Marginal longitudinal semiparametric regression via penalized splines

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dc.contributor.author Al Kadiri, M en_US
dc.contributor.author Carroll, R. en_US
dc.contributor.author Wand, Matt en_US
dc.contributor.editor en_US
dc.date.accessioned 2011-02-07T06:17:51Z
dc.date.available 2011-02-07T06:17:51Z
dc.date.issued 2010 en_US
dc.identifier 2010000349 en_US
dc.identifier.citation Al Kadiri M, Carroll R., and Wand Matt 2010, 'Marginal longitudinal semiparametric regression via penalized splines', Elsevier BV, vol. 80, no. 15-16, pp. 1242-1252. en_US
dc.identifier.issn 0167-7152 en_US
dc.identifier.other C1UNSUBMIT en_US
dc.identifier.uri http://hdl.handle.net/10453/12992
dc.description.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. en_US
dc.language en_US
dc.publisher Elsevier BV en_US
dc.relation.isbasedon http://dx.doi.org/10.1016/j.spl.2010.04.002 en_US
dc.title Marginal longitudinal semiparametric regression via penalized splines en_US
dc.parent Statistics & Probability Letters en_US
dc.journal.volume 80 en_US
dc.journal.number 15-16 en_US
dc.publocation The Netherlands en_US
dc.identifier.startpage 1242 en_US
dc.identifier.endpage 1252 en_US
dc.cauo.name SCI.Mathematical Sciences en_US
dc.conference Verified OK en_US
dc.for 010400 en_US
dc.personcode 0000065063 en_US
dc.personcode 0000065065 en_US
dc.personcode 110509 en_US
dc.percentage 100 en_US
dc.classification.name Statistics en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords Additive models; Best prediction; Maximum likelihood; Gibbs sampling; Nonparametric regression; Restricted maximum likelihood; Varying coefficient models en_US
dc.staffid 110509 en_US


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