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Missing Covariates in Quantile Regression
澳门新葡8455最新网站:2014年09月23日 00:00 点击数:

报告人:Ying Wei

报告地点:澳门新葡8455最新网站501室

报告澳门新葡8455最新网站:2014年10月16日星期四15:30-16:30

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报告摘要:

Regression quantile can be underpowered or biased when there are missing values in some covariates. Depending on the missing data mechanism, we develop several approaches to handle missing covariates to correct the potential bias and achieve a better efficiency. The finite sample performances of our estimators are investigated through simulation studies. Finally, to illustrate the utility of the proposed methods, we apply our methodology to a national nutritional survey data on dietary intakes.

主讲人概况:

Dr. Ying Wei is a tenured Associate Professor of Biostatistics of Columbia University. She received her PhD in 2004 from the University of Illinois at Urbana-Champaign. Her research interests include quantile regression, longitudinal data analysis, measurement errors, and growth charts. She received the 2011 Noether Young Scholar Award from the American Statistical Association for her significant contributions to the advancement of nonparametric statistics. Currently she is the director of the Consulting Center at the Biostatistics Department at Columbia, and serves on the editorial board of Journal of the American Statistical Association. She has published discussion papers in Annals of Statistics and in The American Journal of Epidemiology.

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