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Estimating a Proportion Using Stratified Data From Both Convenience and Random Samples

Summary: [This abstract is based on the authors’ abstract.] A Bayesian methodology is proposed for making inferences about a proportion of an attribute present in a population that accurately accounts for the potential bias of the convenience samples, the stratification by lots, and the fact that not all of the lots have been sampled. While the methodology is illustrated with a simulated population, the solution is motivated by a similar proprietary production situation.

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  • Topics: Sampling, Statistics
  • Keywords: Bayesian methods, Attributes sampling, Discrete data, Extended-hypergeometric and hypergeometric distributions, Finite population, Markov chain, Monte Carlo methods.
  • Author: Graves, Todd; Hamada, Michael; Booker, Jane; Decroix, Michele; Chilcoat, Kathy; Bowyer, Clint
  • Journal: Technometrics