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

Abstract Details

Activity Number: 518
Type: Contributed
Date/Time: Wednesday, August 4, 2010 : 10:30 AM to 12:20 PM
Sponsor: ENAR
Abstract - #307394
Title: Evaluating Statistical Hypotheses Using Nonidentifiable Estimating Functions
Author(s): Guanqun Cao*+ and David Todem and Lijian Yang and Jason Peter Fine
Companies: Michigan State University and Michigan State University and Michigan State University and The University of North Carolina at Chapel Hill
Address: A413 Wells Hall, East Lansing, MI, 48824,
Keywords: General estimating function ; Global sensitivity analysis ; Infimum/Supremum statistic ; Model misspecification ; Missing data not at random ; Nonidentifiable models;
Abstract:

Many statistical models in biomedical research contain non and weakly identified parameters under interesting parametric formulations. Due to identifiability concerns, tests concerning some model parameters cannot use conventional statistical theory to assess significance. This paper extends the literature by developing a test statistic that can be used to evaluate hypotheses for any nonidentifiable estimating function. We derive the limiting distribution of this test statistic, and propose resampling approaches to approximate its asymptotic distribution. The methodology's practical utility is illustrated in simulations and an analysis of quality-of-life outcomes from a longitudinal study on breast cancer.


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