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 | 01677152 | en_US |
| dc.identifier.other | C1 | 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 and 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 | 010200 | en_US |
| dc.personcode | 0000065063;0000065065;110509 | en_US |
| dc.percentage | 000050 | en_US |
| dc.classification.name | Applied Mathematics | 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 | University of Wollongong;Texas A&M University - College Station | en_US |
