Linked Life Data

15171804

Source:http://linkedlifedata.com/resource/pubmed/id/15171804

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
Pt 1
pubmed:dateCreated
2004-6-2
pubmed:abstractText
Missing data are very common in behavioural and psychological research. In this paper, we develop a Bayesian approach in the context of a general nonlinear structural equation model with missing continuous and ordinal categorical data. In the development, the missing data are treated as latent quantities, and provision for the incompleteness of the data is made by a hybrid algorithm that combines the Gibbs sampler and the Metropolis-Hastings algorithm. We show by means of a simulation study that the Bayesian estimates are accurate. A Bayesian model comparison procedure based on the Bayes factor and path sampling is proposed. The required observations from the posterior distribution for computing the Bayes factor are simulated by the hybrid algorithm in Bayesian estimation. Our simulation results indicate that the correct model is selected more frequently when the incomplete records are used in the analysis than when they are ignored. The methodology is further illustrated with a real data set from a study concerned with an AIDS preventative intervention for Filipina sex workers.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
May
pubmed:issn
0007-1102
pubmed:author
pubmed:issnType
Print
pubmed:volume
57
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
131-50
pubmed:dateRevised
2009-11-11
pubmed:meshHeading
pubmed:year
2004
pubmed:articleTitle
Bayesian model comparison of nonlinear structural equation models with missing continuous and ordinal categorical data.
pubmed:affiliation
Department of Statistics, The Chinese University of Hong Kong.
pubmed:publicationType
Journal Article, Comparative Study, Research Support, Non-U.S. Gov't