Shaobo Li

When and where: 11/02 (Thursday), 2017, 11AM-12PM, Lindner Hall 608

Speaker: Shaobo Li from University of Cincinnati


Title: Penalized Maximum Tangent Likelihood Estimation and Robust Variable Selection

Abstract: We introduce a new class of mean regression estimators -- penalized maximum tangent likelihood estimation -- for high-dimensional regression estimation and variable selection. We first explain the motivations for the key ingredient, maximum tangent likelihood estimation (MTE), and establish its asymptotic properties. We further propose a penalized MTE for variable selection and show that it is root-n consistent, enjoys the oracle property. The proposed class of estimators consists penalized L-2 distance, penalized exponential squared loss, penalized least trimmed square and penalized least square as special cases and can be regarded as a mixture of minimum Kullback-Leibler distance estimation and minimum L-2 distance estimation. Furthermore, we consider the proposed class of estimators under the high-dimensional setting when the number of variables d can grow exponentially with the sample size n, and show that the entire class of estimators (including the aforementioned special cases) can achieve the optimal rate of convergence. Finally, simulation studies and real data analysis demonstrate the advantages of the penalized MTE.

Bio: Shaobo Li is PhD candidate of Business Analytics in Carl H. Lindner College of Business, University of Cincinnati. He received M.S. degree in Statistics from University of Cincinnati, and B.S. degree in Mathematics from Shandong University, China. His research interests include high-dimensional robust statistics, nonparametric regression, ordinal data analysis, marketing data privacy, and corporate bankruptcy prediction. His work has been published in top Business Journals such as Marketing Science. He has been awarded the 2017 Lindner Outstanding Graduate Research Award, 2017 JSM Travel Award, and 2016 URC Graduate Student Summer Research Fellowship.