Download or read book Non parametric Empirical Bayes Procedures written by Milton V. Johns and published by . This book was released on 1957 with total page 21 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Empirical Bayes Methods with Applications written by J.S. Maritz and published by CRC Press. This book was released on 2018-01-18 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: The second edition of Empirical Bayes Methods details are provided of the derivation and the performance of empirical Bayes rules for a variety of special models. Attention is given to the problem of assessing the goodness of an empirical Bayes estimator for a given set of prior data. A chapter is devoted to a discussion of alternatives to the empirical Bayes approach and there is also a chapter giving details of several actual applications of empirical Bayes method.
Download or read book Bayesian Nonparametrics written by J.K. Ghosh and published by Springer Science & Business Media. This book was released on 2006-05-11 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the first systematic treatment of Bayesian nonparametric methods and the theory behind them. It will also appeal to statisticians in general. The book is primarily aimed at graduate students and can be used as the text for a graduate course in Bayesian non-parametrics.
Download or read book A Non parametric Empirical Bayes Approach for Estimating a Process Average in Quality Control written by J. H. MacMillan and published by . This book was released on 1988 with total page 12 pages. Available in PDF, EPUB and Kindle. Book excerpt: "At Statistics Canada, acceptance sampling is used as a method of quality control for survey processing operations. The sampling plans which are used will ensure minimum inspection at a specific incoming error level. This error level is estimated by a quantity known as the process average. It is an unknown parameter which is usually estimated from current inspection results, but frequently the estimation is difficult because of small sample sizes. Greater accuracy in the estimate may be produced by using more data from previous samples to improve upon the current sample result. A non-parametric empirical Bayes estimator of the process average is presented. An approximate confidence interval is also constructed. Examples are provided"--Abstract.
Download or read book Empirical Bayes Methods written by J. S. Maritz and published by . This book was released on 1970 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Non parametric Empirical Bayes Estimation written by Hans Hedén and published by . This book was released on 1975 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Data Gathering Analysis and Protection of Privacy Through Randomized Response Techniques Qualitative and Quantitative Human Traits written by and published by Elsevier. This book was released on 2016-04-20 with total page 545 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Gathering, Analysis and Protection of Privacy through Randomized Response Techniques: Qualitative and Quantitative Human Traits tackles how to gather and analyze data relating to stigmatizing human traits. S.L. Warner invented RRT and published it in JASA, 1965. In the 50 years since, the subject has grown tremendously, with continued growth. This book comprehensively consolidates the literature to commemorate the inception of RR. Brings together all relevant aspects of randomized response and indirect questioning Tackles how to gather and analyze data relating to stigmatizing human traits Gives an encyclopedic coverage of the topic Covers recent developments and extrapolates to future trends
Download or read book On Empirical Bayes Procedures for Selecting Good Populations in Positive Exponential Family written by and published by . This book was released on 2001 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The problem of selecting good ones compared with a control from k(greater than or equal to 2) positive exponential family populations is considered in this paper. A nonparametric empirical Bayes approach is used to construct the selection procedures. It has been shown that the risks of the empirical Bayes procedures converge to the (minimum) Bayes risk with a rate of O(1/n), where n is the number of accumulated past observations at hand. Simulations were carried out to study the performance of the procedures for small to moderate values of n. The results of this study are provided in the paper.
Download or read book The Analysis of Gene Expression Data written by Giovanni Parmigiani and published by Springer Science & Business Media. This book was released on 2006-04-11 with total page 511 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents practical approaches for the analysis of data from gene expression micro-arrays. It describes the conceptual and methodological underpinning for a statistical tool and its implementation in software. The book includes coverage of various packages that are part of the Bioconductor project and several related R tools. The materials presented cover a range of software tools designed for varied audiences.
