报告人：林路教授

报告（一）

题目：A Simple and Bias-Corrected DC Method for Biased Estimation under Memory Constraint

报告摘要: We introduce a bias-corrected divide-and-conquer (BC-DC) method for biased estimation under the case of memory constraint. In order to introduce the new estimation, a precise representation of the local estimators obtained by the data in each batch is adopted to formulate a pro forma linear regression between the local estimators and the true parameter of interest. A least square is used within this framework to composite a global estimator of the parameter. Thus, the main difference from the classical DC method is that the new BC-DC method can absorb the information hidden in the statistical structure and the variables in each batch of data. Consequently, the resulting global estimator is strictly unbiased even if the local estimators have a non-negligible bias. Moreover, the global estimator is consistent under some mild conditions, and even can achieve root-n consistency when the number of batches is large. The new method is simple and computationally efficient, without use of any iterative algorithm. Moreover, the proposed BC-DC method applies to various biased estimations such as shrinkage-type estimation and nonparametric regression estimation.

报告时间：4月8日下午3:45-4:45

报告地点：统计学院211教室

报告（二）

题目：统计学中的多种复合方法

报告摘要: 通过几个实例讨论统计学中的多种复合估计方法，研究它们基本思想、它们之间的关系以及在大数据中的应用。

报告时间：4月8日下午5:00-6:00

报告地点：统计学院213会议室

报告人简介：林路是山东大学金融研究院教授、博士生导师、副院长；在南开大学获得博士学位后,先在南开大学任教，然后到山东大学任教至今；从事高维统计、非参数和半参数统计以及金融统计等方的研究，在国际统计学、机器学习和相关应用学科顶级期刊Annals of Statistics, Journal of Machine Learning Research, PLoS computational biology和其它重要期刊发表研究论文90余篇；主持过多项国家自然科学基金课题、博士点专项基金课题、山东省自然科学基金重点项目等；获得国家统计局颁发的统计科技进步一、二等奖，山东省优秀教学成果一等奖；是国家973项目、国家创新群体和教育部创新团队的核心成员，教育部应用统计专业硕士教育指导委员会成员，山东省政府参事。