDP-SCFGs are more accurate than the MFE-based and other generative models.
A fresh look at the use of nonparametric analysis in actimetry.
Gonçalves, B S B; Adamowicz, Taísa; Louzada, Fernando Mazzilli; Moreno, Claudia Roberta; Araujo, John Fontenele
2015-04-01
Actimetry has been used to estimate the sleep-wake cycle instead of the rest-activity rhythm. Although algorithms for assessing sleep from actimetry data exist, it is useful to analyze the rest-activity rhythm using nonparametric methods. This would then allow rest-activity rhythm stability, fragmentation and amplitude to be quantified. In addition, sleep and wakefulness efficiency can be quantified separately. These variables have been used in studies analyzing the effect of age, diseases and their respective treatments on human circadian rhythmicity. In this study, we carried out a comprehensive analysis of the main results from published articles and devised a functional model of interaction among the several components involved in generating the sleep-wake cycle. The nonparametric variables render it possible to infer the main characteristics of circadian rhythms, such as synchronization with a zeitgeber, and its amplitude and robustness.
Applications of non-parametric statistics and analysis of variance on sample variances
NASA Technical Reports Server (NTRS)
Myers, R. H.
1981-01-01
Nonparametric methods that are available for NASA-type applications are discussed. An attempt will be made here to survey what can be used, to attempt recommendations as to when each would be applicable, and to compare the methods, when possible, with the usual normal-theory procedures that are avavilable for the Gaussion analog. It is important here to point out the hypotheses that are being tested, the assumptions that are being made, and limitations of the nonparametric procedures. The appropriateness of doing analysis of variance on sample variances are also discussed and studied. This procedure is followed in several NASA simulation projects. On the surface this would appear to be reasonably sound procedure. However, difficulties involved center around the normality problem and the basic homogeneous variance assumption that is mase in usual analysis of variance problems. These difficulties discussed and guidelines given for using the methods.
Inciardi, J F; Batra, K K
1993-01-01
A nonparametric expectation maximization approach to the study of population pharmacokinetics is described for an aminoglycoside antibiotic. The method is used to explore population estimates for gentamicin clearance (liters per hour per creatinine clearance) and volume of distribution (liters per kilogram) in tumor patients. Joint and marginal probability distributions are plotted and further characterized by using standard descriptors such as mean, median, mode, standard deviation, skewness, and kurtosis. Results of additional analyses using hematologic or solid tumor subpopulations agree with those of a recent larger study which found no significant pharmacokinetic differences between these groups. Nonparametric maximum-expectation analyses are convenient and allow exploratory analysis of population estimates directly from routine laboratory information. PMID:8517689
Lan, Ling; Datta, Somnath
2010-04-01
As a type of multivariate survival data, multistate models have a wide range of applications, notably in cancer and infectious disease progression studies. In this article, we revisit the problem of estimation of state occupation, entry and exit times in a multistate model where various estimators have been proposed in the past under a variety of parametric and non-parametric assumptions. We focus on two non-parametric approaches, one using a product limit formula as recently proposed in Datta and Sundaram(1) and a novel approach using a fractional risk set calculation followed by a subtraction formula to calculate the state occupation probability of a transient state. A numerical comparison between the two methods is presented using detailed simulation studies. We show that the new estimators have lower statistical errors of estimation of state occupation probabilities for the distant states. We illustrate the two methods using a pubertal development data set obtained from the NHANES III.(2).
A non-parametric approach to estimate the total deviation index for non-normal data.
Perez-Jaume, Sara; Carrasco, Josep L
2015-11-10
Concordance indices are used to assess the degree of agreement between different methods that measure the same characteristic. In this context, the total deviation index (TDI) is an unscaled concordance measure that quantifies to which extent the readings from the same subject obtained by different methods may differ with a certain probability. Common approaches to estimate the TDI assume data are normally distributed and linearity between response and effects (subjects, methods and random error). Here, we introduce a new non-parametric methodology for estimation and inference of the TDI that can deal with any kind of quantitative data. The present study introduces this non-parametric approach and compares it with the already established methods in two real case examples that represent situations of non-normal data (more specifically, skewed data and count data). The performance of the already established methodologies and our approach in these contexts is assessed by means of a simulation study.
Thomas, Laine; Stefanski, Leonard A.; Davidian, Marie
2013-01-01
In clinical studies, covariates are often measured with error due to biological fluctuations, device error and other sources. Summary statistics and regression models that are based on mismeasured data will differ from the corresponding analysis based on the “true” covariate. Statistical analysis can be adjusted for measurement error, however various methods exhibit a tradeo between convenience and performance. Moment Adjusted Imputation (MAI) is method for measurement error in a scalar latent variable that is easy to implement and performs well in a variety of settings. In practice, multiple covariates may be similarly influenced by biological fluctuastions, inducing correlated multivariate measurement error. The extension of MAI to the setting of multivariate latent variables involves unique challenges. Alternative strategies are described, including a computationally feasible option that is shown to perform well. PMID:24072947
IPADE: Iterative prototype adjustment for nearest neighbor classification.
Triguero, Isaac; Garcia, Salvador; Herrera, Francisco
2010-12-01
Nearest prototype methods are a successful trend of many pattern classification tasks. However, they present several shortcomings such as time response, noise sensitivity, and storage requirements. Data reduction techniques are suitable to alleviate these drawbacks. Prototype generation is an appropriate process for data reduction, which allows the fitting of a dataset for nearest neighbor (NN) classification. This brief presents a methodology to learn iteratively the positioning of prototypes using real parameter optimization procedures. Concretely, we propose an iterative prototype adjustment technique based on differential evolution. The results obtained are contrasted with nonparametric statistical tests and show that our proposal consistently outperforms previously proposed methods, thus becoming a suitable tool in the task of enhancing the performance of the NN classifier.
