Semiparametric maximum likelihood variance component estimation using mixture moment structure models.

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  • Author(s): Markon KE;Markon KE
  • Source:
    Twin research and human genetics : the official journal of the International Society for Twin Studies [Twin Res Hum Genet] 2006 Jun; Vol. 9 (3), pp. 360-6.
  • Publication Type:
    Journal Article
  • Language:
    English
  • Additional Information
    • Source:
      Publisher: Cambridge University Press Country of Publication: England NLM ID: 101244624 Publication Model: Print Cited Medium: Print ISSN: 1832-4274 (Print) Linking ISSN: 18324274 NLM ISO Abbreviation: Twin Res Hum Genet Subsets: MEDLINE
    • Publication Information:
      Publication: 2012- : Cambridge, England : Cambridge University Press
      Original Publication: Bowen Hills, QLD, Australia : Published for the ISTS by Australian Academic Press, [2005]-
    • Subject Terms:
      Likelihood Functions* ; Models, Genetic* ; Social Alienation* ; Twin Studies as Topic*; Humans ; Monte Carlo Method ; Phenotype ; Twins, Dizygotic/genetics ; Twins, Dizygotic/psychology ; Twins, Monozygotic/genetics ; Twins, Monozygotic/psychology
    • 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.
    • Publication Date:
      Date Created: 20060623 Date Completed: 20060816 Latest Revision: 20170713
    • Publication Date:
      20231215
    • Accession Number:
      10.1375/183242706777591245
    • Accession Number:
      16790146