This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 580
Type: Contributed
Date/Time: Wednesday, August 4, 2010 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #307127
Title: Information Matrices in Estimating Function Approach: Test for Model Misspecification and Model Selection
Author(s): Qian Zhou*+ and Peter Song and Mary Thompson
Companies: Harvard School of Public Health and University of Michigan and University of Michigan
Address: 655 Huntington Avenue, SPH2, 4th floor, Boston, MA, 02115, United States
Keywords: Estimating functions ; Generalized estimating equations ; Information unbiasedness ; Model misspecification

In this paper, we focus on the situations that the estimation accuracy of mean structure parameters is guaranteed by correct specification of the first moment, but the estimation efficiency could be diminished due to misspecification of the second moment. We propose an information ratio (IR) statistic to test for misspecification of the variance/covariance structure through comparing two forms of information matrix: the negative sensitivity matrix and variability matrix. We also propose an information discrepancy criterion (IDC) to select the best fitting variance/covariance structure from a class of candidates. Simulation studies have shown that the IR statistic provides a powerful test to detect different scenarios of misspecification. In addition, the IR test and IDC show substantial improvement over existing methods, including the information matrix test of White (1982) and others.

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