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  • 标题:Bayesian Mixture Models with Focused Clustering for Mixed Ordinal and Nominal Data
  • 本地全文:下载
  • 作者:Maria DeYoreo ; Jerome P. Reiter ; D. Sunshine Hillygus
  • 期刊名称:Bayesian Analysis
  • 印刷版ISSN:1931-6690
  • 电子版ISSN:1936-0975
  • 出版年度:2017
  • 卷号:12
  • 期号:3
  • 页码:679-703
  • DOI:10.1214/16-BA1020
  • 语种:English
  • 出版社:International Society for Bayesian Analysis
  • 摘要:In some contexts, mixture models can fit certain variables well at the expense of others in ways beyond the analyst’s control. For example, when the data include some variables with non-trivial amounts of missing values, the mixture model may fit the marginal distributions of the nearly and fully complete variables at the expense of the variables with high fractions of missing data. Motivated by this setting, we present a mixture model for mixed ordinal and nominal data that splits variables into two groups, focus variables and remainder variables. The model allows the analyst to specify a rich sub-model for the focus variables and a simpler sub-model for remainder variables, yet still capture associations among the variables. Using simulations, we illustrate advantages and limitations of focused clustering compared to mixture models that do not distinguish variables. We apply the model to handle missing values in an analysis of the 2012 American National Election Study, estimating relationships among voting behavior, ideology, and political party affiliation.
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