报告题目：On Estimation of General Index Model for Survival Data
主讲人：马彦源教授（宾州州立大学）
时间：2017年6月2日14:30-15:30
地点：北院卓远楼305
主办单位：统计与数学学院

摘要：
We propose a general index model for survival data, which generalizes many commonly used semiparametric survival models and belongs to the framework of dimension reduction. Using a combination of geometric approach in semiparametrics and martingale treatment in survival data analysis, we devise estimation procedures that are feasible and do not require covariate-independent censoring as assumed in many dimension reduction methods for censored survival data. We establish the root-$n$ consistency and asymptotic normality of the proposed estimators and derive the most efficient estimator in this class for the general index model. Numerical experiments are carried out to demonstrate the empirical performance of the proposed estimators and an application to an AIDS data further illustrates the usefulness of the work.

马彦源教授简介：应用数学博士（MIT），宾州州立大学教授，主要研究领域为半参数模型，测量误差模型，降维理论，潜在变量模型等。在Annals of Statistics, Journal of the Royal Statistical Society (Series B), Journal of the American Statistical Association, Biometrika，Journal of Econometrics等顶级统计学/计量经济学期刊发表过三十余篇论文，现担任顶级期刊JRSSB的副主编。

报告题目：Kernel Averaging Estimators
主讲人：张新雨博士（中国科学院）
时间：2017年6月2日15:30-16:30
地点：北院卓远楼305
主办单位：统计与数学学院

摘要：
Bandwidth or smoothness parameter selection has always been a key issue for kernel regression analysis despite the fact that kernel regression has rapidly become a regular tool for explorative empirical research. The issue of bandwidth selection is a fundamental model selection problem stemming from the uncertainty about the smoothness of the regression. In this paper, we advocate a model averaging approach to circumvent the problem caused by this uncertainty. Our new approach involves averaging across a series of Nadaraya-Watson kernel estimators each under a different bandwidth, with weights for these different estimators chosen such that a least-squares cross validation criterion is minimised. We prove that the resultant combined-kernel estimator achieves the smallest possible asymptotic aggregate squared error. The superiority of the new estimator over estimators based on widely accepted conventional bandwidth choices in finite samples is demonstrated in a simulation study and a real data example.
张新雨博士简介：张新雨，统计学博士（中科院），数学和系统科学研究所副研究员，优秀青年基金获得者，中科院优秀博士学位论文奖（2011）获得者，《系统科学与数学》期刊编委。主要研究领域为模型平均/选择，组合预测以及混合效应模型。在Annals of Statistics, Journal of the American Statistical Association, Biometrika, Journal of Econometrics等顶级统计学和计量经济学期刊发表过数十篇论文。