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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/6820

Title: Smooth random effects distribution in a linear mixed model
Authors: Ghidey, Wendimagegn
LESAFFRE, Emmanuel
Eilers, Paul
Issue Date: 2004
Citation: Biometrics, 60(4). p. 945-953
Abstract: A linear mixed model with a smooth random effects density is proposed. A similar approach to P-spline smoothing of Eilers and Marx (1996, Statistical Science 11, 89-121) is applied to yield a more flexible estimate of the random effects density. Our approach differs from theirs in that the B-spline basis functions are replaced by approximating Gaussian densities. Fitting the model involves maximizing a penalized marginal likelihood. The best penalty parameters minimize Akaike's Information Criterion employing Gray's (1992, Journal of the American Statistical Association 87, 942-951) results. Although our method is applicable to any dimensions of the random effects structure, in this article the two-dimensional case is explored. Our methodology is conceptually simple, and it is relatively easy to fit in practice and is applied to the cholesterol data first analyzed by Zhang and Davidian (2001, Biometrics 57, 795-802). A simulation study shows that our approach yields almost unbiased estimates of the regression and the smoothing parameters in small sample settings. Consistency of the estimates is shown in a particular case.
URI: http://hdl.handle.net/1942/6820
DOI: 10.1111/j.0006-341X.2004.00250.x
ISI #: 000225939300012
ISSN: 0006-341X
Category: A1
Type: Journal Contribution
Appears in Collections: Research publications

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