Source:http://linkedlifedata.com/resource/pubmed/id/12933602
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| Predicate | Object |
|---|---|
| rdf:type | |
| lifeskim:mentions | |
| pubmed:issue |
3
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| pubmed:dateCreated |
2003-8-22
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| pubmed:abstractText |
A popular way to represent clustered binary, count, or other data is via the generalized linear mixed model framework, which accommodates correlation through incorporation of random effects. A standard assumption is that the random effects follow a parametric family such as the normal distribution; however, this may be unrealistic or too restrictive to represent the data. We relax this assumption and require only that the distribution of random effects belong to a class of 'smooth' densities and approximate the density by the seminonparametric (SNP) approach of Gallant and Nychka (1987). This representation allows the density to be skewed, multi-modal, fat- or thin-tailed relative to the normal and includes the normal as a special case. Because an efficient algorithm to sample from an SNP density is available, we propose a Monte Carlo EM algorithm using a rejection sampling scheme to estimate the fixed parameters of the linear predictor, variance components and the SNP density. The approach is illustrated by application to a data set and via simulation.
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| pubmed:language |
eng
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| pubmed:journal | |
| pubmed:status |
PubMed-not-MEDLINE
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| pubmed:month |
Sep
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| pubmed:issn |
1465-4644
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| pubmed:author | |
| pubmed:issnType |
Print
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| pubmed:volume |
3
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| pubmed:owner |
NLM
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| pubmed:authorsComplete |
Y
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| pubmed:pagination |
347-60
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| pubmed:year |
2002
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| pubmed:articleTitle |
A Monte Carlo EM algorithm for generalized linear mixed models with flexible random effects distribution.
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| pubmed:affiliation |
Department of Statistics, Box 8203, North Carolina State University, Raleigh, NC 27695-8203, USA. jchen2@stat.ncsu.edu
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| pubmed:publicationType |
Journal Article
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