A0658
Title: Estimation of principal functional coefficient models for longitudinal data
Authors: Degui Li - University of York (United Kingdom) [presenting]
Abstract: The estimation of the functional coefficient longitudinal data models is studied. In order to achieve dimension reduction for the nonparametric functional coefficients and improve the estimation efficiency, we introduce a novel semiparametric estimation procedure which combines a principal component analysis of the functional coefficients and a Cholesky decomposition of the within-subject covariance matrices. Under some regularity conditions, we derive the asymptotic distribution theory for the proposed semiparametric estimators and show that the efficiency of the estimation of the (principal) functional coefficients can be improved when the within-subject covariance structure is correctly specified. Furthermore, we apply two approaches to consistently estimate the autoregressive coefficients in the Cholesky decomposition, which help avoid a possible misspecification of the within-subject covariance structure and ensure the efficiency improvement for the estimation of the (principal) functional coefficients. Some numerical studies including Monte Carlo experiments and an empirical application show that the developed semiparametric method works reasonably well in finite samples.
