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Activity Number: 256 - Contemporary Mixed Model Methodology and Applications
Type: Invited
Date/Time: Tuesday, August 4, 2020 : 1:00 PM to 2:50 PM
Sponsor: Biometrics Section
Abstract #309190
Title: Consistent Maximum Likelihood Estimation In Mixed Models Using Subsets
Author(s): Karl Oskar Ekvall* and Galin Jones
Companies: Karolinska Institute and University of Minnesota
Keywords: mixed models; maximum likelihood; consistency; subsets

Despite the popularity of mixed models, it was unknown until recently whether maximum likelihood estimators are consistent even in some simple generalized linear mixed models with crossed random effects. In this talk, new results that use a non-trivial relation between the likelihood of the full data and that of a subset to establish consistency are presented. The relation allows one to formalize the intuition that estimators based on the full data should be consistent if those based on a subset are. The talk covers how subsets can be used to prove consistency of maximum likelihood estimators in mixed models, including ones with crossed random effects, but also how the argument can be extended to a much larger class of parametric models.

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

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