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Semiparametric Maximum Likelihood Variance Component Estimation Using Mixture Moment Structure Models

Published online by Cambridge University Press:  21 February 2012

Kristian E. Markon*
Department of Psychology, University of Minnesota, Minneapolis, United States of America.
*Address for correspondence: Kristian E. Markon, Department of Psychology, University of Minnesota, Elliott Hall, 75 E. River Rd, Minneapolis, MN 55455, USA.


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Nonnormal phenotypic distributions introduce significant problems in the estimation and selection of genetic models. Here, a semiparametric maximum likelihood approach to analyzing non-normal 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.

Special Section on Advances in Statistical Models and Methods
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