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dc.contributor.advisorYoshida, Ruriko
dc.contributor.authorSaluke, Patrick M.
dc.date.accessioned2018-10-26T19:22:19Z
dc.date.available2018-10-26T19:22:19Z
dc.date.issued2018-09
dc.identifier.urihttp://hdl.handle.net/10945/60457
dc.description.abstractLogistic regression is one of the most popular means of modeling contingency table data due to its ease of use. Simple asymptotic inference (like a χ2 approximation) for evaluating goodness-of-fit tests, however, may not be valid for sparse datasets having cell counts less than 5. In these cases, we often attempt exact conditional inference via a sampler, such as Markov Chain Monte Carlo (MCMC) or Sequential Importance Sampling (SIS). This paper proposes a hybrid sampling scheme that combines MCMC and SIS to sample sparse, multidimensional contingency tables satisfying fixed marginals when MCMC alone does not guarantee an exhaustive sampling of the conditional state space. To investigate its suitability, the proposed hybrid scheme is applied to an observational dataset from Alzheimer's researcher JA Mortimer measuring the cognitive states of nuns over a 15-year period beginning in 1991. Through the application of our proposed scheme, we find the estimated p-values via a hybrid MCMC and SIS sampler are remarkably similar to the χ2 asymptotic approximation p-values, even for sparse contingency tables.en_US
dc.description.urihttp://archive.org/details/hybridsisandmark1094560457
dc.publisherMonterey, CA; Naval Postgraduate Schoolen_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.en_US
dc.titleHYBRID SIS AND MARKOV CHAIN MONTE CARLO SAMPLING METHODOLOGY FOR GOODNESS-OF-FIT TESTS ON CONTINGENCY TABLESen_US
dc.typeThesisen_US
dc.contributor.secondreaderCarlyle, W M.
dc.contributor.departmentOperations Research (OR)
dc.subject.authorMarkov Chain Monte Carloen_US
dc.subject.authorMCMCen_US
dc.subject.authorSequential Importance Samplingen_US
dc.subject.authorSISen_US
dc.subject.authorsparseen_US
dc.subject.authormultidimensional contingency tableen_US
dc.description.recognitionOutstanding Thesisen_US
dc.description.serviceLieutenant Commander, United States Navyen_US
etd.thesisdegree.nameMaster of Science in Operations Researchen_US
etd.thesisdegree.levelMastersen_US
etd.thesisdegree.disciplineOperations Researchen_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US
dc.identifier.thesisid29737
dc.description.distributionstatementApproved for public release; distribution is unlimited.


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