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E-raamat: Bayesian Methods for Finite Population Sampling

(Alcon Laboratories, Forth Worth, Texas, USA), (University of Minessota, Minneapolis, MN)
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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.

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Preface ix
1 Bayesian foundations
1(20)
1.1 Notation
1(2)
1.2 Sufficiency
3(4)
1.3 The sufficiency and likelihood principles
7(3)
1.4 A Bayesian example
10(4)
1.5 Posterior linearity
14(2)
1.6 Overview
16(5)
2 A noninformative Bayesian approach
21(40)
2.1 A binomial example
22(5)
2.2 A characterization of admissibility
27(4)
2.3 Admissibility of the sample mean
31(6)
2.4 Set estimation
37(3)
2.5 The Polya urn
40(2)
2.6 The Polya posterior
42(5)
2.7 Simulating the Polya posterior
47(3)
2.8 Some examples
50(11)
3 Extensions of the Polya posterior
61(100)
3.1 Prior information
62(9)
3.2 Using an auxiliary variable
71(22)
3.3 Stratification and prior information
93(16)
3.4 Choosing between experiments
109(6)
3.5 Nonresponse
115(19)
3.6 Some nonparametric problems
134(15)
3.7 Linear interpolation
149(12)
4 Empirical Bayes estimation
161(60)
4.1 Introduction
161(2)
4.2 Stepwise Bayes estimators
163(1)
4.3 Estimation of stratum means
164(8)
4.4 Robust estimation of stratum means
172(19)
4.5 Multistage sampling
191(12)
4.6 Auxiliary information
203(7)
4.7 Nested error regression models
210(11)
5 Hierarchical Bayes estimation
221(54)
5.1 Introduction
221(1)
5.2 Stepwise Bayes estimators
222(4)
5.3 Estimation of stratum means
226(10)
5.4 Auxiliary information I
236(17)
5.5 Auxiliary information II
253(16)
5.6 Generalized linear models
269(6)
References 275(8)
Author index 283(4)
Subject index 287
Ghosh\, Malay; Meeden\, Glen