Empirical Bayes Methods with Applications

Empirical Bayes Methods with Applications
Author: J.S. Maritz
Publisher: CRC Press
Total Pages: 296
Release: 2018-01-18
Genre: Mathematics
ISBN: 1351080113

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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.

Empirical Bayes Methods

Empirical Bayes Methods
Author: J. S. Maritz
Publisher:
Total Pages: 176
Release: 1970
Genre: Mathematics
ISBN:

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Empirical Bayes Methods with Applications

Empirical Bayes Methods with Applications
Author: J.S. Maritz
Publisher: CRC Press
Total Pages: 360
Release: 2018-01-18
Genre: Mathematics
ISBN: 1351088564

Download Empirical Bayes Methods with Applications Book in PDF, Epub and Kindle

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.

Revisiting Empirical Bayes Methods and Applications to Special Types of Data

Revisiting Empirical Bayes Methods and Applications to Special Types of Data
Author: Xiuwen Duan
Publisher:
Total Pages:
Release: 2021
Genre:
ISBN:

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Empirical Bayes methods have been around for a long time and have a wide range of applications. These methods provide a way in which historical data can be aggregated to provide estimates of the posterior mean. This thesis revisits some of the empirical Bayesian methods and develops new applications. We first look at a linear empirical Bayes estimator and apply it on ranking and symbolic data. Next, we consider Tweedie's formula and show how it can be applied to analyze a microarray dataset. The application of the formula is simplified with the Pearson system of distributions. Saddlepoint approximations enable us to generalize several results in this direction. The results show that the proposed methods perform well in applications to real data sets.

Large-Scale Inference

Large-Scale Inference
Author: Bradley Efron
Publisher: Cambridge University Press
Total Pages:
Release: 2012-11-29
Genre: Mathematics
ISBN: 1139492136

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We live in a new age for statistical inference, where modern scientific technology such as microarrays and fMRI machines routinely produce thousands and sometimes millions of parallel data sets, each with its own estimation or testing problem. Doing thousands of problems at once is more than repeated application of classical methods. Taking an empirical Bayes approach, Bradley Efron, inventor of the bootstrap, shows how information accrues across problems in a way that combines Bayesian and frequentist ideas. Estimation, testing and prediction blend in this framework, producing opportunities for new methodologies of increased power. New difficulties also arise, easily leading to flawed inferences. This book takes a careful look at both the promise and pitfalls of large-scale statistical inference, with particular attention to false discovery rates, the most successful of the new statistical techniques. Emphasis is on the inferential ideas underlying technical developments, illustrated using a large number of real examples.

The Theory and Applications of Reliability With Emphasis on Bayesian and Nonparametric Methods

The Theory and Applications of Reliability With Emphasis on Bayesian and Nonparametric Methods
Author: Chris Tsokos
Publisher: Elsevier
Total Pages: 566
Release: 2012-12-02
Genre: Technology & Engineering
ISBN: 032314585X

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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.

A Mixture Model Approach to Empirical Bayes Testing and Estimation

A Mixture Model Approach to Empirical Bayes Testing and Estimation
Author: Omkar Muralidharan
Publisher: Stanford University
Total Pages: 89
Release: 2011
Genre:
ISBN:

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Many modern statistical problems require making similar decisions or estimates for many different entities. For example, we may ask whether each of 10,000 genes is associated with some disease, or try to measure the degree to which each is associated with the disease. As in this example, the entities can often be divided into a vast majority of "null" objects and a small minority of interesting ones. Empirical Bayes is a useful technique for such situations, but finding the right empirical Bayes method for each problem can be difficult. Mixture models, however, provide an easy and effective way to apply empirical Bayes. This thesis motivates mixture models by analyzing a simple high-dimensional problem, and shows their practical use by applying them to detecting single nucleotide polymorphisms.

Empirical Bayes and Likelihood Inference

Empirical Bayes and Likelihood Inference
Author: S.E. Ahmed
Publisher: Springer Science & Business Media
Total Pages: 260
Release: 2001
Genre: Mathematics
ISBN: 9780387950181

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Bayesian and such approaches to inference have a number of points of close contact, especially from an asymptotic point of view. Both emphasize the construction of interval estimates of unknown parameters. In this volume, researchers present recent work on several aspects of Bayesian, likelihood and empirical Bayes methods, presented at a workshop held in Montreal, Canada. The goal of the workshop was to explore the linkages among the methods, and to suggest new directions for research in the theory of inference.