报告人：杨玥含（中央财经大学）

时间：2021年4月30日16:30开始

地点：数统学院LD202

摘要:Many large-scale applications of regression models have correlated data. Although a variety of methods have been developed for this modeling problem, yet it is still challenging to keep an accurate estimation. We propose an adaptive and “reversed” penalty, which focuses on removing the shrinking bias and encouraging the grouping effect. Combining the L1 penalty and the Minimax Concave Penalty, we propose two methods called Smooth Adjustment for Correlated Effects and Generalized Smooth Adjustment for Correlated Effects. They can be seen as special adaptive estimators, but different from the traditional adaptive estimators that highly rely on the initial estimation. The proposed estimators obtain valid information even from the wrong initial estimates, providing stable and accurate estimation from finite samples. Under mild regularity conditions, we prove that the methods satisfy oracle property. Simulations show that the proposed procedures estimate the coefficients accurately in correlation structures. We also apply the proposed estimator to financial data and show that it is successful in asset allocation selection.

简介:杨玥含现为中央财经大学副教授，硕士生导师，中央财经大学青年英才，主要从事高维统计、复杂数据分析、资产配置等研究，作为通信作者在统计学四大之一Biometrika和作为第一作者在应用数学TOP期刊App.Math.Mod.，CSDA，JSPI，中国科学英文版,数学年刊英文版等国内外期刊发表论文十余篇，重庆大学2005级统计专业本科毕业，2019级北京大学优秀博士生毕业。曾去美国密西根大学做学术访问，多次在国内外学术会议做邀请报告。

邀请人：杨虎

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