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Abstract Details

Activity Number: 188
Type: Contributed
Date/Time: Monday, July 30, 2012 : 10:30 AM to 12:20 PM
Sponsor: Quality and Productivity Section
Abstract - #304987
Title: A Diagnostic of Influential Cases in Generalized Linear Mixed Models
Author(s): Junfeng Shang*+
Companies: Bowling Green State University
Address: 450 Mathematics Science Building, Bowling Green, OH, 43403, United States
Keywords: Modeling diagnostic ; generalized linear mixed model ; informational complexity criterion ; logistic regression ; case-deleted data set ; random effects
Abstract:

In the generalized linear mixed modeling setting, we develop a diagnostic for detecting influential cases based on the informational complexity criterion. The computational formula of the informational complexity criterion is evaluated using the Fisher information matrix. The diagnostic compares the informational complexity criteria between the full data set and a case-deleted data set. To demonstrate its effectiveness, the proposed diagnostic is applied in a data set where the mortality of cancer cells under radiation is modeled by the logistic regression with random effects.


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