An accelerated EM algorithm for mixture models with uncertainty for rating data

被引:0
作者
Rosaria Simone
机构
[1] University of Naples Federico II,Department of Political Sciences
来源
Computational Statistics | 2021年 / 36卷
关键词
Louis’ Identity; Accelerated EM algorithm; Mixture models; Rating data; Standard errors;
D O I
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学科分类号
摘要
The paper is framed within the literature around Louis’ identity for the observed information matrix in incomplete data problems, with a focus on the implied acceleration of maximum likelihood estimation for mixture models. The goal is twofold: to obtain direct expressions for standard errors of parameters from the EM algorithm and to reduce the computational burden of the estimation procedure for a class of mixture models with uncertainty for rating variables. This achievement fosters the feasibility of best-subset variable selection, which is an advisable strategy to identify response patterns from regression models for all Mixtures of Experts systems. The discussion is supported by simulation experiments and a real case study.
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页码:691 / 714
页数:23
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