  In this paper, we study a smoothness regularization method for a varying
coefficient model based on sparse and irregularly sampled functional data which
is contaminated with some measurement errors. We estimate the one-dimensional
covariance and cross-covariance functions of the underlying stochastic
processes based on a reproducing kernel Hilbert space approach. We then obtain
least squares estimates of the coefficient functions. Simulation studies
demonstrate that the proposed method has good performance. We illustrate our
method by an analysis of longitudinal primary biliary liver cirrhosis data.
