A semiparametric mixture regression model for longitudinal data

Research output: Contribution to journalArticle

  • Authors:
  • Tapio Nummi
  • Janne Salonen
  • Lasse Koskinen
  • Jianxin Pan


A normal semiparametric mixture regression model is proposed for longitudinal data. The proposed model contains one smooth term and a set of possible linear predictors. Model terms are estimated using the penalized likelihood method with the EM-algorithm. A computationally feasible alternative method that provides an approximate solution is also introduced. Simulation experiments and real data example are used to illustrate the methods.

Bibliographical metadata

Original languageEnglish
Pages (from-to)12-22
JournalJournal of Statistical Theory and Practice
Issue number1
Early online date15 Mar 2017
Publication statusPublished - 2018

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