Variable selection via nonconcave penalized likelihood in high dimensional medical problems

Mylona, K; Koukouvinos, C; Theodoraki, E-M; Katsaragakis, S

dc.contributor.author	Mylona, K	en
dc.contributor.author	Koukouvinos, C	en
dc.contributor.author	Theodoraki, E-M	en
dc.contributor.author	Katsaragakis, S	en
dc.date.accessioned	2014-03-01T01:58:50Z
dc.date.available	2014-03-01T01:58:50Z
dc.date.issued	2009	en
dc.identifier.issn	09731377	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/28749
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-77954781235&partnerID=40&md5=8794a1f7bbe15f8d8dc56ad85f90d06c	en
dc.subject	Generalized linear model	en
dc.subject	High-dimensional dataset	en
dc.subject	Nonconcave penalized likelihood	en
dc.subject	Trauma	en
dc.subject	Variable selection	en
dc.subject.other	Data sets	en
dc.subject.other	Diverse fields	en
dc.subject.other	Execution time	en
dc.subject.other	Generalized linear model	en
dc.subject.other	Health-study	en
dc.subject.other	High-dimensional	en
dc.subject.other	High-dimensional dataset	en
dc.subject.other	Logistic regressions	en
dc.subject.other	Model selection techniques	en
dc.subject.other	Penalized likelihood	en
dc.subject.other	Risk factors	en
dc.subject.other	Statistical modelling	en
dc.subject.other	Statistical models	en
dc.subject.other	Subset variable selection	en
dc.subject.other	Variable selection	en
dc.subject.other	Health risks	en
dc.subject.other	Medical problems	en
dc.subject.other	Statistics	en
dc.title	Variable selection via nonconcave penalized likelihood in high dimensional medical problems	en
heal.type	journalArticle	en
heal.publicationDate	2009	en
heal.abstract	Variable selection is fundamental to high-dimensional statistical modelling in diverse fields of sciences. Specially in health studies, many potential factors are introduced to determine an outcome variable. In our study, different statistical methods are applied to analyze trauma annual data, collected by 30 General Hospitals in Greece. The dataset consists of 6334 observations and at most 131 factors that include demographic, transport and intrahospital data. The statistical methods employed in this work were the nonconcave penalized likelihood methods, SCAD, LASSO, and Hard, the generalized linear logistic regression, and the best subset variable selection, used to detect possible risk factors of death. A variety of different statistical models are considered, with respect to the combinations of factors and the number of observations. A comparative survey reveals differences between results and execution times of each method. The performed analysis reveals several distinct advantages of the nonconcave penalized likelihood methods over the traditional model selection techniques. © 2009 by IJAMAS, CESER.	en
heal.journalName	International Journal of Applied Mathematics and Statistics	en
dc.identifier.volume	14	en
dc.identifier.issue	J09	en
dc.identifier.spage	1	en
dc.identifier.epage	11	en