报告简介  Abstract

Information criterion is very important in model selection and variable selection, more so in high dimensional settings. There are several existing ones designed in high dimension settings like high dimensional Bayesian Information Criteria (HBIC) and extended Bayesian Information Criterion (EBIC), which are proved to be useful in both theory and application. However, the subtle balance between unknown parameters and the complexity of the model is worthy to be further studied. In the paper, we propose a new Bayesian information criterion, which allows the dimensionality of covariates to grow exponentially fast with the sample size. Model selection consistency for both unpenalized and penalized estimators are established. Extensive simulation studies in commonly used models show that our information criterion has substantial improvement against other major competitors. Thus we name our method as improved Bayesian Information Criterion (IBIC). Moreover, we extend IBIC to select thresholding parameter for sparse covariance matrix estimation and the results are promising.

嘉宾简介  About the Speaker

ZHOU Wang, 2004年7月起在新加坡国立大学统计系任教，并于2009年1月获终身教授。现为新加坡国立大学正教授。 主要研究方向为: random matrices, SLE, high dimensional statistics。近年来发表有较高学术水平的论文七十多篇。 其中在概率统计学方面的国际公认的顶尖杂志Annals of Statistics, Journal of American Statistical Association, Biometrika, Annals of  Probability, Probability Theory and Related Fields, Annals of Applied Probability上发表论文十余篇。2012获得国际统计学会当选成员（Elected Member of International Statistical Institute）。

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