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Joint modelling complex longitudinal data and survival data

编辑:wfy 时间:2019年05月14日 访问次数:238

报告题目:Joint modelling complex longitudinal data and survival data 

报告人:  Wu Lang, Professor 

          Department of Statistics, University of British Columbia, CA

时间地点:2019522(星期三)下午14:30-  

            紫金港校区管理学院行政楼141417报告厅

摘要:In the analysis of longitudinal data and survival data, joint models are useful since the longitudinal data and survival data are often strongly associated. In practice, the longitudinal data can be highly complicated, such as being truncated and mixed types of discrete and continuous. In this talk, I will discuss some recent work to address these data complications in joint models. Another challenge for joint models is computation, since the likelihoods of joint models often involve high-dimensional and intractable integrations. I will also discuss a computationally efficient approximate likelihood method. The models and methods will be applied to the analysis of a recent HIV vaccine dataset.

  

欢迎参加!

联系人苏中根教授  suzhonggen@zju.edu.cn

       浙江大学数据科学研究中心、浙江大学数学科学学院统计学研究所 

报告人简介:

    吴浪加拿大哥伦比亚大学教授。1998年获华盛顿大学(University of Washington, Seattle, USA)统计学博士学位,1998-2000年在哈佛大学(Harvard University)从事博士后研究。2000年至今,在加拿大哥伦比亚大学(University of British Columbia)统计系任教。现任Canadian Journal of Statistics的副主编(2017-),曾任Computational Statistics and Data Analysis的副主编(2008 – 2011)。

    主要从事缺失数据、纵向数据、生物统计等领域的研究工作。在JASA(Journal of the American Statistical Association)Journal of the Royal Statistical SocietyStatistical ScienceBiostatisticsBiometrics等国际权威统计期刊上发表学术论文60多篇。出版专著两部。主持加拿大国家自然科学基金项目4项。