Linked Life Data

14652867

Source:http://linkedlifedata.com/resource/pubmed/id/14652867

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
23
pubmed:dateCreated
2003-12-3
pubmed:abstractText
We review and compare three estimators of median regression in linear models with longitudinal data. The estimators are constructed based on well-known ideas of weighting, decorrelating, and the working assumption of independence. Both asymptotic efficiency calculations and finite-sample Monte Carlo studies are used to assess the performance of these estimators. We find that their relative performances depend on the nature of covariates. The estimator under the working assumption of independence is computationally simple and yet has good relative performance when the covariates are invariant over time or when the within-subject correlations are small. Its relative performance in finite samples is also found to be more favourable than suggested by the asymptotic comparisons.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Dec
pubmed:issn
0277-6715
pubmed:author
pubmed:copyrightInfo
Copyright 2003 John Wiley & Sons, Ltd.
pubmed:issnType
Print
pubmed:day
15
pubmed:volume
22
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
3655-69
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2003
pubmed:articleTitle
Median regression for longitudinal data.
pubmed:affiliation
Department of Statistics, University of Illinois, Champaign, Illinois 61820, USA.
pubmed:publicationType
Journal Article, Comparative Study, Research Support, U.S. Gov't, Non-P.H.S., Research Support, Non-U.S. Gov't