摘要：We study functional varying coe.cient mo del in which both the response and the predictor are functions of a common variable such as time. We demonstrate the esti- mation of the slope function for the case of sparse and noise-contaminated longitudinal data. So far, a few metho ds have been intro duced based on varying coe.cient model. To estimate the slope function, we consider a regularization metho d using a repro- ducing kernel Hilbert space framework. Despite the generality of the regularization method, the procedure is easy to implement. Our numerical results show that the introduced pro cedure p erforms well in some senses.
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