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统计学院学术报告预告

发布日期 : 2020-09-07 浏览次数 :

题目:Sieve Maximum Likelihood Regression Analysis of Dependent Current Status Data

报告人:胡涛 教授

摘要:Current status data occur in contexts including demographic studies and tumorigenicity experiments. In such cases, each subject is observed only once and the failure time of interest is either left- or right-censored (Kalbfleisch & Prentice, 2002). Many methods have been developed for the analysis of such data (Huang, 1996; Sun, 2006), most of which assume that the failure time and the observation time are independent completely or given covariates. In this paper, we present a sieve maximum likelihood approach for current status data when independence does not hold. A copula model and monotone I-splines are used and the asymptotic properties of the resulting estimators are established. In particular, the estimated regression parameters are shown to be semiparametrically efficient. An illustrative example is provided.

报告时间:2020年9月11日 10:30-11:30

地点:腾讯会议 ID:202900203

报告人简介:胡涛,首都师范大学数学科学学院教授,博士生导师。研究方向:生存分析、风能数据分析。2009年毕业于北京师范大学数学科学学院,获概率论与数理统计专业博士学位。美国University of Missouri 统计系博士后。2009年3月至2012年12月先后在新加坡国立大学统计与应用概率系、南洋理工大学数学与物理学院任Research Assistant 和Research Fellow。在国内外学术刊物Journal of the American Statistical Association、Biometrika、Renewable Energy、Energy Conversion and Management、中国科学:数学等上发表学术论文多篇。

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