Download or read book Empirical Bayes Methods with Applications written by J.S. Maritz and published by CRC Press. This book was released on 2018-01-18 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: The second edition of Empirical Bayes Methods details are provided of the derivation and the performance of empirical Bayes rules for a variety of special models. Attention is given to the problem of assessing the goodness of an empirical Bayes estimator for a given set of prior data. A chapter is devoted to a discussion of alternatives to the empirical Bayes approach and there is also a chapter giving details of several actual applications of empirical Bayes method.
Download or read book The Theory and Applications of Reliability With Emphasis on Bayesian and Nonparametric Methods written by Chris Tsokos and published by Elsevier. This book was released on 2012-12-02 with total page 566 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Theory and Applications of Reliability: With Emphasis on Bayesian and Nonparametric Methods, Volume I covers the proceedings of the conference on ""The Theory and Applications of Reliability with Emphasis on Bayesian and Nonparametric Methods."" The conference is organized so as to have technical presentations, a clinical session, and round table discussions. This volume is a 29-chapter text that specifically deals with the theoretical aspects of reliability estimation. Considerable chapters on the technical sessions are devoted to initial findings on the theory and applications of reliability estimation, with special emphasis on Bayesian and nonparametric methods. A Bayesian analysis implies the use of suitable prior information in association with Bayes theorem while the nonparametric approach analyzes the reliability components and systems under the assumption of a time-to-failure distribution with a wide defining property rather than a specific parametric class of probability distributions. The clinical session chapters discuss the actual problems encountered in reliability estimation. The remaining chapters deal with the status of the subject areas and the empirical Bayes developments. These chapters also present various probabilistic and statistic methods for reliability estimation. Theoreticians and reliability engineers will find this book invaluable.
Download or read book Fully Nonparametric Empirical Bayes Estimation Via Projection Pursuit written by Stanford University. Dept. of Statistics. Laboratory for Computational Statistics and published by . This book was released on 1985 with total page 15 pages. Available in PDF, EPUB and Kindle. Book excerpt: The fully nonparametric formulation of the empirical Bayes estimation problem considers m populations characterized by conditional (sampling) distributions chosen independently by some unspecified random mechanism. No parametric constraints are imposed on the family of possible sampling distributions or on the prior mechanism which selects them. The quantity to be estimated subject to squared-error loss for each population is defined by a functional T(F) where F is the population sampling cdf. The empirical Bayes estimator is based on n iid observations from each population where n> 1. Asymptotically optimal procedures for this problem typically employ consistent nonparametric estimators of certain nonlinear conditional expectation functions. In this study a particular projection pursuit algorithm is used for this purpose. The proposed method is applied to the estimation of population means for several simulated data sets and one familiar real world data set. Certain possible extensions are discussed. Additional keywords: nonparametric regression; nonparametric estimation; charts; tables(data). (Author).
Download or read book Approximate Nonparametric Bayes Empirical Bayes Procedure for Estimating the Percent Nonconforming in Accepted Lots Using a Double Sampling Scheme written by William Paul Hahn and published by . This book was released on 1992 with total page 50 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Empirical Bayes Methods written by J. S. Maritz and published by Routledge. This book was released on 2018-03-05 with total page 299 pages. Available in PDF, EPUB and Kindle. Book excerpt: Originally published in 1970; with a second edition in 1989. Empirical Bayes methods use some of the apparatus of the pure Bayes approach, but an actual prior distribution is assumed to generate the data sequence. It can be estimated thus producing empirical Bayes estimates or decision rules. In this second edition, details are provided of the derivation and the performance of empirical Bayes rules for a variety of special models. Attention is given to the problem of assessing the goodness of an empirical Bayes estimator for a given set of prior data. Chapters also focus on alternatives to the empirical Bayes approach and actual applications of empirical Bayes methods.
