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The Variational Form Of Bayes Estimators Of Normal Variance In The Linear Models Case
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Book Synopsis The Variational Form of Bayes Estimators of Normal Variance in the Linear Models Case by : Alfredo J. Julian
Download or read book The Variational Form of Bayes Estimators of Normal Variance in the Linear Models Case written by Alfredo J. Julian and published by . This book was released on 1992 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Bayesian Estimation and Experimental Design in Linear Regression Models by : Jürgen Pilz
Download or read book Bayesian Estimation and Experimental Design in Linear Regression Models written by Jürgen Pilz and published by . This book was released on 1991-07-09 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presents a clear treatment of the design and analysis of linear regression experiments in the presence of prior knowledge about the model parameters. Develops a unified approach to estimation and design; provides a Bayesian alternative to the least squares estimator; and indicates methods for the construction of optimal designs for the Bayes estimator. Material is also applicable to some well-known estimators using prior knowledge that is not available in the form of a prior distribution for the model parameters; such as mixed linear, minimax linear and ridge-type estimators.
Book Synopsis Empirical Bayes Methods in Mixed Linear Models by : Marta Lydia Zanelli
Download or read book Empirical Bayes Methods in Mixed Linear Models written by Marta Lydia Zanelli and published by . This book was released on 1985 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Dissertation Abstracts International by :
Download or read book Dissertation Abstracts International written by and published by . This book was released on 1998 with total page 652 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Bayes Equivariant Estimators of the Variance of a Finite Population for Exponential Priors by : Shelemyahu Zacks
Download or read book Bayes Equivariant Estimators of the Variance of a Finite Population for Exponential Priors written by Shelemyahu Zacks and published by . This book was released on 1978 with total page 12 pages. Available in PDF, EPUB and Kindle. Book excerpt: The present paper presents the derivation of the Bayes equivariant estimator of the variance of a finite population for prior i.i.d. exponential random variables and quadratic loss. The forms of the Bayes equivariant estimator strongly depends on the prior distribution assumed. In a previous paper the form of the Bayes equivariant estimator was derived for normal i.i.d. random variables. This model yields a relatively simple result. The present paper is therefore devoted to the study of the form of the estimator when the prior distribution is considerably different from a normal distribution. Numerical illustrations are provided too. (Author).
Book Synopsis Bayesian Statistics by : Peter M. Lee
Download or read book Bayesian Statistics written by Peter M. Lee and published by Wiley. This book was released on 2009-01-20 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Statistics is the school of thought that uses all information surrounding the likelihood of an event rather than just that collected experimentally. Among statisticians the Bayesian approach continues to gain adherents and this new edition of Peter Lee’s well-established introduction maintains the clarity of exposition and use of examples for which this text is known and praised. In addition, there is extended coverage of the Metropolis-Hastings algorithm as well as an introduction to the use of BUGS (Bayesian Inference Using Gibbs Sampling) as this is now the standard computational tool for such numerical work. Other alterations include new material on generalized linear modelling and Bernardo’s theory of reference points.
Book Synopsis Empirical Bayes Methods with Applications by : J.S. Maritz
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.
Book Synopsis Some Bayes Estimators of Reliability for the Inverse Gaussian Lifetime Model by : W. J. Padgett
Download or read book Some Bayes Estimators of Reliability for the Inverse Gaussian Lifetime Model written by W. J. Padgett and published by . This book was released on 1980 with total page 12 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayes estimation of the reliability function for the inverse Gaussian distribution is discussed. For the case that the mean lifetime is known, Bayes estimators are obtained with Jeffreys' noninformative prior and with the natural conjugate prior for the scale parameter. In the case that both parameters are unknown, an estimator of reliability is suggested which is based on the Bayes estimator obtained for the case that the mean lifetime is known. This estimator is not Bayes but compares favorably with the maximum likelihood and minimum variance unbiased estimators as indicated by computer simulations. (Author).
Book Synopsis Bayesian Estimation in the Linear Model by : Valen Earl Johnson
Download or read book Bayesian Estimation in the Linear Model written by Valen Earl Johnson and published by . This book was released on 1990 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Empirical Bayes and Likelihood Inference by : S.E. Ahmed
Download or read book Empirical Bayes and Likelihood Inference written by S.E. Ahmed and published by Springer Science & Business Media. This book was released on 2001 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Bayesian Data Analysis, Third Edition by : Andrew Gelman
Download or read book Bayesian Data Analysis, Third Edition written by Andrew Gelman and published by CRC Press. This book was released on 2013-11-01 with total page 677 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.
Book Synopsis Variance Component Estimators for Binary Data Derived from the Dispersion-mean Model by : Deborah Lynn Reichert
Download or read book Variance Component Estimators for Binary Data Derived from the Dispersion-mean Model written by Deborah Lynn Reichert and published by . This book was released on 1993 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Bayesian Filtering and Smoothing by : Simo Särkkä
Download or read book Bayesian Filtering and Smoothing written by Simo Särkkä and published by Cambridge University Press. This book was released on 2013-09-05 with total page 255 pages. Available in PDF, EPUB and Kindle. Book excerpt: A unified Bayesian treatment of the state-of-the-art filtering, smoothing, and parameter estimation algorithms for non-linear state space models.
Book Synopsis Formal Bayes Estimation with Application to a Random Effects Analysis of Variance Model by : Stanford University. Department of Statistics
Download or read book Formal Bayes Estimation with Application to a Random Effects Analysis of Variance Model written by Stanford University. Department of Statistics and published by . This book was released on 1969 with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt: Techniques of formal Bayes estimation are discussed and applied to the estimation of the components of variance in the one way layout random effects ANOVA model. Invariant formal Bayes estimators are presented and proved to be admissible in certain classes of invariant rules and also minimax. To do this a general sufficient condition for admissible in certain multiparameter problems is given and applied to the ANOVA problem. Numerical calculations of mean squared errors of various estimators are also presented, and these results help justify the recommendation of the formal Bayes estimators. (Author).
Book Synopsis Empirical Bayes, James Stein and Ridge Regression Type Estimators for Linear Models by : Marvin Haskel Jack Gruber
Download or read book Empirical Bayes, James Stein and Ridge Regression Type Estimators for Linear Models written by Marvin Haskel Jack Gruber and published by . This book was released on 1979 with total page 720 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Frequentist and Bayesian Aspects of Some Nonparametric Estimation Problems by : Hong Zhao
Download or read book Frequentist and Bayesian Aspects of Some Nonparametric Estimation Problems written by Hong Zhao and published by . This book was released on 1993 with total page 88 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Statistics for High-Dimensional Data by : Peter Bühlmann
Download or read book Statistics for High-Dimensional Data written by Peter Bühlmann and published by Springer Science & Business Media. This book was released on 2011-06-08 with total page 568 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern statistics deals with large and complex data sets, and consequently with models containing a large number of parameters. This book presents a detailed account of recently developed approaches, including the Lasso and versions of it for various models, boosting methods, undirected graphical modeling, and procedures controlling false positive selections. A special characteristic of the book is that it contains comprehensive mathematical theory on high-dimensional statistics combined with methodology, algorithms and illustrations with real data examples. This in-depth approach highlights the methods’ great potential and practical applicability in a variety of settings. As such, it is a valuable resource for researchers, graduate students and experts in statistics, applied mathematics and computer science.
