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报告题目:Fully Bayesian Inference for Structured Elastic Net

报告人:王海斌 教授

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. An appealing feature is that it can select groups of correlated predictors. 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.




报告人简介:王海斌,厦门大学数学科学学院教授、博士生导师。兼任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/半参数模型及时间序列分析的研究工作。曾主持国家自然科学基金面上项目和福建省自然科学基金面上项目、参与国家自然科学基金重点项目等。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、Journal of Time Series Analysis、Journal of Nonparametric Statistics、Psychometrika、Science China: Mathematics、Statistics and Computing等国内外数学、概率、统计、心理学等主流学术期刊上发表学术论文30余篇。