TY - JOUR
T1 - Semiparametric maximum likelihood variance component estimation using mixture moment structure models
AU - Markon, Kristian E.
PY - 2006/6
Y1 - 2006/6
N2 - 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.
AB - 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.
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U2 - 10.1375/183242706777591245
DO - 10.1375/183242706777591245
M3 - Review article
C2 - 16790146
AN - SCOPUS:33745754038
SN - 1832-4274
VL - 9
SP - 360
EP - 366
JO - Twin Research and Human Genetics
JF - Twin Research and Human Genetics
IS - 3
ER -