Klous, Miriam; Mikulic, Pavle; Latash, Mark L
2011-05-01
We used the framework of the uncontrolled manifold hypothesis to explore the relations between anticipatory synergy adjustments (ASAs) and anticipatory postural adjustments (APAs) during feedforward control of vertical posture. ASAs represent a drop in the index of a multimuscle-mode synergy stabilizing the coordinate of the center of pressure in preparation to an action. ASAs reflect early changes of an index of covariation among variables reflecting muscle activation, whereas APAs reflect early changes in muscle activation levels averaged across trials. The assumed purpose of ASAs is to modify stability of performance variables, whereas the purpose of APAs is to change magnitudes of those variables. We hypothesized that ASAs would be seen before APAs and that this finding would be consistent with regard to the muscle-mode composition defined on the basis of different tasks and phases of action. Subjects performed a voluntary body sway task and a quick, bilateral shoulder flexion task under self-paced and reaction time conditions. Surface muscle activity of 12 leg and trunk muscles was analyzed to identify sets of 4 muscle modes for each task and for different phases within the shoulder flexion task. Variance components in the muscle-mode space and indexes of multimuscle-mode synergy stabilizing shift of the center of pressure were computed. ASAs were seen ∼ 100-150 ms prior to the task initiation, before APAs. The results were consistent with respect to different sets of muscle modes defined over the two tasks and different shoulder flexion phases. We conclude that the preparation for a self-triggered postural perturbation is associated with two types of anticipatory adjustments, ASAs and APAs. They reflect different feedforward processes within the hypothetical hierarchical control scheme, resulting in changes in patterns of covariation of elemental variables and in their patterns averaged across trials, respectively. The results show that synergies quantified
System and Method of Use for Non-parametric Circular Autocorrelation for Signal Processing
2012-07-30
0012] Wald , A. and J. Wolfowitz , An exact test for randomness in the non–Parametric case based on serial correlation, Annals of Mathematical...Statistics Vol. 14, No. 4, pages 378–388, 1943, (hereinafter “ Wald and Wolfowitz ”) provides a non-parametric permutations method such that if n is...present disclosure models accurately and efficiently. 8 [0015] Wald and Wolfowitz generally describe the properties of hxxR , in the context
NASA Technical Reports Server (NTRS)
Ackleson, S. G.; Klemas, V.
1984-01-01
The spatial, spectral and radiometric characteristics of LANDSAT TM and MSS imagery for detecting submerged aquatic vegetation are assessed. The problem is approached from two perspectives; purely stochastic or nonparametric in a radiative sense and theoretical in which radiative transfer equations are used to predict upwelling radiance at satellite altitude. The spectral and radiometric aspects of the theoretical approach are addressed with which a submerged plant canopy is distinguished from a surrounding bottom of sand or mud.
System Availability: Time Dependence and Statistical Inference by (Semi) Non-Parametric Methods
1988-08-01
Technical FROM -TO 1988 August T 42 16. SUPPLEMENTARY NOTATION 17. COSATI CODES 18 SUBJECT TERMS (Continue on reverse if necessary and identify by block...availability in finite time (not steady-state or long -run), and to non-parametric estimates. 20 DISTRIBUTION, AVAILABILITY OF ABSTRACT 21 ABSTRACT...productivity of commercial nuclear power plants; in that arena it is quantified by probabilistic risk assessment (PRA). Relaued finite state
Linderman, Scott W.; Johnson, Matthew J.; Wilson, Matthew A.; Chen, Zhe
2016-01-01
Background Rodent hippocampal population codes represent important spatial information about the environment during navigation. Computational methods have been developed to uncover the neural representation of spatial topology embedded in rodent hippocampal ensemble spike activity. New method We extend our previous work and propose a novel nonparametric Bayesian approach to infer rat hippocampal population codes during spatial navigation. To tackle the model selection problem, we leverage a nonparametric Bayesian model. Specifically, we apply a hierarchical Dirichlet process-hidden Markov model (HDP-HMM) using two Bayesian inference methods, one based on Markov chain Monte Carlo (MCMC) and the other based on variational Bayes (VB). Results The effectiveness of our Bayesian approaches is demonstrated on recordings from a freely-behaving rat navigating in an open field environment. Comparison with existing methods The HDP-HMM outperforms the finite-state HMM in both simulated and experimental data. For HPD-HMM, the MCMC-based inference with Hamiltonian Monte Carlo (HMC) hyperparameter sampling is flexible and efficient, and outperforms VB and MCMC approaches with hyperparameters set by empirical Bayes. Conclusion The nonparametric Bayesian HDP-HMM method can efficiently perform model selection and identify model parameters, which can used for modeling latent-state neuronal population dynamics. PMID:26854398
The properties and mechanism of long-term memory in nonparametric volatility
NASA Astrophysics Data System (ADS)
Li, Handong; Cao, Shi-Nan; Wang, Yan
2010-08-01
Recent empirical literature documents the presence of long-term memory in return volatility. But the mechanism of the existence of long-term memory is still unclear. In this paper, we investigate the origin and properties of long-term memory with nonparametric volatility, using high-frequency time series data of the Chinese Shanghai Composite Stock Price Index. We perform Detrended Fluctuation Analysis (DFA) on three different nonparametric volatility estimators with different sampling frequencies. For the same volatility series, the Hurst exponents reduce as the sampling time interval increases, but they are still larger than 1/2, which means that no matter how the interval changes, it still cannot change the existence of long memory. RRV presents a relatively stable property on long-term memory and is less influenced by sampling frequency. RV and RBV have some evolutionary trends depending on time intervals, which indicating that the jump component has no significant impact on the long-term memory property. This suggests that the presence of long-term memory in nonparametric volatility can be contributed to the integrated variance component. Considering the impact of microstructure noise, RBV and RRV still present long-term memory under various time intervals. We can infer that the presence of long-term memory in realized volatility is not affected by market microstructure noise. Our findings imply that the long-term memory phenomenon is an inherent characteristic of the data generating process, not a result of microstructure noise or volatility clustering.
Nonparametric simulation-based statistics for detecting linkage in general pedigrees
Davis, S.; Schroeder, M.; Weeks, D.E.; Goldin, L.R.
1996-04-01
We present here four nonparametric statistics for linkage analysis that test whether pairs of affected relatives share marker alleles more often than expected. These statistics are based on simulating the null distribution of a given statistic conditional on the unaffecteds` marker genotypes. Each statistic uses a different measure of marker sharing: the SimAPM statistic uses the simulation-based affected-pedigree-member measure based on identity-by-state (IBS) sharing. The SimKIN (kinship) measure is 1.0 for identity-by-descent (IBD) sharing, 0.0 for no IBD sharing, and the kinship coefficient when the IBD status is ambiguous. The simulation-based IBD (SimIBD) statistic uses a recursive algorithm to determine the probability of two affecteds sharing a specific allele IBD. The SimISO statistic is identical to SimIBD, except that it also measures marker similarity between unaffected pairs. We evaluated our statistics on data simulated under different two-locus disease models, comparing our results to those obtained with several other nonparametric statistics. Use of IBD information produces dramatic increases in power over the SimAPM method, which uses only IBS information. The power of our best statistic in most cases meets or exceeds the power of the other nonparametric statistics. Furthermore, our statistics perform comparisons between all affected relative pairs within general pedigrees and are not restricted to sib pairs or nuclear families. 32 refs., 5 figs., 6 tabs.
Nonparametric simulation-based statistics for detecting linkage in general pedigrees.
Davis, S.; Schroeder, M.; Goldin, L. R.; Weeks, D. E.
