Subsample Ignorable Likelihood for Regression Analysis with Missing Data

by cp2530 on April 11, 2011

Title: Subsample Ignorable Likelihood for Regression Analysis with Missing Data
Location: New York University, Tisch Bldg. LC-21
Link out: Click here
Description: The Center for the Promotion of Research Involving Innovative
Statistical Methodology (PRIISM) invites you to a talk by Rod Little, currently Associate Director for Research and Methodology, Bureau of the Census, on leave from Dept. of Biostatistics, University of Michigan.

Abstract: Two common approaches to regression with missing covariates are
complete-case analysis (CC) and ignorable likelihood (IL) methods. We review these approaches, and propose a hybrid class, subsample ignorable likelihood (SSIL) methods, which applies an IL method to the subsample of observations that are complete on one set of variables, but possibly incomplete on others. Conditions on the missing data mechanism are presented under which SSIL gives consistent estimates, but both CC and IL are inconsistent. We motivate and apply the proposed method to data from National Health and Nutrition Examination Survey, and illustrate properties of the methods by simulation. Extensions to non-likelihood analyses are also mentioned. (Joint Work with Nanhua Zhang).

There will be an informal reception at the end of the talk.

Start Time: 1:00
Date: 2011-04-15
End Time: 2:30

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