Zimmerman, Patrick Lennon Kendall2013-11-082013-11-082013-09https://hdl.handle.net/11299/160015University of Minnesota Ph.D. dissertation. September 2013. Major: Statistics. Advisor: Glen Meeden. 1 computer file (PDF); vii, 100 pages.In survey sampling, stratied 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.en-USBayesianMixture modelStratificationSurvey samplingThesis or Dissertation