Local polynomial mixed-effects models for longitudinal data.

From: Journal of the American Statistical Association | Date: September 1, 2002| Author: Wu, Hulin; Zhang, Jin-Ting | Copyright information

We consider a nonparametric mixed-effects model [y.sub.i]([t.sub.ij]) = [eta]([t.sub.ij]) + [v.sub.i]([t.sub.ij])+[[epsilon].sub.i]([t.sub.ij]), j = 1,2,..., [n.sub.i]; i = 1,2,..., n for longitudinal data. We propose combining local polynomial kernel regression and linear mixed-effects (LME) model techniques to estimate both fixed-effects (population) curve [eta](t) and random-effects curves [v.sub.i](t). The resulting estimator, called the local polynomial LME (LLME) estimator, takes the ...

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