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Department of Mathematics & Statistics GRADUATE STUDENT SEMINAR

Summary: Department of Mathematics & Statistics
Speaker: Yu Zhang
Title: A mean score method for missing and auxiliary covariate data in regression models
Date: Thursday, November 1, 2007
Time: 2.30 pm
Location: College West 307.20
Abstract: We consider regression analysis when incomplete or auxiliary covariate data
are available for all study subjects and, in addition, for a subset called the validation
sample, true covariate data of interest have been ascertained. The term auxiliary data
refers to data not in regression model, but thought to be informative about the true miss-
ing covariate data of interest. We discuss a method which is nonparametric with respect
to the association between available and missing data, allows missingness to depend on
available response and covariate values, and is applicable to both cohort and case-control
study designs. The method previously proposed by Flanders & Greenland (1991) and by
Zhao & Lipsitz (1992) is generalized and asymptotic theory is derived. Out expression
for the asymptotic variance of the estimator provides intuition regarding performance of
the method. Optimal sampling strategies for the validation set are also suggested by the
asymptotic results.
We also carried out a limited simulation study. Our simulation result shows that this


Source: Argerami, Martin - Department of Mathematics and Statistics, University of Regina


Collections: Mathematics