1996-01-01
We present here four nonparametric statistics for linkage analysis that test whether pairs of affected relatives share marker alleles more often than expected. These statistics are based on simulating the null distribution of a given statistic conditional on the unaffecteds' marker genotypes. Each statistic uses a different measure of marker sharing: the SimAPM statistic uses the simulation-based affected-pedigree-member measure based on identity-by-state (IBS) sharing. The SimKIN (kinship) measure is 1.0 for identity-by-descent (IBD) sharing, 0.0 for no IBD status sharing, and the kinship coefficient when the IBD status is ambiguous. The simulation-based IBD (SimIBD) statistic uses a recursive algorithm to determine the probability of two affecteds sharing a specific allele IBD. The SimISO statistic is identical to SimIBD, except that it also measures marker similarity between unaffected pairs. We evaluated our statistics on data simulated under different two-locus disease models, comparing our results to those obtained with several other nonparametric statistics. Use of IBD information produces dramatic increases in power over the SimAPM method, which uses only IBS information. The power of our best statistic in most cases meets or exceeds the power of the other nonparametric statistics. Furthermore, our statistics perform comparisons between all affected relative pairs within general pedigrees and are not restricted to sib pairs or nuclear families. PMID:8644751
Chang, Jinyuan; Tang, Cheng Yong; Wu, Yichao
2015-01-01
We consider an independence feature screening technique for identifying explanatory variables that locally contribute to the response variable in high-dimensional regression analysis. Without requiring a specific parametric form of the underlying data model, our approach accommodates a wide spectrum of nonparametric and semiparametric model families. To detect the local contributions of explanatory variables, our approach constructs empirical likelihood locally in conjunction with marginal nonparametric regressions. Since our approach actually requires no estimation, it is advantageous in scenarios such as the single-index models where even specification and identification of a marginal model is an issue. By automatically incorporating the level of variation of the nonparametric regression and directly assessing the strength of data evidence supporting local contribution from each explanatory variable, our approach provides a unique perspective for solving feature screening problems. Theoretical analysis shows that our approach can handle data dimensionality growing exponentially with the sample size. With extensive theoretical illustrations and numerical examples, we show that the local independence screening approach performs promisingly. PMID:27242388
NASA Astrophysics Data System (ADS)
Kianisarkaleh, Azadeh; Ghassemian, Hassan
2016-09-01
Feature extraction plays a crucial role in improvement of hyperspectral images classification. Nonparametric feature extraction methods show better performance compared to parametric ones when distribution of classes is non normal-like. Moreover, they can extract more features than parametric methods do. In this paper, a new nonparametric linear feature extraction method is introduced for classification of hyperspectral images. The proposed method has no free parameter and its novelty can be discussed in two parts. First, neighbor samples are specified by using Parzen window idea for determining local mean. Second, two new weighting functions are used. Samples close to class boundaries will have more weight in the between-class scatter matrix formation and samples close to class mean will have more weight in the within-class scatter matrix formation. The experimental results on three real hyperspectral data sets, Indian Pines, Salinas and Pavia University, demonstrate that the proposed method has better performance in comparison with some other nonparametric and parametric feature extraction methods.
Psychological Adjustment and Homosexuality.
ERIC Educational Resources Information Center
Gonsiorek, John C.
In this paper, the diverse literature bearing on the topic of homosexuality and psychological adjustment is critically reviewed and synthesized. The first chapter discusses the most crucial methodological issue in this area, the problem of sampling. The kinds of samples used to date are critically examined, and some suggestions for improved…
NASA Technical Reports Server (NTRS)
1986-01-01
Corning Glass Works' Serengeti Driver sunglasses are unique in that their lenses self-adjust and filter light while suppressing glare. They eliminate more than 99% of the ultraviolet rays in sunlight. The frames are based on the NASA Anthropometric Source Book.
Hunter, Steven L.
2002-01-01
An inclinometer utilizing synchronous demodulation for high resolution and electronic offset adjustment provides a wide dynamic range without any moving components. A device encompassing a tiltmeter and accompanying electronic circuitry provides quasi-leveled tilt sensors that detect highly resolved tilt change without signal saturation.
ERIC Educational Resources Information Center
Sasser, Tyler R.; Bierman, Karen L.
2012-01-01
The aim of this study was to examine co-variation in the development of self-regulatory skills evident in pre-kindergarten and evaluate the implications of that variation for school adjustment in kindergarten and first grade. Measures of self-regulatory skill development included: direct assessments of EF (executive function) (e.g., Peg Tapping,…
Ma, Weiping; Feng, Yang; Chen, Kani; Ying, Zhiliang
2015-11-01
Motivated by modeling and analysis of mass-spectrometry data, a semi- and nonparametric model is proposed that consists of linear parametric components for individual location and scale and a nonparametric regression function for the common shape. A multi-step approach is developed that simultaneously estimates the parametric components and the nonparametric function. Under certain regularity conditions, it is shown that the resulting estimators is consistent and asymptotic normal for the parametric part and achieve the optimal rate of convergence for the nonparametric part when the bandwidth is suitably chosen. Simulation results are presented to demonstrate the effectiveness and finite-sample performance of the method. The method is also applied to a SELDI-TOF mass spectrometry data set from a study of liver cancer patients.
Recurrence Analysis of Eddy Covariance Fluxes
NASA Astrophysics Data System (ADS)
Lange, Holger; Flach, Milan; Foken, Thomas; Hauhs, Michael
2015-04-01
The eddy covariance (EC) method is one key method to quantify fluxes in biogeochemical cycles in general, and carbon and energy transport across the vegetation-atmosphere boundary layer in particular. EC data from the worldwide net of flux towers (Fluxnet) have also been used to validate biogeochemical models. The high resolution data are usually obtained at 20 Hz sampling rate but are affected by missing values and other restrictions. In this contribution, we investigate the nonlinear dynamics of EC fluxes using Recurrence Analysis (RA). High resolution data from the site DE-Bay (Waldstein-Weidenbrunnen) and fluxes calculated at half-hourly resolution from eight locations (part of the La Thuile dataset) provide a set of very long time series to analyze. After careful quality assessment and Fluxnet standard gapfilling pretreatment, we calculate properties and indicators of the recurrent structure based both on Recurrence Plots as well as Recurrence Networks. Time series of RA measures obtained from windows moving along the time axis are presented. Their interpretation is guided by three different questions: (1) Is RA able to discern periods where the (atmospheric) conditions are particularly suitable to obtain reliable EC fluxes? (2) Is RA capable to detect dynamical transitions (different behavior) beyond those obvious from visual inspection? (3) Does RA contribute to an understanding of the nonlinear synchronization between EC fluxes and atmospheric parameters, which is crucial for both improving carbon flux models as well for reliable interpolation of gaps? (4) Is RA able to recommend an optimal time resolution for measuring EC data and for analyzing EC fluxes? (5) Is it possible to detect non-trivial periodicities with a global RA? We will demonstrate that the answers to all five questions is affirmative, and that RA provides insights into EC dynamics not easily obtained otherwise.
