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Abstract Details

Activity Number: 177
Type: Contributed
Date/Time: Monday, August 2, 2010 : 10:30 AM to 12:20 PM
Sponsor: IMS
Abstract - #308707
Title: Estimation and Hypothesis Testing in Submodels Using Fisher Estimating Functions
Author(s): Ryan Janicki*+
Companies: U.S. Census Bureau
Address: SRD HQ-5K116E, Washington, DC, 20233,
Keywords: Estimating Functions ; Rao Score Test ; Wald Test ; Submodels
Abstract:

This paper is concerned with the problem of estimation of a parameter in a submodel, and testing the submodel against the full model using estimators that are solutions to estimating equations. We make use of a class of estimating functions called Fisher estimating functions, which share some important properties with the Fisher score. We give two main results. We first give the form of the best estimating function for a submodel and show that when estimating a parameter using estimating equations, it is always better to parameterize the model with as few parameters as possible. We then give analogues of the Wald test statistic and the Rao score test statistic based on Fisher estimating functions and show that these test statistics are asymptotically chi-square distributed and asymptotically equivalent. An example is given using a simple estimating function for a missing data model.


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