The EM algorithm for mixture regression with missing covariates

被引：0

作者：

Kim, Hyungmin ^{[1
]}

Ham, Geonhee ^{[2
]}

Seo, Byungtae ^{[1
]}

机构：

[1] Sungkyunkwan Univ, Dept Stat, 25-2 Sungkyunkwan Ro, Seoul 03063, South Korea

[2] Asan Inst Policy Studies, Ctr Publ Opin & Quantitat Res, Seoul, South Korea

来源：

KOREAN JOURNAL OF APPLIED STATISTICS | 2016年 / 29卷 / 07期

基金：

新加坡国家研究基金会;

关键词：

mixture models; missing covariates; mixture regression; EM algorithm;

D O I：

10.5351/KJAS.2016.29.7.1347

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

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.

引用

页码：1347 / 1359

页数：13

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