Bayesian Methods for Finite Population Sampling / Edition 1

Bayesian Methods for Finite Population Sampling / Edition 1

by Malay Ghosh, Glen Meeden
     
 

ISBN-10: 0412987716

ISBN-13: 9780412987717

Pub. Date: 06/01/1997

Publisher: Taylor & Francis

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

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:
9780412987717
Publisher:
Taylor & Francis
Publication date:
06/01/1997
Series:
Chapman & Hall/CRC Monographs on Statistics & Applied Probability Series, #79
Pages:
296
Product dimensions:
6.40(w) x 9.60(h) x 0.90(d)

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
Introduction
Stepwise Bayes Estimators
Estimation of Stratum Means
Auxiliary Information I
Auxiliary Information II

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