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王海斌:Bayesian Estimation and Variable Selection via Structured Elastic Net(时间1.11)
【 作者:  校对时间:2021年01月11日 08:33  访问次数: 】

报告人:王海斌 厦门大学教授

报告时间:1月11昌15:00

报告地点:数学与统计学院一楼报告厅

主办单位:数学与统计学院

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  报告摘要:Structured elastic net is a rather general and flexible technique of regularization and variable selection, which includes the elastic net, the smooth lasso and the spline lasso as special cases. We consider a fully Bayesian method to make statistical inference about it.  Main difficulty lies in that there exists an intractable term in the full conditional posterior of the tuning parameters, which makes ordinary MH algorithm unusable. We develop an exchange algorithm and a double MH sampler, respectively, to address this difficulty. We also consider an empirical posterior credible interval method with ``adaptively level'' for variable selection. The proposed methods are illustrated by the simulated examples, and applied to the diabetes and the biscuit dough datasets.

  王海斌,厦门大学数学科学学院教授、博士生导师,中国现场统计研究会理事、中国现场统计研究会高维数据统计分会理事。主要从事潜在变量模型、非/半参数统计模型及时间序列分析的研究工作。主持完成国家和福建省自然科学基金面上项目多项。多次应邀赴香港中文大学统计系进行合作研究。已在British Journal of Mathematical and Statistical Psychology、Computational Statistics and Data Analysis、Journal of Applied Probability等国内外数学、概率、统计、计量心理学等重要学术期刊上发表学术论文30余篇。