学术活动
Local independence feature screening
2015-03-30
来源：科技处 供稿：冀俊宇 点击次数：学术活动名称：Local independence feature screening for nonparametric and semiparametric models by marginal empirical likelihood
主讲人：Chang Jinyuan(常晋源)（Department of Mathematics and Statistics, University of Melbourne）
时间：4月1日（周三）14:00-15:00
地点：必赢76net线路官网北一区文科楼 707 教室
摘要: We consider an independence feature screening technique for identifying explanatory variables that locally contribute to the response variable in high-dimensional regression analysis. Without requiring a specific parametric form of the underlying data model, our approach accommodates a wide spectrum of nonparametric and semiparametric model families. To detect the local contributions of explanatory variables, our approach constructs empirical likelihood locally in conjunction with marginal nonparametric regressions. Since our approach actually requires no estimation, it is advantageous in scenarios such as the single-index models where even specification and identification of a marginal model is an issue. By automatically incorporating the level of variation of the nonparametric regression and directly assessing the strength of data evidence supporting local contribution from each explanatory variable, our approach provides a unique perspective for solving feature screening problems. Theoretical analysis shows that our approach can handle data dimensionality growing exponentially with the sample size. With extensive theoretical illustrations and numerical examples, we show that the local independence screening approach performs promisingly. It is a joint work with Cheng Yong Tang and Yichao Wu.
