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報(bào)告人簡(jiǎn)介

汪敏，美國(guó)德州大學(xué)圣安東尼奧分校 (University of Texas at San Antonio) 商學(xué)院管理科學(xué)與統(tǒng)計(jì)系副教授（獲終身教職），博士生導(dǎo)師。2010年5月于美國(guó)克萊姆森大學(xué)（Clemson University）獲得統(tǒng)計(jì)碩士學(xué)位；2013年5月于克萊姆森大學(xué)大學(xué)獲得統(tǒng)計(jì)博士學(xué)位。2013年8月- 2017年12月在美國(guó)密歇根理工大學(xué)數(shù)學(xué)科學(xué)系工作和在2017年8月破格提前提升為副教授并獲得終身任期教授資格；現(xiàn)在在德州大學(xué)圣安東尼奧分校從事教學(xué)科研工作。近年來(lái)，先后參與和主持了美國(guó)自然科學(xué)基金委（NSF），密歇根交通部，以及美國(guó)衛(wèi)生院（NIH）的研究課題。其研究成果已發(fā)表在IISE Transactions, Naval Research Logistics, International Journal of Production Research, Bayesian Analysis, Computer & Industrial Engineering, The American Statistician, Computational Statistics & Data Analysis等國(guó)際權(quán)威期刊。主要研究方向包括貝葉斯統(tǒng)計(jì)；計(jì)算統(tǒng)計(jì)；統(tǒng)計(jì)推斷；質(zhì)量和可靠性工程研究；高維數(shù)據(jù)分析和統(tǒng)計(jì)應(yīng)用。

Part 01

報(bào)告題目

Novel Bayesian Approaches for Variable Selection in Quantile Regression Models

Part 02

參會(huì)方式

騰訊會(huì)議 ID：284 629 215

2021年11月29日：9：00-10：30

Part 03

報(bào)告摘要

Asymmetric Laplace (AL) specification has become one of the ideal statistical models for Bayesian quantile regression, as it not only offers fast convergence of Markov Chain Monte Carlo (MCMC), but also guarantees posterior consistency under model misspecification. However, variable selection under such a specification is a daunting task because, realistically, prior specification of regression parameters should take the quantile levels into consideration. In this talk, we first develop a novel three-stage computational scheme for the recent proposed quantile-specific g-prior, which starts with an expectation-maximization algorithm, followed by Gibbs sampler and ends with an importance re-weighting step that improves the accuracy of approximation. We then consider the problems of parameter estimation and variable selection in Bayesian hierarchical quantile regression model in high-dimensional settings, in which the model dimension could greatly exceed the sample size. An efficient sampling algorithm based on Gibbs sampler and Metropolis-Hastings algorithm to draw samples from the full conditional posterior distributions to make posterior inference. Finally, the performance of the proposed methods is examined through simulation studies and real-data applications.

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