Bayesian Methods for Finite Population Sampling
1st Edition
Published on 24. December 2021
296 pages
978-1-351-46442-0 (ISBN)
Description
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.
More details
Series
Edition
1. Auflage
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
ISBN-13
978-1-351-46442-0 (9781351464420)
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Schweitzer Classification
Other editions
Additional editions
Malay Ghosh | G. Meeden
Bayesian Methods for Finite Population Sampling
Book
06/1997
Chapman and Hall
€85.59
Shipment within 10-20 days
Persons
Malay Ghosh (Alcon Laboratories, Forth Worth, Texas, USA) (Author) , Glen Meeden (University of Minessota, Minneapolis, MN) (Author)
Content
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
