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  • 统计与管理学院2018年学术报告第47期

    【主 题】 A Unified Approach for Censored Quantile Regression

    【报告人】 Naveen Narisetty, 助教授

                University of Illinois at Urbana-Champaign

    【时 间】 2018年12月12日(星期三)10:00-11:00

    【地 点】 上海财经大学统计与管理学院大楼1208会议室

    摘 要】Quantile regression provides an attractive tool for analyzing censored responses because the conditional quantile functions are often of direct interest in regression analysis,and moreover, the quantiles are often identifiable while the conditional mean functions arenot. Existing methods of estimation for censored quantiles are mostly limited to single left orright-censored data, with some attempts made to extend the methods to doubly-censoreddata. In this talk, I will present a new and unified approach, based on a variation of the data augmentation algorithm, to censored quantile regression estimation. The proposed method adapts easily to different forms of censoring including doubly censored and interval censored data and the resulting estimates improve on the performance of the best-known estimators developed for the singly censored data.

    嘉宾简介】Naveen Narisetty博士2016年毕业于美国密西根大学统计学系,之后加入美国伊利诺伊大学香槟分校统计学系。其研究兴趣包括高维数据、模型选择、贝叶斯统计等,在包括JASA、Annals of Statistics等国际统计学期刊上发表10余篇。