上海财经大学 > 科学研究 > 学术交流 > 学术报告
  • 统计与管理学院2017年学术报告第60期

    【主 题】 Strong selection consistency of Bayesian model selection algorithms

    【报告人】 Xuming He 教授

    University of Michigan

    【时 间】 2017年10月20日(星期五)10:00-11:00

    【地 点】 上海财经大学统计与管理学院大楼一楼1208会议室

    摘 要】Bayesian model selection algorithms can be used as an alternative to optimization-based methods for model selection, and there is evidence that Bayesian methods approximate the L0-penalty better, but not much has been published about model selection consistency of Bayesian methods in the high dimensional setting.  In this talk, we will discuss the notion of strong selection consistency and show that some of the simple spike-and-slab priors, if allowed to be sample-size dependent, can be strongly consistent even when the number of features exceeds the sample size. The spike-and-slab variable selection algorithms however are not so scalable outside the linear model framework. A more scalable alternative, called Skinny Gibbs, is introduced to mitigate the computational burden without losing strong selection consistency. Logistic regression with high dimensional covariates is used as a primary example. Part of the talk is based on joint work with Naveen Narisetty and Juan Shen.

    嘉宾简介】何旭铭,1989年在美国伊利诺伊大学香槟分校取得统计学博士学位。他在2011年加入密歇根大学并担任H. C. Carver 学院教授,之前曾执教于新加坡国立大学和美国伊利诺伊大学香槟分校。何旭铭教授在稳健统计方法,分位数回归和统计应用等领域开展了在国际上具有相当影响的独创性研究,担任多家世界著名统计期刊的主编或副主编。2005年,鉴于何旭铭教授在稳健性和半参数统计方法研究领域的杰出贡献及其深远影响,他当选为美国统计学会(ASA)院士 (Fellow)。他也是国际数理统计学会(IMS), 美国科学进步`学会(AAAS)以及国际统计学会(ISI)当选会员。