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December, 1993 A Bayesian Method for Weighted Sampling
Albert Y. Lo
Ann. Statist. 21(4): 2138-2148 (December, 1993). DOI: 10.1214/aos/1176349414

Abstract

Bayesian statistical inference for sampling from weighted distribution models is studied. Small-sample Bayesian bootstrap clone (BBC) approximations to the posterior distribution are discussed. A second-order property for the BBC in unweighted i.i.d. sampling is given. A consequence is that BBC approximations to a posterior distribution of the mean and to the sampling distribution of the sample average, can be made asymptotically accurate by a proper choice of the random variables that generate the clones. It also follows from this result that in weighted sampling models, BBC approximations to a posterior distribution of the reciprocal of the weighted mean are asymptotically accurate; BBC approximations to a sampling distribution of the reciprocal of the empirical weighted mean are also asymptotically accurate.

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Albert Y. Lo. "A Bayesian Method for Weighted Sampling." Ann. Statist. 21 (4) 2138 - 2148, December, 1993. https://doi.org/10.1214/aos/1176349414

Information

Published: December, 1993
First available in Project Euclid: 12 April 2007

zbMATH: 0793.62024
MathSciNet: MR1245785
Digital Object Identifier: 10.1214/aos/1176349414

Subjects:
Primary: 62G09
Secondary: 62G20, 62G99

Rights: Copyright © 1993 Institute of Mathematical Statistics

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Vol.21 • No. 4 • December, 1993
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