American Statistical Association
New York City
Metropolitan Area Chapter

Levin Lecture Series: Fall 2018 Colloquium Seminars
Department of Biostatistics
Mailman School of Public Health
Columbia University



MULTIPLY ROBUST QUANTILE ESTIMATION WITH MISSING DATA

by

Peisong Han
Assistant Professor
Department of Biostatistics
University of Michigan

Host: Dr. Zhezhen Jin


Abstract

Quantiles provide a more complete picture of a data distribution compared to the mean and are of major interest in many cases. Quantile estimation is often complicated by the presence of missing data, and there has been only a limited literature dealing with this problem. We propose a general framework that combines the two widely adopted approaches for missing data analysis, the imputation approach and the inverse probability weighting approach. The proposed method allows multiple working models for both the missingness probability and the data distribution. The resulting estimators are multiply robust in the sense that they are consistent if any one of these models is correctly specified. Our proposed method is capable of dealing with many different missingness settings, including the estimation of both the marginal quantiles and the conditional quantiles for quantile regression with missing responses and/or covariates, with or without extra auxiliary variables. As an illustration, we will reanalyze the data collected from the AIDS Clinical Trials Group Protocol 175 (ACTG 175).


Date: Thursday, December 13, 2018
Time: 11:45 A.M. - 12:45 P.M.
Location: Mailman School of Public Health
Department of Biostatistics
722 West 168th Street
AR Building
8th Floor Auditorium
New York, New York

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