报告时间：2019年10月28日（周一）上午10:30 - 11:30
报告地点：明德主楼 1016 会议室
报告主题：A Global Bias-Correction DC Method for Biased Estimation under Memory Constraint
报告摘要：This paper introduces a global bias-correction divide-and-conquer (GBC-DC) method for biased estimation under the case of memory constraint. In order to introduce the new estimation, a closed 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 squares 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 GBC-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 and local bias-correction. Moreover, the proposed GBC-DC method applies to various biased estimations such as shrinkage-type estimation and nonparametric regression estimation. Based on our comprehensive simulation studies, the proposed GBC-DC approach is significantly bias-corrected, and the behavior is comparable with that of the full data estimation.
报告人简介：林路，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100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等和二等奖，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。