Seminar
Frequentist’s Properties of Posterior Distributions in Semiparametric Regression Models
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관리자
작성일Date
20170928 17:53
조회Views
254
분야Field  Seminar  

날짜Date  20170929  시간Time  16:00 ~ 17:00 
장소Place  Math. Bldg. 404  초청자Host  포스텍수리응용센터 
연사Speaker  Minwoo Chae  소속Affiliation  Case Western Reserve university 
TOPIC  Frequentist’s Properties of Posterior Distributions in Semiparametric Regression Models  
소개 및 안내사항Content  In a smooth semiparametric model, the marginal posterior distribution for the ﬁnite dimensional parameter of interest is expected to be asymptotically equivalent to the sampling distribution of any eﬃcient estimator. The assertion leads to asymptotic equivalence of credible and conﬁdence sets for the parameter of interest and is known as the semiparametric Bernsteinvon Mises (sBvM) theorem. In this talk, the sBvM theorem is considered in regression models in which errors with symmetric densities play a role. Then, it is extended to a highdimensional semiparametric model where most components of the regression coeﬃcient are zero. Various asymptotic properties such as optimal convergence rate, adaptation, distributional approximation and selection consistency are studied. The results guarantee asymptotic eﬃciency, adaptiveness to unknown sparsity level and error density, model selection consistency and valid uncertainty quantiﬁcation for Bayes procedures. 
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