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英国约克大学张文扬教授:Estimation for Varying-Coefficient Informative Survival Models

光华讲坛——社会名流与企业家论坛第 5201期

主题:Estimation for Varying-Coefficient Informative Survival Models主讲人:英国约克大学张文扬教授

主持人:统计学院 林华珍教授

时间:2019年1月3日(星期四)下午3:00-4:00

地点:西南财经大学柳林校区弘远楼408会议室

主办单位:统计研究中心 统计学院 科研处

主讲人简介:

张文扬教授是英国约克大学统计学首席教授,统计学顶级期刊JASA的Associate Editor,IMS(国际数理统计学会)教材-专著系列的编委会委员,英国皇家统计学会科研分会委员(历史上仅有三位华人担任该委员会委员)。他在非参数统计、时间序列、统计计算、生存分析、结构方程模型等领域发表了很多高质量的研究论文,其中有12篇文章发表在统计学顶级期刊AOS,JASA,JRSSB及Biometrika上。他和合作者1999年合作发表在AOS的文章,已经成为半参数变系数模型(VCM)研究中的经典工作,几乎被每一篇有关VCM文章所引用,文章的引用次数超过600次(Google citations)。他2002年发表在Genetics上另一篇论文研究Bayesian近似计算算法,已经被引用1850次。

具体详情请见其个人主页:https://www.york.ac.uk/maths/staff/wenyang-zhang/

主要内容:

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.