A semiparametric mixture regression model for longitudinal data
- 4 Downloads
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.
KeywordsCurve clustering EM algorithm finite mixtures growth curves
AMS Subject Classification62G05 62B99 62J07
Unable to display preview. Download preview PDF.
- Fitzmaurize, G. M., N. M. Laird, and J. H. Ware. 2011. Applied longitudinal analysis, 2nd ed. Hoboken, NJ: Wiley.Google Scholar
- Muthen, L., and B. Muthen. 2007. Mplus user’s guide, 6th ed. Los Angeles, CA: Muthen & Muthen.Google Scholar
- Poortema, K. 1984. On the statistical analysis of growth. PhD thesis, Groningen University, Groningen, The Netherlands.Google Scholar
- Ruppert, D., M. P. Wand, and R. J. Carrol. 2005. Semiparametric regression. New York, NY: Cambridge University Press.Google Scholar
- Titterington, D. M., A. F. M. Smith, and U. E. Makov. 1985. Statistical analysis of finite mixture distribution. Wiley, New York.Google Scholar
- Vuorela, N. 2011. Body mass index, overweight and obesity among children in Finland — A retrospective epidemilogical study in Pirkanmaa District spanning over four decades. Acta Universitatis Tamperensis 1611, Tampere University Press, Tampere.Google Scholar