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…mehr
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.
Produktdetails
Produktdetails
Chapman & Hall/CRC Monographs on Statistics and Applied Probability 79
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
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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