Modeling and inference for mixtures of simple symmetric exponential families of p-dimensional distributions for vectors with binary coordinates

dc.contributor.author Chakraborty, Abhishek
dc.contributor.author Vardeman, Stephen
dc.contributor.author Vardeman, Stephen
dc.contributor.department Statistics
dc.contributor.department Industrial and Manufacturing Systems Engineering
dc.date 2021-06-11T13:25:02.000
dc.date.accessioned 2021-08-15T01:49:09Z
dc.date.available 2021-08-15T01:49:09Z
dc.date.copyright Fri Jan 01 00:00:00 UTC 2021
dc.date.issued 2021-01-01
dc.description.abstract <p>We propose tractable symmetric exponential families of distributions for multivariate vectors of 0's and 1's in dimensions, or what are referred to in this paper as binary vectors, that allow for nontrivial amounts of variation around some central value . We note that more or less standard asymptotics provides likelihood-based inference in the one-sample problem. We then consider mixture models where component distributions are of this form. Bayes analysis based on Dirichlet processes and Jeffreys priors for the exponential family parameters prove tractable and informative in problems where relevant distributions for a vector of binary variables are clearly not symmetric. We also extend our proposed Bayesian mixture model analysis to datasets with missing entries. Performance is illustrated through simulation studies and application to real datasets.</p>
dc.description.comments <p>This article is published as Chakraborty, Abhishek, and Stephen B. Vardeman. "Modeling and inference for mixtures of simple symmetric exponential families of‐dimensional distributions for vectors with binary coordinates." <em>Statistical Analysis and Data Mining</em> (2021). doi:<a href="https://doi.org/10.1002/sam.11528">10.1002/sam.11528</a>.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/stat_las_pubs/340/
dc.identifier.articleid 1343
dc.identifier.contextkey 23307692
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath stat_las_pubs/340
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/jw2742xv
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/stat_las_pubs/340/2021_Vardeman_ModelingInference.pdf|||Fri Jan 14 23:41:33 UTC 2022
dc.source.uri 10.1002/sam.11528
dc.subject.disciplines Multivariate Analysis
dc.subject.disciplines Statistical Methodology
dc.subject.disciplines Statistical Models
dc.subject.keywords Bayesian analysis
dc.subject.keywords mixture models
dc.subject.keywords MCMC
dc.subject.keywords pixel flips
dc.subject.keywords missing entries
dc.title Modeling and inference for mixtures of simple symmetric exponential families of p-dimensional distributions for vectors with binary coordinates
dc.type article
dc.type.genre article
dspace.entity.type Publication
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relation.isOrgUnitOfPublication 51d8b1a0-5b93-4ee8-990a-a0e04d3501b1
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