JSM 2004 - Toronto

Abstract #300890

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Activity Number: 151
Type: Topic Contributed
Date/Time: Monday, August 9, 2004 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #300890
Title: Linear Mixed Models With Semiparametric Generalized Skew-elliptical Random Effects
Author(s): Yanyuan Ma*+ and Marc G. Genton and Marie Davidian
Companies: North Carolina State University and North Carolina State University and North Carolina State University
Address: , Raleigh, NC, 27606,
Keywords: mixed effect ; skew-elliptical
Abstract:

We relax the standard normal assumption of the random effects in linear random effect models to semiparametric generalized skew-elliptical distributions (SGSE). The SGSE family is able to take into account heavy tail, skewness, and multimodality through the elliptical part, the coefficients in the skewing function part, and a tuning parameter respectively. When the tuning parameter is set to 0, the distribution reduces to an elliptical distribution. SGSE family facilitates simple sampling procedure, which reduces computation complexity for Monte Carlo type algorithms. It also avoids artifactual fluctuations which tend to occur for SNP distributions and mixture distributions.

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