Reducing component estimation for varying coefficient models with longitudinal data

Abstract

Varying-coefficient models with longitudinal observations are very useful in epidemiology and some other practical fields. In this paper, a reducing component procedure is proposed for estimating the unknown functions and their derivatives in very general models, in which the unknown coefficient functions admit different or the same degrees of smoothness and the covariates can be time-dependent. The asymptotic properties of the estimators, such as consistency, rate of convergence and asymptotic distribution, are derived. The asymptotic results show that the asymptotic variance of the reducing component estimators is smaller than that of the existing estimators when the coefficient functions admit different degrees of smoothness. Finite sample properties of our procedures are studied through Monte Carlo simulations.