报告人：刘妍岩
报告地点：腾讯会议ID:457 916 936
报告时间：2021年6月10日星期四10:30-11:30
报告摘要：
Regularization methods for the Cox proportional hazards regression with high-dimensional survival data have been studied extensively in the literature. However, if the models are misspecified, this would result in misleading statistical inference and prediction. To enhance
the prediction accuracy for the relative risk and the survival probability of clinical interest, we propose three model averaging approaches for the high-dimensional Cox proportional hazards regression. Based on the martingale residual process, we define the delete-one crossvalidation process. Further, we propose three novel cross-validation functionals, including the end-time cross-validation, integrated cross-validation, and supremum cross-validation, to achieve more accurate prediction for the risk quantities. The optimal weights for candidate models, without the constraint of summing up to one, can be obtained by minimizing these
functionals, respectively. The proposed model averaging approaches can attain the lowest possible prediction loss asymptotically. Furthermore, we develop a greedy model averaging algorithm to overcome the computational obstacle when the dimension is high. The performance of the proposed model averaging procedures is evaluated via extensive simulation studies, showing that our methods have superior prediction accuracy over the existing regularization
methods. As an illustration, we apply the proposed methods to the mantle cell lymphoma study.
主讲人简介：
刘妍岩,武汉大学数学与统计学院教授，博士生导师。2001年获武汉大学理学博士学位。主要研究方向为生存分析、半参数统计推断、高维数据统计分析等。在统计学期刊 Journal of Machine Learning Research, Biometrics, Biostatistics, Genetics，Lifetime Data Analysis等期刊发表SCI研究论文六十余篇。目前担任统计期刊Communication in Statistics 和 Statistical Papers的AE、中国现场统计学会第十届理事会常务理事、中国数学会女专委员会委员。
