A semiparametric mixture regression model for longitudinal data

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 a real data example are used to illustrate the methods.

KEYWORDS:

• Curve clustering
• EM algorithm
• finite mixtures
• growth curves

AMS SUBJECT CLASSIFICATION:

• 62G05
• 62B99
• 62J07

Acknowledgments

The authors thank the referees, who gave valuable comments that led to improvements in the article.