Simultaneous non-parametric regressions of unbalanced longitudinal data
نویسنده
چکیده
The aim of this paper is to simultaneously estimate n curves corrupted by noise, this means several observations of a random process. The non parametric estimation of the sampled paths leads to a new kind of functional principal components analysis which simultaneously takes into account a dimensionality and a smoothness constraint. Furthermore, the use of B-spline approximation to estimate the curves allows the study of unbalanced longitudinal data. The relationship between the choice of the smoothing parameter and that of dimensionality is discussed. A simulation study shows good behaviors of this proposed estimate compared to n independent smoothing splines under generalized cross validation. Finally, the methodology of this paper is illustrated by its application to a real world data set.
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تاریخ انتشار 1997