Please use this identifier to cite or link to this item: http://hdl.handle.net/11718/21851
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dc.contributor.authorLaha, Arnab Kumar-
dc.contributor.authorDutta, Somak-
dc.contributor.authorRoy, Vivekananda-
dc.date.accessioned2019-05-15T01:09:53Z-
dc.date.available2019-05-15T01:09:53Z-
dc.date.issued2017-
dc.identifier.citationLaha, A.K., Dutta, S., & Roy, V. (2017). A novel sandwich algorithm for empirical Bayes analysis of rank data. Statistics and its interface, 10(4), 543 – 556. doi: http://dx.doi.org/10.4310/SII.2017.v10.n4.a2en_US
dc.identifier.urihttp://hdl.handle.net/11718/21851-
dc.description.abstractRank data arises frequently in marketing, nance, organizational behavior, and psychology. Most analysis of rank data reported in the literature assumes the presence of one or more variables (sometimes latent) based on whose values the items are ranked. In this paper we analyze rank data using a purely probabilistic model where the observed ranks are assumed to be perturbed versions of the true rank and each perturbation has a speci c probability of occurring. We consider the general case when covariate information is present and has an impact on the rankings. An empirical Bayes approach is taken for estimating the model parameters. The Gibbs sampler is shown to converge very slowly to the target posterior distribution and we show that some of the widely used empirical convergence diagnostic tools may fail to detect this lack of convergence. We propose a novel, fast mixing sandwich algorithm for exploring the posterior distribution. An EM algorithm based on Markov chain Monte Carlo (MCMC) sampling is developed for estimating prior hyper parameters. A real life rank data set is analyzed using the methods developed in the paper. The results obtained indicate the usefulness of these methods in analyzing rank data with covariate information.en_US
dc.publisherInternational Pressen_US
dc.subjectAlgebraic-Probabilistic modelen_US
dc.subjectConvergenceen_US
dc.subjectGibbs sampleren_US
dc.titleA novel sandwich algorithm for empirical Bayes analysis of rank dataen_US
dc.title.alternativeStatistics and its interfaceen_US
dc.typeArticleen_US
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