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Activity Number: 345 - High-Dimensional Statistics
Type: Contributed
Date/Time: Tuesday, July 30, 2019 : 10:30 AM to 12:20 PM
Sponsor: IMS
Abstract #306611
Title: Divergence Based Inference for High-Dimensional GLMM
Author(s): Lei Li* and Anand Vidyashankar
Companies: George Mason University and George Mason University
Keywords: GLMM; Divergence Method; Contingency Tables; Random Effects; Robust

High dimensional generalized linear mixed models (GLMM) arise in several contemporary applications spanning various scientific disciplines. Robust and Efficient inference in these problems are challenging due to heavy computational burden and theoretical challenges. Specifically, robustness analyses require investigation of the effect of choice of the random effect distribution on the parameter estimates. In this presentation we describe a new divergence based methodology which allows for a large class of distributions for the random effect distribution. Using techniques from convex analyses, we establish both theoretical and computational properties of our proposed procedure. We illustrate with an application to high-dimensional sparse contingency table analysis.

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

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