Abstract: We consider nonparametric estimation of coefficient functions in a varying coefficient model of the form based on longitudinal observations , , , where and are the time of the th measurement and the number of repeated measurements for the th subject, and and , for are the th subject's observed outcome and covariates at . We approximate each coefficient function by a polynomial spline and employ the least squares method to do the estimation. An asymptotic theory for the resulting estimates is established, including consistency, rate of convergence and asymptotic distribution. The asymptotic distribution results are used as a guideline to construct approximate confidence intervals and confidence bands for components of . We also propose a polynomial spline estimate of the covariance structure of , which is used to estimate the variance of the spline estimate . A data example in epidemiology and a simulation study are used to demonstrate our methods.
Key words and phrases: Asymptotic normality, confidence intervals, nonparametric regression, repeated measurements, varying coefficient models.