A semiparametric mixture regression model for longitudinal data

Research output: Contribution to journalArticle

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

Abstract

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
JournalJournal of Statistical Theory and Practice
Early online date15 Mar 2017
DOIs
StatePublished - 2017