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. 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.

Bayesian FoundationsNotation SufficiencyThe Sufficiency and Likelihood PrinciplesA Bayesian ExamplePosterior LinearityOverviewA Noninfromative Bayesian ApproachA Binomial ExampleA Characterization of AdmissibilityAdmissibility of the Sample MeanSet EstimationThe Polya UrnThe Polya PosteriorSimulating the Polya Posterior Some ExamplesExtensions of the Polya PosteriorPrior InformationUsing an Auxiliary VariableStratification and Prior InformationChoosing between ExperimentsNonresponseSome Nonparametric ProblemsLinear InterpolationEmpirical Bayes EstimationIntroduction Stepwise Bayes EstimatorsEstimation of Stratum MeansRobust Estimation of Stratum MeansMultistage SamplingAuxiliary InformationNested Error Regression ModelsHierarchical Bayes EstimationIntroductionStepwise Bayes EstimatorsEstimation of Stratum MeansAuxiliary Information IAuxiliary Information II

Ghosh\, Malay; Meeden\, Glen