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Estimation for Varying-Coefficient Informative Survival Models

2018/12/23 17:46  点击:[]

报告人:张文扬 (英国约克大学)


 :20181224  15:30--17:30


 :理科楼 LD106


 : A proportional hazard function together with partial likelihood estimation is the most common approach to the analysis of censored data. However,  partial likelihood estimation is established on the grounds that the censoring is non-informative.  The partial likelihood approach enjoys many good properties when the censoring is indeed non-informative.  However, in reality, censoring can be informative.  One pays a price in the efficiency of the estimator if partial likelihood estimation is used when the censoring is indeed informative. This problem is particularly acute in the nonparametric case.  When censoring is informative, to make use of the information provided by the censoring times, it is better to take the local complete likelihood approach.  Motivated by the data set about the first birth interval in Bangladesh,  we propose here a varying-coefficient proportional hazard function to fit informatively censored data.  We take the complete likelihood approach coupled with local linear modelling to estimate the functional coefficients involved in the model. Asymptotic properties of the proposed estimator are established, they show the proposed estimator is indeed more efficient than the maximum local partial likelihood estimator.  A simulation study was conducted to demonstrate how much the proposed estimator improves the efficiency of the maximum local partial likelihood estimator when sample size is finite.  In reality, we do not know whether censoring is informative or not, and a cross-validation based criterion is proposed  to check whether the censoring is informative or not. Finally, the proposed varying-coefficient proportional hazard function, together with the proposed estimation method, is used to analyse the first birth interval in Bangladesh,  leading to some interesting findings.


报告人简介: 张文扬教授是英国顶尖大学约克大学的统计学首席教授,商务和经济统计方面的国际顶尖期刊 Journal of Business & Economic Statistics 的副主编。张文扬教授主要从事大数据分析,金融数据分析,高维数据分析,非参数建模、时间序列分析、空间数据分析,多层次建模,生存分析,结构方程模型等方向的研究。曾先后在英国伦敦政治经济学院、英国 Kent 大学、英国 Bath 大学、英国 York 大学任教,现为英国 York 大学统计学首席教授。他曾是英国皇家统计学会科研委员会委员(历史上仅有三位华人担任该委员会委员),曾经连续担任三届统计学三大国际顶尖期刊之一 Journal of the American Statistical Association 的副主编。


学院联系人:穆春来


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