Marginal longitudinal semiparametric regression via penalized splines

Handle URI:
http://hdl.handle.net/10754/598758
Title:
Marginal longitudinal semiparametric regression via penalized splines
Authors:
Al Kadiri, M.; Carroll, R.J.; Wand, M.P.
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.
Citation:
Al Kadiri M, Carroll RJ, Wand MP (2010) Marginal longitudinal semiparametric regression via penalized splines. Statistics & Probability Letters 80: 1242–1252. Available: http://dx.doi.org/10.1016/j.spl.2010.04.002.
Publisher:
Elsevier BV
Journal:
Statistics & Probability Letters
KAUST Grant Number:
KUS-CI-016-04
Issue Date:
Aug-2010
DOI:
10.1016/j.spl.2010.04.002
PubMed ID:
21037941
PubMed Central ID:
PMC2964941
Type:
Article
ISSN:
0167-7152
Sponsors:
Wand’s research was partially supported by Australian Research Council Discovery Project DP0877055. Carroll’s research was supported by a grant from the US National Cancer Institute (CA57030) and by Award Number KUS-CI-016-04, made by King Abdullah University of Science and Technology, Saudi Arabia.
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Full metadata record

DC FieldValue Language
dc.contributor.authorAl Kadiri, M.en
dc.contributor.authorCarroll, R.J.en
dc.contributor.authorWand, M.P.en
dc.date.accessioned2016-02-25T13:40:38Zen
dc.date.available2016-02-25T13:40:38Zen
dc.date.issued2010-08en
dc.identifier.citationAl Kadiri M, Carroll RJ, Wand MP (2010) Marginal longitudinal semiparametric regression via penalized splines. Statistics & Probability Letters 80: 1242–1252. Available: http://dx.doi.org/10.1016/j.spl.2010.04.002.en
dc.identifier.issn0167-7152en
dc.identifier.pmid21037941en
dc.identifier.doi10.1016/j.spl.2010.04.002en
dc.identifier.urihttp://hdl.handle.net/10754/598758en
dc.description.abstractWe 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.en
dc.description.sponsorshipWand’s research was partially supported by Australian Research Council Discovery Project DP0877055. Carroll’s research was supported by a grant from the US National Cancer Institute (CA57030) and by Award Number KUS-CI-016-04, made by King Abdullah University of Science and Technology, Saudi Arabia.en
dc.publisherElsevier BVen
dc.subjectAdditive modelsen
dc.subjectBest predictionen
dc.subjectGibbs samplingen
dc.subjectMaximum likelihooden
dc.subjectNonparametric regressionen
dc.subjectRestricted maximum likelihooden
dc.subjectVarying coefficient modelsen
dc.titleMarginal longitudinal semiparametric regression via penalized splinesen
dc.typeArticleen
dc.identifier.journalStatistics & Probability Lettersen
dc.identifier.pmcidPMC2964941en
dc.contributor.institutionCentre for Statistical and Survey Methodology, School of Mathematics and Applied Statistics, University of Wollongong, Wollongong, New South Wales, Australia.en
kaust.grant.numberKUS-CI-016-04en

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