Longitudinal Data Regression Analysis Using Semiparametric Modelling

Abstract

Zhang, Leng and Tang [1] propose joint parametric modelling of the means, variances, and the correlations by decomposing the correlation matrix via hyperspherical co-ordinates and show that this results unconstrained parameterization, fast computation, easy interpretation of the parameters, and model parsimony. With unconstrained structures, they also suggest future research on modelling the mean, the variance, and the correlations non-parametrically and semiparametrically. In this paper we explore semiparametric modelling via simulations and data analysis. Extensive simulations show that the semiparametric modelling produces similar bias and efficiency properties of the parameter estimates as those by the parametric modelling. However, model selection, using the AIC and the BIC, through the analysis of two real biomedical data sets show significant improvement in model parsimony.