Unsupervised learning of mixture regression models for longitudinal data

Peirong Xu, Heng PENG, Tao Huang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

This paper is concerned with learning of mixture regression models for individuals that are measured repeatedly. The adjective “unsupervised” implies that the number of mixing components is unknown and has to be determined, ideally by data driven tools. For this purpose, a novel penalized method is proposed to simultaneously select the number of mixing components and to estimate the mixture proportions and unknown parameters in the models. The proposed method is capable of handling both continuous and discrete responses by only requiring the first two moment conditions of the model distribution. It is shown to be consistent in both selecting the number of components and estimating the mixture proportions and unknown regression parameters. Further, a modified EM algorithm is developed to seamlessly integrate model selection and estimation. Simulation studies are conducted to evaluate the finite sample performance of the proposed procedure. And it is further illustrated via an analysis of a primary biliary cirrhosis data set.

Original languageEnglish
Pages (from-to)44-56
Number of pages13
JournalComputational Statistics and Data Analysis
Volume125
DOIs
Publication statusPublished - Sep 2018

Scopus Subject Areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

User-Defined Keywords

  • EM algorithm
  • Longitudinal data analysis
  • Model selection
  • Quasi-likelihood
  • Unsupervised learning

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