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Abstract Details

Activity Number: 178
Type: Contributed
Date/Time: Monday, July 30, 2012 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #306432
Title: A Robust Linear Mixed Effects Model with Application to Method Comparison Studies
Author(s): Pankaj Choudhary*+ and Dishari Sengupta
Companies: The University of Texas at Dallas and The University of Texas at Dallas
Address: 800 W. Campbell Road, Richardson, TX, 75080,
Keywords: Agreement evaluation ; Concordance correlation ; EM algorithm ; Hierarchical model ; Skew t distribution ; Total deviation index
Abstract:

We generalize the usual normality based linear mixed effects model by assuming a multivariate skew-t distribution for the random effects and a multivariate t distribution for the error terms. The proposed model can simultaneously capture the effects of skewness and heavy tailedness in random effects, and the effect of heavy tailedness in error terms. These deviations from normality are often found in method comparison studies wherein the goal is to evaluate agreement between two or more methods of measuring a continuous response variable. Our work extends the model studied in Ho and Lin (2010) by letting the random effects and the errors have different degrees of freedom. We use a version of EM algorithm to compute the maximum likelihood estimates of model parameters. This algorithm relies on a hierarchical representation of the proposed model. The methodology is illustrated by applying it to a real data set from method comparison studies.


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