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

Abstract Details

Activity Number: 76
Type: Contributed
Date/Time: Sunday, August 1, 2010 : 4:00 PM to 5:50 PM
Sponsor: Section on Nonparametric Statistics
Abstract - #306947
Title: Dimension-Reduced Kernel Estimation for Distribution Functions with Incomplete Data
Author(s): Zonghui Hu*+
Companies: National Institutes of Health
Address: 6700 A Rockledge Dr, Bethesda, MD, 20892,
Keywords: Curse of dimensionality ; dimension reduction ; kernel regression ; ignorable missingness
Abstract:

This work focuses on the estimation of distribution functions with incomplete data, where the variable of interest $Y$ has ignorable missingness but the covariates are always observed. A semiparametric estimation is proposed, which is developed under a nonparametric kernel regression frame work, but with a parametric working index to condense the high dimensional covariate information for reduced dimension. This kernel dimension reduction estimator has double robustness to model misspecification and is most efficient if the working index adequately conveys the covariate information about the distribution of $Y$. The semiparametric estimation is applied to an HIV study for the effect of antiretroviral therapy on HIV virologic suppression.


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