Inflation in general covariant theory of gravity
NASA Astrophysics Data System (ADS)
Huang, Yongqing; Wang, Anzhong; Wu, Qiang
2012-10-01
In this paper, we study inflation in the framework of the nonrelativistic general covariant theory of the Hořava-Lifshitz gravity with the projectability condition and an arbitrary coupling constant λ. We find that the Friedmann-Robterson-Walker (FRW) universe is necessarily flat in such a setup. We work out explicitly the linear perturbations of the flat FRW universe without specifying to a particular gauge, and find that the perturbations are different from those obtained in general relativity, because of the presence of the high-order spatial derivative terms. Applying the general formulas to a single scalar field, we show that in the sub-horizon regions, the metric and scalar field are tightly coupled and have the same oscillating frequencies. In the super-horizon regions, the perturbations become adiabatic, and the comoving curvature perturbation is constant. We also calculate the power spectra and indices of both the scalar and tensor perturbations, and express them explicitly in terms of the slow roll parameters and the coupling constants of the high-order spatial derivative terms. In particular, we find that the perturbations, of both scalar and tensor, are almost scale-invariant, and, with some reasonable assumptions on the coupling coefficients, the spectrum index of the tensor perturbation is the same as that given in the minimum scenario in general relativity (GR), whereas the index for scalar perturbation in general depends on λ and is different from the standard GR value. The ratio of the scalar and tensor power spectra depends on the high-order spatial derivative terms, and can be different from that of GR significantly.
Schwinger mechanism in linear covariant gauges
NASA Astrophysics Data System (ADS)
Aguilar, A. C.; Binosi, D.; Papavassiliou, J.
2017-02-01
In this work we explore the applicability of a special gluon mass generating mechanism in the context of the linear covariant gauges. In particular, the implementation of the Schwinger mechanism in pure Yang-Mills theories hinges crucially on the inclusion of massless bound-state excitations in the fundamental nonperturbative vertices of the theory. The dynamical formation of such excitations is controlled by a homogeneous linear Bethe-Salpeter equation, whose nontrivial solutions have been studied only in the Landau gauge. Here, the form of this integral equation is derived for general values of the gauge-fixing parameter, under a number of simplifying assumptions that reduce the degree of technical complexity. The kernel of this equation consists of fully dressed gluon propagators, for which recent lattice data are used as input, and of three-gluon vertices dressed by a single form factor, which is modeled by means of certain physically motivated Ansätze. The gauge-dependent terms contributing to this kernel impose considerable restrictions on the infrared behavior of the vertex form factor; specifically, only infrared finite Ansätze are compatible with the existence of nontrivial solutions. When such Ansätze are employed, the numerical study of the integral equation reveals a continuity in the type of solutions as one varies the gauge-fixing parameter, indicating a smooth departure from the Landau gauge. Instead, the logarithmically divergent form factor displaying the characteristic "zero crossing," while perfectly consistent in the Landau gauge, has to undergo a dramatic qualitative transformation away from it, in order to yield acceptable solutions. The possible implications of these results are briefly discussed.
Quantification of Covariance in Tropical Cyclone Activity across Teleconnected Basins
NASA Astrophysics Data System (ADS)
Tolwinski-Ward, S. E.; Wang, D.
2015-12-01
Rigorous statistical quantification of natural hazard covariance across regions has important implications for risk management, and is also of fundamental scientific interest. We present a multivariate Bayesian Poisson regression model for inferring the covariance in tropical cyclone (TC) counts across multiple ocean basins and across Saffir-Simpson intensity categories. Such covariability results from the influence of large-scale modes of climate variability on local environments that can alternately suppress or enhance TC genesis and intensification, and our model also simultaneously quantifies the covariance of TC counts with various climatic modes in order to deduce the source of inter-basin TC covariability. The model explicitly treats the time-dependent uncertainty in observed maximum sustained wind data, and hence the nominal intensity category of each TC. Differences in annual TC counts as measured by different agencies are also formally addressed. The probabilistic output of the model can be probed for probabilistic answers to such questions as: - Does the relationship between different categories of TCs differ statistically by basin? - Which climatic predictors have significant relationships with TC activity in each basin? - Are the relationships between counts in different basins conditionally independent given the climatic predictors, or are there other factors at play affecting inter-basin covariability? - How can a portfolio of insured property be optimized across space to minimize risk? Although we present results of our model applied to TCs, the framework is generalizable to covariance estimation between multivariate counts of natural hazards across regions and/or across peril types.
Action recognition from video using feature covariance matrices.
Guo, Kai; Ishwar, Prakash; Konrad, Janusz
2013-06-01
We propose a general framework for fast and accurate recognition of actions in video using empirical covariance matrices of features. A dense set of spatio-temporal feature vectors are computed from video to provide a localized description of the action, and subsequently aggregated in an empirical covariance matrix to compactly represent the action. Two supervised learning methods for action recognition are developed using feature covariance matrices. Common to both methods is the transformation of the classification problem in the closed convex cone of covariance matrices into an equivalent problem in the vector space of symmetric matrices via the matrix logarithm. The first method applies nearest-neighbor classification using a suitable Riemannian metric for covariance matrices. The second method approximates the logarithm of a query covariance matrix by a sparse linear combination of the logarithms of training covariance matrices. The action label is then determined from the sparse coefficients. Both methods achieve state-of-the-art classification performance on several datasets, and are robust to action variability, viewpoint changes, and low object resolution. The proposed framework is conceptually simple and has low storage and computational requirements making it attractive for real-time implementation.
Propensity score method: a non-parametric technique to reduce model dependence
2017-01-01
Propensity score analysis (PSA) is a powerful technique that it balances pretreatment covariates, making the causal effect inference from observational data as reliable as possible. The use of PSA in medical literature has increased exponentially in recent years, and the trend continue to rise. The article introduces rationales behind PSA, followed by illustrating how to perform PSA in R with MatchIt package. There are a variety of methods available for PS matching such as nearest neighbors, full matching, exact matching and genetic matching. The task can be easily done by simply assigning a string value to the method argument in the matchit() function. The generic summary() and plot() functions can be applied to an object of class matchit to check covariate balance after matching. Furthermore, there is a useful package PSAgraphics that contains several graphical functions to check covariate balance between treatment groups across strata. If covariate balance is not achieved, one can modify model specifications or use other techniques such as random forest and recursive partitioning to better represent the underlying structure between pretreatment covariates and treatment assignment. The process can be repeated until the desirable covariate balance is achieved. PMID:28164092
Parametric vs. non-parametric statistics of low resolution electromagnetic tomography (LORETA).
