时 间：2022年11月10日10：00-11：00
地 点：腾讯会议ID：785-901-143
报告人：夏志明 教授
主持人：项冬冬 教授
摘 要：
In this talk, we proposes two types of weighted-averaging estimators of coefficients in segmented linear regressions with a possible threshold.We construct an approximate Mallows criterion which can be looked as an average of limit cases with corresponding threshold effect zero and infinite. Following Hansen (2007) we propose to weightedly average two estimators of coefficients separately with and without threshold effect where weights can be selected by minimizing the Mallows criterion. Further we construct another type of Mallows criterion which is an estimate of the squared error from the model average fit and is used to obtain the new weights for averaging. Under the second Mallows criterion, we find that the new weights make Mallows Model Average (MMA) estimator to be asymptotically optimal in the sense of achieving a lower squared error. Specially in the case of a possible abrupt change, new weights are proved to tend to either one or zero under the true model with only one threshold or tend to 1/2 under the true model without a threshold. Numerical results demonstrate that the proposed MMA estimator performs better under different complicated changes and does choose the true model under one possible abrupt change.
报告人简介：
夏志明，统计学博士、教授、博士生导师，任西北大学数学学院副院长、西北大学现代统计研究中心副主任、陕西省统计协会常务理事，主要致力于张量数据分析、大数据异质性结构推断、分布式统计推断与计算、生物统计学等数据科学理论与应用研究。在“Biometrika”、“Journal of Machine Learning Research”, “Technometrics”、“IEEE Transaction of Cybernetics”、“Statistics in Medicine”等国际统计与机器学习期刊以及“中国科学”、“应用概率统计”等国内期刊发表论文40余篇；主持国家自然科学基金项目4项，主持省部级项目3项, 作为骨干成员获得“陕西省科学技术进步奖”二、三等奖共2项，“陕西省高校科学技术奖”一等奖共2项，“陕西省**科技进步奖”一等奖1项；先后赴香港科技大学、佛罗里达大学等科研机构进行专业访问与学术交流。