时间：2019年1月2日(周三)上午10:00-11:00
地点：闵行校区法商南楼302室
题目：A General Method for Quantile Estimation with Missing Data
报告人：Peisong Han Assistant Professor,University of Michigan
摘要：
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).
报告人简介：
Peisong Han is an Assistant Professor in the Department of Biostatistics. He received his Ph.D. in Biostatistics from the University of Michigan in 2013. Before joining the University of Michigan in 2018, Dr. Han was an Assistant Professor in the Department of Statistics and Actuarial Science at the University of Waterloo in Canada from 2013 to 2017. His primary research interests include (i) missing data problems in public health studies and survey sampling, and (ii) data integration, especially when summary information is available for some studies and individual-level data is available for others.