Estimation and variable selection via frailty models with penalized likelihood

Androulakis, E; Koukouvinos, C; Vonta, F

dc.contributor.author	Androulakis, E	en
dc.contributor.author	Koukouvinos, C	en
dc.contributor.author	Vonta, F	en
dc.date.accessioned	2014-03-01T02:08:52Z
dc.date.available	2014-03-01T02:08:52Z
dc.date.issued	2012	en
dc.identifier.issn	02776715	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/29741
dc.subject	Clustered data	en
dc.subject	Cox proportional hazards model	en
dc.subject	Frailty model	en
dc.subject	Penalized likelihood	en
dc.subject	Variable selection	en
dc.subject.other	article	en
dc.subject.other	cluster analysis	en
dc.subject.other	data analysis	en
dc.subject.other	frailty model	en
dc.subject.other	mathematical analysis	en
dc.subject.other	maximum likelihood method	en
dc.subject.other	methodology	en
dc.subject.other	proportional hazards model	en
dc.subject.other	reliability	en
dc.subject.other	simulation	en
dc.subject.other	statistical analysis	en
dc.subject.other	statistical model	en
dc.title	Estimation and variable selection via frailty models with penalized likelihood	en
heal.type	journalArticle	en
heal.identifier.primary	10.1002/sim.5325	en
heal.identifier.secondary	http://dx.doi.org/10.1002/sim.5325	en
heal.publicationDate	2012	en
heal.abstract	The penalized likelihood methodology has been consistently demonstrated to be an attractive shrinkage and selection method. It does not only automatically and consistently select the important variables but also produces estimators that are as efficient as the oracle estimator. In this paper, we apply this approach to a general likelihood function for data organized in clusters, which corresponds to a class of frailty models, which includes the Cox model and the Gamma frailty model as special cases. Our aim was to provide practitioners in the medical or reliability field with options other than the Gamma frailty model, which has been extensively studied because of its mathematical convenience. We illustrate the penalized likelihood methodology for frailty models through simulations and real data. © 2012 John Wiley & Sons, Ltd.	en
heal.journalName	Statistics in Medicine	en
dc.identifier.doi	10.1002/sim.5325	en
dc.identifier.volume	31	en
dc.identifier.issue	20	en
dc.identifier.spage	2223	en
dc.identifier.epage	2239	en