时间: 12月26日上午9:00--11:00
地点：统计楼105报告厅
第一场：9:00--10:00
报告人: 刘妍岩教授 武汉大学
报告题目: Censored cumulative residual independent screening for ultrahigh-dimensional survival data
摘要：
With the recent explosion of ultrahigh-dimensional data, extensive work has been carried out for screening methods which can e ectively reduce the dimensionality. However, censored survival data which often arise in clinical trials and genetic studies have been left greatly un-explored for ultrahigh-dimensional scenarios. We propose a novel feature screening procedure for the ultrahigh-dimensional survival data, and establish the ranking consistency and the sure independent screening properties. Compared with the existing methods, the proposed screening procedure is invariant to the monotone transformation, known or unknown, of the responses. Moreover, it can be readily applied to the ultrahigh-dimensional complete data when the censoring rate is zero. We evaluate the nite-sample performances of the proposed procedure by extensive simulation studies and numerical comparisons. As an application,we apply the proposed screening procedure to the mantle cell lymphoma study.
第二场：10:00--11:00
报告人:丁洁丽教授 武汉大学
报告题目：Additive Mixed Effect Model for Recurrent Gap Time Data
摘要：
Gap times between recurrent events are often of primary interest in medical and observational studies.The additive hazards model, focusing on risk differences rather than risk ratios,has been widely used in practice.However, the marginal additive hazards model does not take the dependence among gap times into account.In this paper,we propose an additive mixed effect model to analyze gap time data,and the proposed model includes a subject-specific random effect to account for the dependence among the gap times.Estimating equation approaches are developed for parameter estimation,and the asymptotic properties of the resulting estimators are established.In addition, some graphical and numerical procedures are presented for model checking.The finite sample behavior of the proposed methods is evaluated through simulation studies,and an application to a data set from a clinic study on chronic granulomatous disease (CGD) is provided.