主  題：Robust Conditional Sure Independence Screening via Blum-Kiefer-Rosenblatt Correlation learning

內容簡介：Marginal screening methods have been widely used in the high dimensional data analysis. Despite they are easy to implement, they still suffer from the failure in detecting the important predictors with weak marginal signals. In this paper we develop a model-free conditional screening procedure based on conditional Blum-Kiefer-Rosenblatt correlation (CBKR for short), a metric to measure the conditional contributions of predictors to the response. Our proposed procedure is robust to the presence of extreme values and outliers in the observations, indicating it can accommodate the heterogeneity in the high dimensional data. We also show that, under mild conditions, the proposed procedure has the desirable sure screening property,which guarantee that all important predictors can be retained after screening with probability approaching one. Moreover, we provide a data-driven procedure to determine the number of features to be retained after screening. The usefulness of this conditional screening  procedure is illustrated by the simulation studies and an application to the gene expression microarray dataset of rat eye.

報告人：朱利平    教授    博導    國家優青

                  教育部新世紀優秀人才

                  中組部青年拔尖人才

時  間：2017-04-28    14:00

地  點：競慧東樓302

舉辦單位：理學院  統計科學與大數據研究院  科研部