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In this article we develop a nonparametric estimation procedure for the varying coefficient model when the within-subject covariance is unknown. Extending the idea of iterative reweighted least squares to the functional setting, we iterate between estimating the coefficients conditional on the covariance and estimating the functional covariance conditional on the coefficients. Smoothing splines for correlated errors are used to estimate the functional coefficients with smoothing parameters selected via the generalized maximum likelihood. The covariance is nonparametrically estimated using a penalized estimator with smoothing parameters chosen via a Kullback–Leibler criterion. Empirical properties of the proposed method are demonstrated in simulations and the method is applied to the data collected from an ovarian tumor study in mice to analyze the effects of different chemotherapy treatments on the volumes of two classes of tumors. [ABSTRACT FROM AUTHOR]

The article discusses the need to refine and improve a proposed method for the study of traditional Chinese herbal medications (TCHMs) and extend it to other statistical and applied problems. The role of the mean chromatographic fingerprint technique in the simplification of the covariance structure is mentioned. The importance of combining the preprocessing and estimation procedures in biomedical applications is also noted.