Download or read book Nonparametric Perspectives on Empirical Bayes written by Nikolaos Ignatiadis and published by . This book was released on 2022 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In an empirical Bayes analysis, we use data from repeated sampling to imitate inferences made by an oracle Bayesian with extensive knowledge of the data-generating distribution. Existing results provide a comprehensive characterization of when and why empirical Bayes point estimates accurately recover oracle Bayes behavior--in particular when the likelihood of the individual statistical problems is known and all problems are relevant to each other. In this thesis, we build upon advances in the theory of nonparametric statistics, machine learning, and computation to make three-fold contributions to the empirical Bayes literature: 1) We develop flexible and practical confidence intervals that provide asymptotic frequentist coverage of empirical Bayes estimands, such as the posterior mean or the local false sign rate. The coverage statements hold even when the estimands are only partially identified or when empirical Bayes point estimates converge very slowly. 2) We show that it is possible to achieve near-Bayes optimal mean squared error for the estimation of n effect sizes in the setting where both the prior and the per-problem likelihood are unknown. The requirement of our method is that we have access to replicated data, that is, each effect size of interest is estimated from K> 1 noisy observations. 3) We tackle the issue of relevance in empirical Bayes estimation of effect sizes. We propose a method that shrinks toward a per-problem location determined by a machine learning model prediction of the effect given side-information. We establish an extension to the classic result of James-Stein, whereby our proposed estimator dominates the sample mean for each problem under quadratic risk; even if the side-information contains no information about the true effects, or the machine learning model is arbitrarily miscalibrated. Taken together, these results broaden the applicability of empirical Bayes methods in areas such as genomics, and large scale experimentation, and demonstrate that it is fruitful to revisit traditional ideas in the empirical Bayes literature through a modern lens. The above results largely draw upon the following papers: Ignatiadis and Wager (2019, 2022) and Ignatiadis, Saha, Sun, and Muralidharan (2021).
Download or read book Nonparametric Techniques in Statistical Inference written by Madan Lal Puri and published by . This book was released on 1970-11-02 with total page 644 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book, the proceedings of the first international symposium, surveys the current status of nonparametric theory. It contains thirty-five papers with discussions by some of the world's most eminent authorities in the field. The subjects discussed are testing and estimation, order statistics and allied problems, ranking and selection procedures, general theory, problems, relating to goodness - of -fit, decision theoretic and empirical Bayes procedures, and nonparametric methods and elementary statistics. (Author).
Download or read book Prior Processes and Their Applications written by Eswar G. Phadia and published by Springer. This book was released on 2016-07-27 with total page 337 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a systematic and comprehensive treatment of various prior processes that have been developed over the past four decades for dealing with Bayesian approach to solving selected nonparametric inference problems. This revised edition has been substantially expanded to reflect the current interest in this area. After an overview of different prior processes, it examines the now pre-eminent Dirichlet process and its variants including hierarchical processes, then addresses new processes such as dependent Dirichlet, local Dirichlet, time-varying and spatial processes, all of which exploit the countable mixture representation of the Dirichlet process. It subsequently discusses various neutral to right type processes, including gamma and extended gamma, beta and beta-Stacy processes, and then describes the Chinese Restaurant, Indian Buffet and infinite gamma-Poisson processes, which prove to be very useful in areas such as machine learning, information retrieval and featural modeling. Tailfree and Polya tree and their extensions form a separate chapter, while the last two chapters present the Bayesian solutions to certain estimation problems pertaining to the distribution function and its functional based on complete data as well as right censored data. Because of the conjugacy property of some of these processes, most solutions are presented in closed form. However, the current interest in modeling and treating large-scale and complex data also poses a problem – the posterior distribution, which is essential to Bayesian analysis, is invariably not in a closed form, making it necessary to resort to simulation. Accordingly, the book also introduces several computational procedures, such as the Gibbs sampler, Blocked Gibbs sampler and slice sampling, highlighting essential steps of algorithms while discussing specific models. In addition, it features crucial steps of proofs and derivations, explains the relationships between different processes and provides further clarifications to promote a deeper understanding. Lastly, it includes a comprehensive list of references, equipping readers to explore further on their own.
