Bayesian Mixture Models with Focused Clustering for Mixed Ordinal and Nominal Data

被引:11
作者
DeYoreo, Maria [1 ,3 ]
Reiter, Jerome P. [1 ,4 ]
Hillygus, D. Sunshine [2 ,5 ]
机构
[1] Duke Univ, Dept Stat Sci, Durham, NC 27708 USA
[2] Duke Univ, Dept Polit Sci, Durham, NC USA
[3] Duke Univ, Durham, NC USA
[4] Duke Univ, Stat Sci, Durham, NC USA
[5] Duke Univ, Polit Sci, Durham, NC USA
来源
BAYESIAN ANALYSIS | 2017年 / 12卷 / 03期
基金
美国国家科学基金会;
关键词
categorical; missing; mixture model; multiple imputation; MULTIPLE IMPUTATION; CATEGORICAL-DATA; BINARY;
D O I
10.1214/16-BA1020
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
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.
引用
收藏
页码:679 / 703
页数:25
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