报告题目：Centre-augmented L2-type Regularization for Subgroup Learning

主 讲 人：林华珍

单 位：西南财经大学

时 间：12月24日16:00

地 点：公司二楼会议室

腾 讯 ID：775 409 457

摘 要：

The existing methods for subgroup analysis can be roughly divided into two categories: finite mixture models (FMM) and regularization methods with an L1 -type penalty. In this talk, by introducing the group centres and L2-type penalty in the loss function, we propose a novel centre-augmented regularization (CAR) method; this method can be regarded as a unification of the regularization method and FMM and hence exhibits higher efficiency and robustness and simpler computations than the existing methods. Particularly, its computational complexity is reduced from the O(n^2) of the conventional pairwise-penalty method to only O(nK), where n is the sample size and K is the number of subgroups. The asymptotic normality of CAR is established, and the convergence of the algorithm is proven. CAR is applied to a dataset from a multicenter clinical trial: Buprenorphine in the Treatment of Opiate Dependence; a larger R^2 is produced and three additional significant variables are identified compared to those of the existing methods.

简 介：

林华珍，西南财经大学教授，统计研究中心主任。国际数理统计学会IMS-fellow，教育部特聘教授，国家杰出青年科学基金获得者，国家百千万人才工程获得者，享受国务院政府特殊津贴专家。主要研究方向为非参数方法、转换模型、生存数据分析、函数型数据分析、潜变量分析、时空数据分析。研究成果发表在包括国际统计学四大顶级期刊AoS、JASA、JRSSB、Biometrika和计量经济学顶级期刊JOE及JBES上。先后多次主持国家基金项目，包括国家杰出青年基金及自科重点项目。林华珍教授是国际IMS-China、IBS-CHINA及ICSA-China委员，中国现场统计研究会数据科学与人工智能分会理事长，第九届全国工业统计学教学研究会副会长，中国现场统计研究会多个分会的副理事长。先后是国际统计学权威期刊《Biometrics》、《Scandinavian Journal of Statistics》、《Journal of Business & Economic Statistics》、《Canadian Journal of Statistics》、《Statistics and Its Interface》、《Statistical Theory and Related Fields》的Associate Editor，国内权威或核心学术期刊《数学学报》（英文）、《应用概率统计》、《系统科学与数学》、《数理统计与管理》编委会编委。