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dc.contributor.authorKahle, David
dc.contributor.authorYoshida, Ruriko
dc.contributor.authorGarcia-Puente, Luis
dc.date2017-09
dc.date.accessioned2018-08-10T23:35:04Z
dc.date.available2018-08-10T23:35:04Z
dc.date.issued2017-09
dc.identifier.citationKahle, David, Ruriko Yoshida, and Luis Garcia-Puente. "Hybrid schemes for exact conditional inference in discrete exponential families." Annals of the Institute of Statistical Mathematics (2017): 1-29.en_US
dc.identifier.urihttp://hdl.handle.net/10945/59431
dc.descriptionThe article of record as published may be found at http://dx.doi.org/10.1007/s10463-017-0615-zen_US
dc.description.abstractExact conditional goodness-of-fit tests for discret eexponential family models can be conducted via Monte Carlo estimation of p values by sampling from the conditional distribution of multiway contingency tables. The two most popular methods for such sampling are Markov chain Monte Carlo (MCMC) and sequential importance sampling (SIS). In this work we consider various ways to hybridize the two schemes and propose one standout strategy as a good general purpose method for conducting inference. The proposed method runs many parallel chains initialized at SIS samples across the fiber. When a Markov basis is unavailable, the proposed scheme uses a lattice basis with intermittent SIS proposals to guarantee irreducibility and asymptotic unbiasedness. The scheme alleviates many of the challenges faced by the MCMC and SIS schemes individually while largely retaining their strengths. It also provides diagnostics that guide and lend credibility to the procedure. Simulations demonstrate the viability of the approach.en_US
dc.format.extent29 p.en_US
dc.publisherSpringeren_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 schemes for exact conditional inference in discrete exponential familiesen_US
dc.typeArticleen_US
dc.contributor.corporateNaval Postgraduate School (U.S.)en_US
dc.contributor.departmentOperations Research (OR)en_US


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