Thatcher, R W; North, D; Biver, C
2005-01-01
This study compared the relative statistical sensitivity of non-parametric and parametric statistics of 3-dimensional current sources as estimated by the EEG inverse solution Low Resolution Electromagnetic Tomography (LORETA). One would expect approximately 5% false positives (classification of a normal as abnormal) at the P < .025 level of probability (two tailed test) and approximately 1% false positives at the P < .005 level. EEG digital samples (2 second intervals sampled 128 Hz, 1 to 2 minutes eyes closed) from 43 normal adult subjects were imported into the Key Institute's LORETA program. We then used the Key Institute's cross-spectrum and the Key Institute's LORETA output files (*.lor) as the 2,394 gray matter pixel representation of 3-dimensional currents at different frequencies. The mean and standard deviation *.lor files were computed for each of the 2,394 gray matter pixels for each of the 43 subjects. Tests of Gaussianity and different transforms were computed in order to best approximate a normal distribution for each frequency and gray matter pixel. The relative sensitivity of parametric vs. non-parametric statistics were compared using a "leave-one-out" cross validation method in which individual normal subjects were withdrawn and then statistically classified as being either normal or abnormal based on the remaining subjects. Log10 transforms approximated Gaussian distribution in the range of 95% to 99% accuracy. Parametric Z score tests at P < .05 cross-validation demonstrated an average misclassification rate of approximately 4.25%, and range over the 2,394 gray matter pixels was 27.66% to 0.11%. At P < .01 parametric Z score cross-validation false positives were 0.26% and ranged from 6.65% to 0% false positives. The non-parametric Key Institute's t-max statistic at P < .05 had an average misclassification error rate of 7.64% and ranged from 43.37% to 0.04% false positives. The nonparametric t-max at P < .01 had an average misclassification rate
Hawking radiation, covariant boundary conditions, and vacuum states
Banerjee, Rabin; Kulkarni, Shailesh
2009-04-15
The basic characteristics of the covariant chiral current
Covariance Generation Using CONRAD and SAMMY Computer Codes
Leal, Luiz C; Derrien, Herve; De Saint Jean, C; Noguere, G; Ruggieri, J M
2009-01-01
Covariance generation in the resolved resonance region can be generated using the computer codes CONRAD and SAMMY. These codes use formalisms derived from the R-matrix methodology together with the generalized least squares technique to obtain resonance parameter. In addition, resonance parameter covariance is also obtained. Results of covariance calculations for a simple case of the s-wave resonance parameters of 48Ti in the energy region 10-5 eV to 300 keV are compared. The retroactive approach included in CONRAD and SAMMY was used.
Reverse attenuation in interaction terms due to covariate measurement error.
Muff, Stefanie; Keller, Lukas F
2015-11-01
Covariate measurement error may cause biases in parameters of regression coefficients in generalized linear models. The influence of measurement error on interaction parameters has, however, only rarely been investigated in depth, and if so, attenuation effects were reported. In this paper, we show that also reverse attenuation of interaction effects may emerge, namely when heteroscedastic measurement error or sampling variances of a mismeasured covariate are present, which are not unrealistic scenarios in practice. Theoretical findings are illustrated with simulations. A Bayesian approach employing integrated nested Laplace approximations is suggested to model the heteroscedastic measurement error and covariate variances, and an application shows that the method is able to reveal approximately correct parameter estimates.
Covariance matrices and applications to the field of nuclear data
Smith, D.L.
1981-11-01
A student's introduction to covariance error analysis and least-squares evaluation of data is provided. It is shown that the basic formulas used in error propagation can be derived from a consideration of the geometry of curvilinear coordinates. Procedures for deriving covariances for scaler and vector functions of several variables are presented. Proper methods for reporting experimental errors and for deriving covariance matrices from these errors are indicated. The generalized least-squares method for evaluating experimental data is described. Finally, the use of least-squares techniques in data fitting applications is discussed. Specific examples of the various procedures are presented to clarify the concepts.
The importance of covariance in nuclear data uncertainty propagation studies
Benstead, J.
2012-07-01
A study has been undertaken to investigate what proportion of the uncertainty propagated through plutonium critical assembly calculations is due to the covariances between the fission cross section in different neutron energy groups. The uncertainties on k{sub eff} calculated show that the presence of covariances between the cross section in different neutron energy groups accounts for approximately 27-37% of the propagated uncertainty due to the plutonium fission cross section. This study also confirmed the validity of employing the sandwich equation, with associated sensitivity and covariance data, instead of a Monte Carlo sampling approach to calculating uncertainties for linearly varying systems. (authors)
NASA Technical Reports Server (NTRS)
Malin, Jane T.; Schrenkenghost, Debra K.
2001-01-01
The Adjustable Autonomy Testbed (AAT) is a simulation-based testbed located in the Intelligent Systems Laboratory in the Automation, Robotics and Simulation Division at NASA Johnson Space Center. The purpose of the testbed is to support evaluation and validation of prototypes of adjustable autonomous agent software for control and fault management for complex systems. The AA T project has developed prototype adjustable autonomous agent software and human interfaces for cooperative fault management. This software builds on current autonomous agent technology by altering the architecture, components and interfaces for effective teamwork between autonomous systems and human experts. Autonomous agents include a planner, flexible executive, low level control and deductive model-based fault isolation. Adjustable autonomy is intended to increase the flexibility and effectiveness of fault management with an autonomous system. The test domain for this work is control of advanced life support systems for habitats for planetary exploration. The CONFIG hybrid discrete event simulation environment provides flexible and dynamically reconfigurable models of the behavior of components and fluids in the life support systems. Both discrete event and continuous (discrete time) simulation are supported, and flows and pressures are computed globally. This provides fast dynamic simulations of interacting hardware systems in closed loops that can be reconfigured during operations scenarios, producing complex cascading effects of operations and failures. Current object-oriented model libraries support modeling of fluid systems, and models have been developed of physico-chemical and biological subsystems for processing advanced life support gases. In FY01, water recovery system models will be developed.
Cutburth, Ronald W.; Silva, Leonard L.
1988-01-01
An improved mounting stage of the type used for the detection of laser beams is disclosed. A stage center block is mounted on each of two opposite sides by a pair of spaced ball bearing tracks which provide stability as well as simplicity. The use of the spaced ball bearing pairs in conjunction with an adjustment screw which also provides support eliminates extraneous stabilization components and permits maximization of the area of the center block laser transmission hole.
True covariance simulation of the EUVE update filter
NASA Technical Reports Server (NTRS)
Bar-Itzhack, I. Y.; Harman, R. R.
