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學術動態
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統計學院學術報告預告

發布日期 : 2020-09-07 瀏覽次數 :

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

報告人:胡濤 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.

報告時間:2020年9月11日 10:30-11:30

地點:騰訊會議 ID:202900203

報告人簡介:胡濤,首都師范大學數學科學學院教授,博士生導師。研究方向:生存分析、風能數據分析。2009年畢業于北京師范大學數學科學學院,獲概率論與數理統計專業博士學位。美國University of Missouri 統計系博士后。2009年3月至2012年12月先后在新加坡國立大學統計與應用概率系、南洋理工大學數學與物理學院任Research Assistant 和Research Fellow。在國內外學術刊物Journal of the American Statistical Association、Biometrika、Renewable Energy、Energy Conversion and Management、中國科學:數學等上發表學術論文多篇。

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