Linked Life Data

16625521

Source:http://linkedlifedata.com/resource/pubmed/id/16625521

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
2007-1-29
pubmed:abstractText
Longitudinal models are commonly used for studying data collected on individuals repeatedly through time. While there are now a variety of such models available (marginal models, mixed effects models, etc.), far fewer options exist for the closely related issue of variable selection. In addition, longitudinal data typically derive from medical or other large-scale studies where often large numbers of potential explanatory variables and hence even larger numbers of candidate models must be considered. Cross-validation is a popular method for variable selection based on the predictive ability of the model. Here, we propose a cross-validation Markov chain Monte Carlo procedure as a general variable selection tool which avoids the need to visit all candidate models. Inclusion of a 'one-standard error' rule provides users with a collection of good models as is often desired. We demonstrate the effectiveness of our procedure both in a simulation setting and in a real application.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Feb
pubmed:issn
0277-6715
pubmed:author
pubmed:copyrightInfo
Copyright 2006 John Wiley & Sons, Ltd.
pubmed:issnType
Print
pubmed:day
20
pubmed:volume
26
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
919-30
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
Longitudinal variable selection by cross-validation in the case of many covariates.
pubmed:affiliation
Department of Econometrics, University of Geneva, CH-1211 Geneva 4, Switzerland. eva.cantoni@metri.unige.ch
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't