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Bayesian Methods for Finite Population Sampling
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Artikel-Nr:
9780412987717
Seiten:
300
Autor:
Malay Ghosh
Gewicht:
562 g
Format:
234x156x20 mm
Serie:
79, Chapman & Hall/CRC Monographs on Statistics and Applied Probability
Sprache:
Englisch
Beschreibung:
Ghosh, Malay; Meeden, Glen
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.
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.
Springer Book Archives
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
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