Marginal longitudinal semiparametric regression via penalized splines. (English) Zbl 1190.62085
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.
MSC:
| 62G08 | Nonparametric regression and quantile regression |
| 62H12 | Estimation in multivariate analysis |
| 65C60 | Computational problems in statistics (MSC2010) |
Keywords:
additive models; best prediction; maximum likelihood; Gibbs sampling; nonparametric regression; restricted maximum likelihood; varying coefficient modelsReferences:
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