LOCALLY SPARSE ESTIMATOR OFGENERALIZED VARYING COEFFICIENT MODELFOR ASYNCHRONOUS LONGITUDINAL DATA

In longitudinal studies, it is common that the response and the covariateare not measured at the same time, which complicates the subsequent analysis.In this study, we consider the estimation of a generalized varying coefficientmodel with such asynchronous observations. We construct a penalized kernel-weighted estimating equation using the kernel technique in a functional data analysisframework. Moreover, we consider local sparsity in the estimating equation toimprove the interpretability of the estimate. We extend the iteratively reweightedleast squares algorithm in our computation, and establish the theoretical propertiesof the proposed method, including the consistency, sparsistency, and asymptoticdistribution. Lastly, we use simulation studies to verify the performance of ourmethod, and demonstrate the method by applying it to data from a study onwomen's health