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Group Sparsity via Approximated Information Criteria
澳门新葡8455最新网站:2017年06月23日 10:53 点击数:

报告人:林楠

报告地点:澳门新葡8455最新网站415报告厅

报告澳门新葡8455最新网站:2017年06月30日星期五10:00-11:00

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报告摘要:

We propose a new group variable selection and estimation method, and illustrate its application for the generalized linear model (GLM). This new method, termed “gMIC”, was derived from approximating the information criterion by a smooth unit dent function. The gMIC is derived as a smooth approximation of a group-version modification of the information criterion. The approximated information criterion is further reparameterized in a way that not only renders sparse estimation from a smooth programming problem but also facilitates a convenient way of circumventing post-selection inference. Compared to existing group variable selection and estimation methods, the gMIC is free of parameter tuning and hence computationally advantageous. We also establish the oracle property of the proposed method that is supported by both simulation studies and real examples.

主讲人概况:

Nan Lin is an Associate Professor in the Department of Mathematics at Washington University in St. Louis and has a joint appointment in the Division of Biostatistics, Washington University in St. Louis, School of Medicine. His methodological research is in the areas of statistical computing for massive data, Bayesian regularization, bioinformatics, longitudinal and functional data analysis and psychometrics. His applied research involves statistical analysis of data from anesthesiology, genomics and cognition. He teaches a wide range of statistics courses, including mathematical statistics, Bayesian statistics, linear models, experimental design, statistical computation, and nonparametric statistics.

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