Marginal longitudinal semiparametric regression via penalized splines. Academic Article

abstract

• 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.

authors

• Carroll, Raymond

published proceedings

• Stat Probab Lett

author list (cited authors)

• Kadiri, M. A., Carroll, R. J., & Wand, M. P.

citation count

• 5

complete list of authors

• Kadiri, M Al||Carroll, RJ||Wand, MP

publication date

• August 2010

publisher

• Elsevier  Publisher

published in

• Statistics and Probability Letters  Journal

keywords

• Additive Models
• Best Prediction
• Gibbs Sampling
• Maximum Likelihood
• Nonparametric Regression
• Restricted Maximum Likelihood
• Varying Coefficient Models

PubMed Central ID

• 21037941

Digital Object Identifier (DOI)

• 10.1016/j.spl.2010.04.002

start page

• 1242

end page

• 1252

volume

• 80

issue

• 15-16

URL

• http%3A%2F%2Fdx.doi.org%2F10.1016%2Fj.spl.2010.04.002