1990-01-01
This paper presents a covariance analysis of the performance and sensitivity of the attitude determination Extended Kalman Filter (EKF) used by the On Board Computer (OBC) of the Extreme Ultra Violet Explorer (EUVE) spacecraft. The linearized dynamics and measurement equations of the error states are used in formulating the 'truth model' describing the real behavior of the systems involved. The 'design model' used by the OBC EKF is then obtained by reducing the order of the truth model. The covariance matrix of the EKF which uses the reduced order model is not the correct covariance of the EKF estimation error. A 'true covariance analysis' has to be carried out in order to evaluate the correct accuracy of the OBC generated estimates. The results of such analysis are presented which indicate both the performance and the sensitivity of the OBC EKF.
Optimal Estimation and Rank Detection for Sparse Spiked Covariance Matrices.
Cai, Tony; Ma, Zongming; Wu, Yihong
2015-04-01
This paper considers a sparse spiked covariancematrix model in the high-dimensional setting and studies the minimax estimation of the covariance matrix and the principal subspace as well as the minimax rank detection. The optimal rate of convergence for estimating the spiked covariance matrix under the spectral norm is established, which requires significantly different techniques from those for estimating other structured covariance matrices such as bandable or sparse covariance matrices. We also establish the minimax rate under the spectral norm for estimating the principal subspace, the primary object of interest in principal component analysis. In addition, the optimal rate for the rank detection boundary is obtained. This result also resolves the gap in a recent paper by Berthet and Rigollet [2] where the special case of rank one is considered.
True covariance simulation of the EUVE update filter
NASA Technical Reports Server (NTRS)
Bar-Itzhack, Itzhack Y.; Harman, R. R.
1989-01-01
A covariance analysis of the performance and sensitivity of the attitude determination Extended Kalman Filter (EKF) used by the On Board Computer (OBC) of the Extreme Ultra Violet Explorer (EUVE) spacecraft is presented. The linearized dynamics and measurement equations of the error states are derived which constitute the truth model describing the real behavior of the systems involved. The design model used by the OBC EKF is then obtained by reducing the order of the truth model. The covariance matrix of the EKF which uses the reduced order model is not the correct covariance of the EKF estimation error. A true covariance analysis has to be carried out in order to evaluate the correct accuracy of the OBC generated estimates. The results of such analysis are presented which indicate both the performance and the sensitivity of the OBC EKF.
Progress of Covariance Evaluation at the China Nuclear Data Center
Xu, R.; Zhang, Q.; Zhang, Y.; Liu, T.; Ge, Z.; Lu, H.; Sun, Z.; Yu, B.; Tang, G.
2015-01-15
Covariance evaluations at the China Nuclear Data Center focus on the cross sections of structural materials and actinides in the fast neutron energy range. In addition to the well-known Least-squares approach, a method based on the analysis of the sources of experimental uncertainties is especially introduced to generate a covariance matrix for a particular reaction for which multiple measurements are available. The scheme of the covariance evaluation flow is presented, and an example of n+{sup 90}Zr is given to illuminate the whole procedure. It is proven that the accuracy of measurements can be properly incorporated into the covariance and the long-standing small uncertainty problem can be avoided.
Covariance Matrix Evaluations for Independent Mass Fission Yields
Terranova, N.; Serot, O.; Archier, P.; De Saint Jean, C.
2015-01-15
Recent needs for more accurate fission product yields include covariance information to allow improved uncertainty estimations of the parameters used by design codes. The aim of this work is to investigate the possibility to generate more reliable and complete uncertainty information on independent mass fission yields. Mass yields covariances are estimated through a convolution between the multi-Gaussian empirical model based on Brosa's fission modes, which describe the pre-neutron mass yields, and the average prompt neutron multiplicity curve. The covariance generation task has been approached using the Bayesian generalized least squared method through the CONRAD code. Preliminary results on mass yields variance-covariance matrix will be presented and discussed from physical grounds in the case of {sup 235}U(n{sub th}, f) and {sup 239}Pu(n{sub th}, f) reactions.
Empirical State Error Covariance Matrix for Batch Estimation
NASA Technical Reports Server (NTRS)
Frisbee, Joe
2015-01-01
State estimation techniques effectively provide mean state estimates. However, the theoretical state error covariance matrices provided as part of these techniques often suffer from a lack of confidence in their ability to describe the uncertainty in the estimated states. By a reinterpretation of the equations involved in the weighted batch least squares algorithm, it is possible to directly arrive at an empirical state error covariance matrix. The proposed empirical state error covariance matrix will contain the effect of all error sources, known or not. This empirical error covariance matrix may be calculated as a side computation for each unique batch solution. Results based on the proposed technique will be presented for a simple, two observer and measurement error only problem.
Cosmic shear covariance: the log-normal approximation
NASA Astrophysics Data System (ADS)
Hilbert, S.; Hartlap, J.; Schneider, P.
2011-12-01
Context. Accurate estimates of the errors on the cosmological parameters inferred from cosmic shear surveys require accurate estimates of the covariance of the cosmic shear correlation functions. Aims: We seek approximations to the cosmic shear covariance that are as easy to use as the common approximations based on normal (Gaussian) statistics, but yield more accurate covariance matrices and parameter errors. Methods: We derive expressions for the cosmic shear covariance under the assumption that the underlying convergence field follows log-normal statistics. We also derive a simplified version of this log-normal approximation by only retaining the most important terms beyond normal statistics. We use numerical simulations of weak lensing to study how well the normal, log-normal, and simplified log-normal approximations as well as empirical corrections to the normal approximation proposed in the literature reproduce shear covariances for cosmic shear surveys. We also investigate the resulting confidence regions for cosmological parameters inferred from such surveys. Results: We find that the normal approximation substantially underestimates the cosmic shear covariances and the inferred parameter confidence regions, in particular for surveys with small fields of view and large galaxy densities, but also for very wide surveys. In contrast, the log-normal approximation yields more realistic covariances and confidence regions, but also requires evaluating slightly more complicated expressions. However, the simplified log-normal approximation, although as simple as the normal approximation, yields confidence regions that are almost as accurate as those obtained from the log-normal approximation. The empirical corrections to the normal approximation do not yield more accurate covariances and confidence regions than the (simplified) log-normal approximation. Moreover, they fail to produce positive-semidefinite data covariance matrices in certain cases, rendering them
NASA Astrophysics Data System (ADS)
Yang, Zhongwen; Hsu, Kuolin; Sorooshian, Soroosh; Xu, Xinyi; Braithwaite, Dan; Verbist, Koen M. J.
