Bayesian Methods for Finite Population Sampling

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.

Auteur

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

Contenu

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