|
Activity Number:
|
268
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Tuesday, August 4, 2009 : 8:30 AM to 10:20 AM
|
|
Sponsor:
|
ENAR
|
| Abstract - #304465 |
|
Title:
|
Testing Variance Components in Multilevel Linear Models Using Approximate Bayes Factors
|
|
Author(s):
|
Benjamin Saville*+ and Amy Herring
|
|
Companies:
|
Vanderbilt University and The University of North Carolina at Chapel Hill
|
|
Address:
|
S-2323 Medical Center North, Nashville, TN, 37232-2158,
|
|
Keywords:
|
Bayes factors ; Laplace approximation ; hierarchical ; multilevel ; variance components
|
|
Abstract:
|
Testing whether a random coefficient should be included in a multilevel model involves the test of whether the variance of that random coefficient is equal to 0. This is problematic because the null hypothesis lies on the boundary of the parameter space. We extend an approach for the linear mixed model to multilevel models by scaling the random coefficients to the residual variance and introducing parameters that control the relative contribution of the random coefficients. After integrating over the random coefficients and variance components, the resulting integrals needed to calculate the Bayes factor can be efficiently approximated with Laplace's method. We illustrate our method using a study of infant birth weights in New York City.
|
