Marginal longitudinal semiparametric regression via penalized splines

We study the marginal longitudinal nonparametric regression problem and some of its semiparametric extensions. We point out that, while several elaborate proposals for efficient estimation have been proposed, a relative simple and straightforward one, based on penalized splines, has not. After descr...

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Bibliographic Details
Published in:Statistics & probability letters 2010-08, Vol.80 (15), p.1242-1252
Main Authors: Al Kadiri, M., Carroll, R.J., Wand, M.P.
Format: Article
Language:English
Subjects:
Additive models
Additive models Best prediction Maximum likelihood Gibbs sampling Nonparametric regression Restricted maximum likelihood Varying coefficient models
Best prediction
Exact sciences and technology
General topics
Gibbs sampling
Mathematics
Maximum likelihood
Nonparametric inference
Nonparametric regression
Probability and statistics
Probability theory and stochastic processes
Restricted maximum likelihood
Sciences and techniques of general use
Statistics
Stochastic processes
Varying coefficient models
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Summary:We study the marginal longitudinal nonparametric regression problem and some of its semiparametric extensions. We point out that, while several elaborate proposals for efficient estimation have been proposed, a relative simple and straightforward one, based on penalized splines, has not. After describing our approach, we then explain how Gibbs sampling and the BUGS software can be used to achieve quick and effective implementation. Illustrations are provided for nonparametric regression and additive models.
ISSN:0167-7152
1879-2103
DOI:10.1016/j.spl.2010.04.002