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  • 统计与管理学院2018年学术报告第7期

    【主 题】 Identification of local sparsity and variable selection for varying coefficient additive hazards models

    【报告人】 孙六全  研究员

    中国科学院数学与系统科学研究院

    【时 间】 2018年03月15日(星期四)15:00-16:00

    【地 点】 上海财经大学统计与管理学院大楼1208会议室

    摘 要】Varying coefficient models have numerous applications in a wide scope of scientific areas. Existing methods in varying coefficient models have mainly focused on estimation and variable selection. Besides selecting relevant predictors and estimating their effects, identifying the subregions in which varying coefficients are zero is important to deeply understand the local sparse feature of the functional effects of significant predictors. In this article, we propose a novel method to simultaneously conduct variable selection and identify the local sparsity of significant predictors in the context of varying coefficient additive hazards models. This method combines kernel estimation procedure and the idea of group penalty. The asymptotic properties of the resulting estimators are established. Simulation studies demonstrate that the proposed method can effectively select important predictors and simultaneously identify the null regions of varying coefficients. An application to a nursing home data set is presented.

    嘉宾简介】http://www.amt.ac.cn/member/sunliuquan/