A Bayesian Justification for Random Sampling in Sample Survey
Author(s) -
Glen Meeden
Publication year - 2012
Publication title -
pakistan journal of statistics and operation research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.354
H-Index - 15
eISSN - 2220-5810
pISSN - 1816-2711
DOI - 10.18187/pjsor.v8i3.513
Subject(s) - simple random sample , sampling design , sampling (signal processing) , mathematics , bayesian probability , stratified sampling , statistics , survey sampling , simple (philosophy) , bayesian statistics , econometrics , poisson sampling , probability sampling , systematic sampling , sampling distribution , slice sampling , bayesian inference , computer science , markov chain monte carlo , population , philosophy , demography , filter (signal processing) , epistemology , sociology , computer vision
In the usual Bayesian approach to survey sampling the sampling design, plays a minimal role, at best. Although a close relationship between exchangeable prior distributions and simple random sampling has been noted; how to formally integrate simple random sampling into the Bayesian paradigm is not clear. Recently it has been argued that the sampling design can be thought of as part of a Bayesian's prior distribution. We will show here that under this scenario simple random sample can be given a Bayesian justification in survey sampling.
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