Journal article
Semiparametric maximum likelihood variance component estimation using mixture moment structure models
Twin research and human genetics, Vol.9(3), pp.360-366
06/2006
DOI: 10.1375/183242706777591245
PMID: 16790146
Abstract
Nonnormal phenotypic distributions introduce significant problems in the estimation and selection of genetic models. Here, a semiparametric maximum likelihood approach to analyzing nonnormal phenotypes is described. In this approach, distributions are explicitly modeled together with genetic and environmental effects. Distributional parameters are introduced through mixture constraints, where the distribution of effects are discretized and freely estimated rather than assumed to be normal. Semiparametric maximum likelihood estimation can be used with a variety of genetic models, can be extended to a variety of pedigree structures, and has various advantages over other approaches to modeling nonnormal data.
Details
- Title: Subtitle
- Semiparametric maximum likelihood variance component estimation using mixture moment structure models
- Creators
- Kristian E Markon - Department of Psychology, University of Minnesota, Minneapolis, 55455, USA. mark0060@tc.umn.edu
- Resource Type
- Journal article
- Publication Details
- Twin research and human genetics, Vol.9(3), pp.360-366
- DOI
- 10.1375/183242706777591245
- PMID
- 16790146
- NLM abbreviation
- Twin Res Hum Genet
- ISSN
- 1832-4274
- eISSN
- 1839-2628
- Language
- English
- Date published
- 06/2006
- Academic Unit
- Psychological and Brain Sciences
- Record Identifier
- 9984083872202771
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