报告简介  Abstract

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. In particular, we develop an exchange algorithm and a double Metropolis-Hastings (MH) sampler, respectively, to sample from the full conditional posterior of the tuning parameters, in which there exists an intractable term such that the ordinary MH algorithm cannot be applied. 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.

嘉宾简介  About the Speaker

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、Journal of Nonparametric Statistics、Journal of Time Series Analysis、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。

讲座海报Poster