Online Program Home
My Program

Abstract Details

Activity Number: 73
Type: Contributed
Date/Time: Sunday, July 31, 2016 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #321288 View Presentation
Title: Fully Efficient and Outlier-Robust Estimation in the Linear Mixed Model
Author(s): Won Gyo Suh* and Howard Bondell
Companies: North Carolina State University and North Carolina State University
Keywords: linear mixed-effects model ; robust ; outlier ; efficient ; mixed model ; empirical likelihood

Since Linear mixed-effects models provide general and flexible approach to correlated data, they are widely used. Outliers in Linear mixed-effects model can be more problematic than in Linear model because Linear mixed-effects model has more source of variance. Large- and finite-sample efficiency and resistance to outliers are the key goals of robust statistics. Usually both are hard to achieve at the same time however Howard D. Bondell and Leonard A. Stefanski suggested robust estimation in linear model not only efficient but also robust in their paper (2013). Specifically, by using empirical likelihood efficiency of estimations is obtained and by constraining the associated sum of weighted studentized residual squares, robustness properties are obtained. In this paper, I will apply the idea to Linear mixed-effects models and compare efficiency and robustness with current robust estimator.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2016 program

Copyright © American Statistical Association