Abstract #301385

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JSM 2002 Abstract #301385
Activity Number: 106
Type: Contributed
Date/Time: Monday, August 12, 2002 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Stat. Sciences*
Abstract - #301385
Title: An Approach to Capturing Data Information Contribution in Bayesian Data Analysis
Author(s): Suman Bhattacharya*+ and Robert Weiss
Affiliation(s): Johnson & Johnson Pharmaceutical R&D and University of California, Los Angeles
Address: 920 Route 202, P.O. Box 300, Raritan, New Jersey, 08869-0602, USA
Keywords: Observe Fisher's information ; Leverage ; Generalized linear model ; Conjugate prior ; Non-informative prior
Abstract:

To evaluate case information contribution, we developed one interpretable case influence measure (CIM), which assesses the information contribution of each observation to the posterior in a Bayesian analysis framework. In order to have a better understanding of CIM, I shall present a decomposition of this measure. I shall show that the CIM measure is a generalization of the leverage that is a measure for detecting influential observations. Both algebraic and numerical examples will be presented to demonstrate how our measure works in linear and generalized linear models. Parallel comparisons are drawn between our measure and available Bayesian case diagnostics measures for all numerical examples.

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Revised March 2002