The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Online Program Home
Abstract Details
|
Activity Number:
|
614
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Thursday, August 2, 2012 : 8:30 AM to 10:20 AM
|
|
Sponsor:
|
Biometrics Section
|
| Abstract - #304895 |
|
Title:
|
Comparing Methods of Small Sample Inference for Linear Mixed Models
|
|
Author(s):
|
Min Zhu*+ and Guixian Lin
|
|
Companies:
|
SAS Institute and SAS Institute
|
|
Address:
|
600 Research Dr, Cary, NC, 27513, United States
|
|
Keywords:
|
mixed model ;
small sample inference ;
Kenward-Roger method ;
approximate F test ;
degrees-of-freedom Method
|
|
Abstract:
|
Linear mixed models have become a popular framework for analyzing data that have complicated designs and structured covariances. No exact test is available for fixed effects except for balanced data and special covariance structures. Test statistics based on asymptotic distribution of parameter estimates are useful for large samples. However, these tests can be unreliable in applications with small sample sizes. Various methods have been proposed to better approximate small sample distributions of these test statistics. This paper compares the performance of these methods through simulation studies that vary in experiment design, covariance structure, sample size, and so on. It also investigates robustness to misspecification of covariance structures and nonnormal random effects.
|
The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
Back to the full JSM 2012 program
|
2012 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Continuing Education program, please contact the Education Department.