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http://dx.doi.org/10.5351/KJAS.2016.29.7.1347

The EM algorithm for mixture regression with missing covariates  

Kim, Hyungmin (Department of Statistics, Sungkyunkwan University)
Ham, Geonhee (Center for Public Opinion and Quantitative Research, The Asan Institute for Policy Studies)
Seo, Byungtae (Department of Statistics, Sungkyunkwan University)
Publication Information
The Korean Journal of Applied Statistics / v.29, no.7, 2016 , pp. 1347-1359 More about this Journal
Abstract
Finite mixtures of regression models provide an effective tool to explore a hidden functional relationship between a response variable and covariates. However, it is common in practice that data are not fully observed due to several reasons. In this paper, we derived an expectation-maximization (EM) algorithm to obtain the maximum likelihood estimator when some covariates are missing at random in the finite mixture of regression models. We conduct some simulation studies and we also provide some real data examples to show the validity of the derived EM algorithm.
Keywords
mixture models; missing covariates; mixture regression; EM algorithm;
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