2016-04-01
Satellite-based precipitation estimates (SPEs) are promising alternative precipitation data for climatic and hydrological applications, especially for regions where ground-based observations are limited. However, existing satellite-based rainfall estimations are subject to systematic biases. This study aims to adjust the biases in the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) rainfall data over Chile, using gauge observations as reference. A novel bias adjustment framework, termed QM-GW, is proposed based on the nonparametric quantile mapping approach and a Gaussian weighting interpolation scheme. The PERSIANN-CCS precipitation estimates (daily, 0.04°×0.04°) over Chile are adjusted for the period of 2009-2014. The historical data (satellite and gauge) for 2009-2013 are used to calibrate the methodology; nonparametric cumulative distribution functions of satellite and gauge observations are estimated at every 1°×1° box region. One year (2014) of gauge data was used for validation. The results show that the biases of the PERSIANN-CCS precipitation data are effectively reduced. The spatial patterns of adjusted satellite rainfall show high consistency to the gauge observations, with reduced root-mean-square errors and mean biases. The systematic biases of the PERSIANN-CCS precipitation time series, at both monthly and daily scales, are removed. The extended validation also verifies that the proposed approach can be applied to adjust SPEs into the future, without further need for ground-based measurements. This study serves as a valuable reference for the bias adjustment of existing SPEs using gauge observations worldwide.
Explicitly covariant dispersion relations and self-induced transparency
NASA Astrophysics Data System (ADS)
Mahajan, S. M.; Asenjo, Felipe A.
2017-02-01
Explicitly covariant dispersion relations for a variety of plasma waves in unmagnetized and magnetized plasmas are derived in a systematic manner from a fully covariant plasma formulation. One needs to invoke relatively little known invariant combinations constructed from the ambient electromagnetic fields and the wave vector to accomplish the program. The implication of this work applied to the self-induced transparency effect is discussed. Some problems arising from the inconsistent use of relativity are pointed out.
New capabilities for processing covariance data in resonance region
Wiarda, D.; Dunn, M. E.; Greene, N. M.; Larson, N. M.; Leal, L. C.
2006-07-01
The AMPX [1] code system is a modular system of FORTRAN computer programs that relate to nuclear analysis with a primary emphasis on tasks associated with the production and use of multi group and continuous energy cross sections. The module PUFF-III within this code system handles the creation of multi group covariance data from ENDF information. The resulting covariances are saved in COVERX format [2]. We recently expanded the capabilities of PUFF-III to include full handling of covariance data in the resonance region (resolved as well as unresolved). The new program handles all resonance covariance formats in File 32 except for the long-range covariance sub sections. The new program has been named PUFF-IV. To our knowledge, PUFF-IV is the first processing code that can address both the new ENDF format for resolved resonance parameters and the new ENDF 'compact' covariance format. The existing code base was rewritten in Fortran 90 to allow for a more modular design. Results are identical between the new and old versions within rounding errors, where applicable. Automatic test cases have been added to ensure that consistent results are generated across computer systems. (authors)
Large Covariance Estimation by Thresholding Principal Orthogonal Complements.
Fan, Jianqing; Liao, Yuan; Mincheva, Martina
2013-09-01
This paper deals with the estimation of a high-dimensional covariance with a conditional sparsity structure and fast-diverging eigenvalues. By assuming sparse error covariance matrix in an approximate factor model, we allow for the presence of some cross-sectional correlation even after taking out common but unobservable factors. We introduce the Principal Orthogonal complEment Thresholding (POET) method to explore such an approximate factor structure with sparsity. The POET estimator includes the sample covariance matrix, the factor-based covariance matrix (Fan, Fan, and Lv, 2008), the thresholding estimator (Bickel and Levina, 2008) and the adaptive thresholding estimator (Cai and Liu, 2011) as specific examples. We provide mathematical insights when the factor analysis is approximately the same as the principal component analysis for high-dimensional data. The rates of convergence of the sparse residual covariance matrix and the conditional sparse covariance matrix are studied under various norms. It is shown that the impact of estimating the unknown factors vanishes as the dimensionality increases. The uniform rates of convergence for the unobserved factors and their factor loadings are derived. The asymptotic results are also verified by extensive simulation studies. Finally, a real data application on portfolio allocation is presented.
Real-time probabilistic covariance tracking with efficient model update.
Wu, Yi; Cheng, Jian; Wang, Jinqiao; Lu, Hanqing; Wang, Jun; Ling, Haibin; Blasch, Erik; Bai, Li
2012-05-01
The recently proposed covariance region descriptor has been proven robust and versatile for a modest computational cost. The covariance matrix enables efficient fusion of different types of features, where the spatial and statistical properties, as well as their correlation, are characterized. The similarity between two covariance descriptors is measured on Riemannian manifolds. Based on the same metric but with a probabilistic framework, we propose a novel tracking approach on Riemannian manifolds with a novel incremental covariance tensor learning (ICTL). To address the appearance variations, ICTL incrementally learns a low-dimensional covariance tensor representation and efficiently adapts online to appearance changes of the target with only O(1) computational complexity, resulting in a real-time performance. The covariance-based representation and the ICTL are then combined with the particle filter framework to allow better handling of background clutter, as well as the temporary occlusions. We test the proposed probabilistic ICTL tracker on numerous benchmark sequences involving different types of challenges including occlusions and variations in illumination, scale, and pose. The proposed approach demonstrates excellent real-time performance, both qualitatively and quantitatively, in comparison with several previously proposed trackers.
A three domain covariance framework for EEG/MEG data.
Roś, Beata P; Bijma, Fetsje; de Gunst, Mathisca C M; de Munck, Jan C
2015-10-01
In this paper we introduce a covariance framework for the analysis of single subject EEG and MEG data that takes into account observed temporal stationarity on small time scales and trial-to-trial variations. We formulate a model for the covariance matrix, which is a Kronecker product of three components that correspond to space, time and epochs/trials, and consider maximum likelihood estimation of the unknown parameter values. An iterative algorithm that finds approximations of the maximum likelihood estimates is proposed. Our covariance model is applicable in a variety of cases where spontaneous EEG or MEG acts as source of noise and realistic noise covariance estimates are needed, such as in evoked activity studies, or where the properties of spontaneous EEG or MEG are themselves the topic of interest, like in combined EEG-fMRI experiments in which the correlation between EEG and fMRI signals is investigated. We use a simulation study to assess the performance of the estimator and investigate the influence of different assumptions about the covariance factors on the estimated covariance matrix and on its components. We apply our method to real EEG and MEG data sets.
