Bayesian Methods for Finite Population Sampling

by Malay Ghosh

Overview

Assuming a basic knowledge of the frequentist approach to finite population sampling, Bayesian Methods for Finite Population Sampling describes Bayesian and predictive approaches to inferential problems with an emphasis on the likelihood principle. The authors demonstrate that a variety of levels of prior information can be used in survey sampling in a Bayesian manner. Situations considered range from a noninformative Bayesian justification of standard frequentist methods when the only prior information available is the belief in the exchangeability of the units to a full-fledged Bayesian model. Intended primarily for graduate students and researchers in finite population sampling, this book will also be of interest to statisticians who use sampling and lecturers and researchers in general statistics and biostatistics.

Product Details

ISBN-13:	9781351464413
Publisher:	CRC Press
Publication date:	12/24/2021
Series:	ISSN
Sold by:	Barnes & Noble
Format:	eBook
Pages:	296
File size:	15 MB
Note:	This product may take a few minutes to download.

About the Author

Malay Ghosh (Alcon Laboratories, Forth Worth, Texas, USA) (Author) , Glen Meeden (University of Minessota, Minneapolis, MN) (Author)

Table of Contents

Bayesian Foundations, Notation, Sufficiency, The Sufficiency and Likelihood Principles, A Bayesian Example, Posterior Linearity, Overview, A Noninfromative Bayesian Approach, A Binomial Example, A Characterization of Admissibility, Admissibility of the Sample Mean, Set Estimation, The Polya Urn, The Polya Posterior, Simulating the Polya Posterior , Some Examples, Extensions of the Polya Posterior, Prior Information, Using an Auxiliary Variable, Stratification and Prior Information, Choosing between Experiments, Nonresponse, Some Nonparametric Problems, Linear Interpolation, Empirical Bayes Estimation, Introduction Stepwise Bayes Estimators, Estimation of Stratum Means, Robust Estimation of Stratum Means, Multistage Sampling, Auxiliary Information, Nested Error Regression Models, Hierarchical Bayes Estimation, Stepwise Bayes Estimators, Estimation of Stratum Means, Auxiliary Information I, Auxiliary Information II