Marginal longitudinal semiparametric regression via penalized splines
Copyright policy )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 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.
- Texas A&M University United States
- King Abdullah University of Science and Technology Saudi Arabia
- University of Wollongong Australia
via, longitudinal, Restricted maximum likelihood, Estimation in multivariate analysis, marginal, varying coefficient models, Gibbs sampling, Physical Sciences and Mathematics, splines, Nonparametric regression and quantile regression, Varying coefficient models, maximum likelihood, additive models, Computational problems in statistics, Best prediction, restricted maximum likelihood, semiparametric, Nonparametric regression, nonparametric regression, best prediction, regression, penalized, Additive models, Maximum likelihood
via, longitudinal, Restricted maximum likelihood, Estimation in multivariate analysis, marginal, varying coefficient models, Gibbs sampling, Physical Sciences and Mathematics, splines, Nonparametric regression and quantile regression, Varying coefficient models, maximum likelihood, additive models, Computational problems in statistics, Best prediction, restricted maximum likelihood, semiparametric, Nonparametric regression, nonparametric regression, best prediction, regression, penalized, Additive models, Maximum likelihood
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