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

Abstract Details

Activity Number: 25
Type: Topic Contributed
Date/Time: Sunday, August 1, 2010 : 2:00 PM to 3:50 PM
Sponsor: IMS
Abstract - #306561
Title: Nonparametric Regression with Missing Data
Author(s): Sam Efromovich*+
Companies: UT Southwestern Medical Center
Address: 800 West Campbell Rd , Richardson, TX, 75080,
Keywords: Adaptation ; Asymptotic ; MAR ; Small Sample
Abstract:

Regression based on missing at random (MAR) responses is a familiar topic in the missing data literature. Imputation is the traditional remedy. What will be if inadvertently predictors, corresponding to missing responses, are also lost and only ``complete-case'' pairs of observations are available? It is well documented in the literature that a complete-case analysis is generally inadmissable. It is proved that, contrary to this general rule, in a nonparametric regression this approach implies the same sharp minimax mean integrated squared error (MISE) convergence as by a sharp minimax estimator based on the underlying MAR sample. Then ``the table is turned around'' and a less studied, even in the parametric and semiparametric literature, problem of missed at random (MAR) predictors is explored. Both methodology and methods of estimation are presented.


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