Large Covariance Estimation by Thresholding Principal Orthogonal Complements
Fan, Jianqing; Liao, Yuan; Mincheva, Martina
2012-01-01
This paper deals with the estimation of a high-dimensional covariance with a conditional sparsity structure and fast-diverging eigenvalues. By assuming sparse error covariance matrix in an approximate factor model, we allow for the presence of some cross-sectional correlation even after taking out common but unobservable factors. We introduce the Principal Orthogonal complEment Thresholding (POET) method to explore such an approximate factor structure with sparsity. The POET estimator includes the sample covariance matrix, the factor-based covariance matrix (Fan, Fan, and Lv, 2008), the thresholding estimator (Bickel and Levina, 2008) and the adaptive thresholding estimator (Cai and Liu, 2011) as specific examples. We provide mathematical insights when the factor analysis is approximately the same as the principal component analysis for high-dimensional data. The rates of convergence of the sparse residual covariance matrix and the conditional sparse covariance matrix are studied under various norms. It is shown that the impact of estimating the unknown factors vanishes as the dimensionality increases. The uniform rates of convergence for the unobserved factors and their factor loadings are derived. The asymptotic results are also verified by extensive simulation studies. Finally, a real data application on portfolio allocation is presented. PMID:24348088
Alterations in Anatomical Covariance in the Prematurely Born.
Scheinost, Dustin; Kwon, Soo Hyun; Lacadie, Cheryl; Vohr, Betty R; Schneider, Karen C; Papademetris, Xenophon; Constable, R Todd; Ment, Laura R
2015-10-22
Preterm (PT) birth results in long-term alterations in functional and structural connectivity, but the related changes in anatomical covariance are just beginning to be explored. To test the hypothesis that PT birth alters patterns of anatomical covariance, we investigated brain volumes of 25 PTs and 22 terms at young adulthood using magnetic resonance imaging. Using regional volumetrics, seed-based analyses, and whole brain graphs, we show that PT birth is associated with reduced volume in bilateral temporal and inferior frontal lobes, left caudate, left fusiform, and posterior cingulate for prematurely born subjects at young adulthood. Seed-based analyses demonstrate altered patterns of anatomical covariance for PTs compared with terms. PTs exhibit reduced covariance with R Brodmann area (BA) 47, Broca's area, and L BA 21, Wernicke's area, and white matter volume in the left prefrontal lobe, but increased covariance with R BA 47 and left cerebellum. Graph theory analyses demonstrate that measures of network complexity are significantly less robust in PTs compared with term controls. Volumes in regions showing group differences are significantly correlated with phonological awareness, the fundamental basis for reading acquisition, for the PTs. These data suggest both long-lasting and clinically significant alterations in the covariance in the PTs at young adulthood.
The performance analysis based on SAR sample covariance matrix.
Erten, Esra
2012-01-01
Multi-channel systems appear in several fields of application in science. In the Synthetic Aperture Radar (SAR) context, multi-channel systems may refer to different domains, as multi-polarization, multi-interferometric or multi-temporal data, or even a combination of them. Due to the inherent speckle phenomenon present in SAR images, the statistical description of the data is almost mandatory for its utilization. The complex images acquired over natural media present in general zero-mean circular Gaussian characteristics. In this case, second order statistics as the multi-channel covariance matrix fully describe the data. For practical situations however, the covariance matrix has to be estimated using a limited number of samples, and this sample covariance matrix follow the complex Wishart distribution. In this context, the eigendecomposition of the multi-channel covariance matrix has been shown in different areas of high relevance regarding the physical properties of the imaged scene. Specifically, the maximum eigenvalue of the covariance matrix has been frequently used in different applications as target or change detection, estimation of the dominant scattering mechanism in polarimetric data, moving target indication, etc. In this paper, the statistical behavior of the maximum eigenvalue derived from the eigendecomposition of the sample multi-channel covariance matrix in terms of multi-channel SAR images is simplified for SAR community. Validation is performed against simulated data and examples of estimation and detection problems using the analytical expressions are as well given.
Gaussian covariance matrices for anisotropic galaxy clustering measurements
NASA Astrophysics Data System (ADS)
Grieb, Jan Niklas; Sánchez, Ariel G.; Salazar-Albornoz, Salvador; Dalla Vecchia, Claudio
2016-04-01
Measurements of the redshift-space galaxy clustering have been a prolific source of cosmological information in recent years. Accurate covariance estimates are an essential step for the validation of galaxy clustering models of the redshift-space two-point statistics. Usually, only a limited set of accurate N-body simulations is available. Thus, assessing the data covariance is not possible or only leads to a noisy estimate. Further, relying on simulated realizations of the survey data means that tests of the cosmology dependence of the covariance are expensive. With these points in mind, this work presents a simple theoretical model for the linear covariance of anisotropic galaxy clustering observations with synthetic catalogues. Considering the Legendre moments (`multipoles') of the two-point statistics and projections into wide bins of the line-of-sight parameter (`clustering wedges'), we describe the modelling of the covariance for these anisotropic clustering measurements for galaxy samples with a trivial geometry in the case of a Gaussian approximation of the clustering likelihood. As main result of this paper, we give the explicit formulae for Fourier and configuration space covariance matrices. To validate our model, we create synthetic halo occupation distribution galaxy catalogues by populating the haloes of an ensemble of large-volume N-body simulations. Using linear and non-linear input power spectra, we find very good agreement between the model predictions and the measurements on the synthetic catalogues in the quasi-linear regime.
Modelling the random effects covariance matrix in longitudinal data.
Daniels, Michael J; Zhao, Yan D
2003-05-30
A common class of models for longitudinal data are random effects (mixed) models. In these models, the random effects covariance matrix is typically assumed constant across subject. However, in many situations this matrix may differ by measured covariates. In this paper, we propose an approach to model the random effects covariance matrix by using a special Cholesky decomposition of the matrix. In particular, we will allow the parameters that result from this decomposition to depend on subject-specific covariates and also explore ways to parsimoniously model these parameters. An advantage of this parameterization is that there is no concern about the positive definiteness of the resulting estimator of the covariance matrix. In addition, the parameters resulting from this decomposition have a sensible interpretation. We propose fully Bayesian modelling for which a simple Gibbs sampler can be implemented to sample from the posterior distribution of the parameters. We illustrate these models on data from depression studies and examine the impact of heterogeneity in the covariance matrix on estimation of both fixed and random effects.
NASA Technical Reports Server (NTRS)
Farley, Gary L.
1994-01-01
Local characteristics of fabrics varied to suit special applications. Adjustable reed machinery proposed for use in weaving fabrics in various net shapes, widths, yarn spacings, and yarn angles. Locations of edges of fabric and configuration of warp and filling yarns varied along fabric to obtain specified properties. In machinery, reed wires mounted in groups on sliders, mounted on lengthwise rails in reed frame. Mechanisms incorporated to move sliders lengthwise, parallel to warp yarns, by sliding them along rails; move sliders crosswise by translating reed frame rails perpendicular to warp yarns; and crosswise by spreading reed rails within group. Profile of reed wires in group on each slider changed.