Full Citation

Title: Survey Sampling and Multiple Stratifications

Citation Type: Dissertation/Thesis

Publication Year: 2013

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Abstract: In survey sampling, stratified random sampling and post-stratification can increase the precision of estimation. In some cases, however, there may be multiple ways to stratify a population. We present a method, based on a non-informative Bayesian approach, that uses a finite mixture model to incorporate information from each stratification into estimation. This approach works well when the response variable is categorical or discrete, and for some non-response types of problems. We provide the theoretical basis for our method, present some simulation results, discuss various extensions, and define some software that implements the method.

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Authors: Zimmerman, Patrick L.

Institution: University of Minnesota

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Advisor: Gleb Meeden

Degree: Doctor of Philosophy

Publisher Location: Minneapolis, MN

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Data Collections: IPUMS USA

Topics: Methodology and Data Collection

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