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Activity Number: 193
Type: Contributed
Date/Time: Monday, August 10, 2015 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #316081
Title: New Robust Sandwich Estimators for Repeated Measures Data
Author(s): Bruce Schaalje* and Natalie Blades
Companies: Brigham Young University and Brigham Young University
Keywords: mixed model ; sandwich estimator ; repeated measures ; robust inference ; covariance estimation ; covariance selection
Abstract:

The use of linear mixed models for analyzing repeated measures data presents challenges when small sample sizes yield insufficient information with which both to learn about the covariance structure and estimate the covariance parameters. A possible solution is to base inferences on a robust sandwich estimator of the covariance matrix of estimates of mean parameters; however, this procedure has been questioned theoretically and has not been well characterized empirically. Nor have improvements to the robust sandwich estimator for independent data been used as possible improvements to robust estimators for repeated measures data. This paper proposes new robust estimators for repeated measures data and compares their performance using simulations. We discuss the effective use of these estimators in various small sample repeated measures applications.


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