Online Updating Statistics for Heterogenous Updating Regressions via Homogenization Techniques

报告人：林路教授（山东大学）

报告题目：Online Updating Statistics for Heterogenous Updating Regressions via Homogenization Techniques

报告时间：2021年04月22日周四下午3:30

地点：紫金港校区行政楼1417

主办单位：浙江大学数据科学研究中心、浙江大学数学科学学院

摘要：

We propose a homogenization strategy to represent the heterogenous models that are gradually updated in the process of data streams. With the homogenized representations, we can easily construct various online updating statistics such as parameter estimation, residual sum of squares and $F$-statistic for the heterogenous updating regression models. The main difference from the classical scenarios is that the artificial covariates in the homogenized models are not identically distributed as the natural covariates in the original models, consequently, the related theoretical properties are distinct from the classical ones. The asymptotical properties of the online updating statistics are established, which show that the new method can achieve estimation efficiency and oracle property, without any constraint on the number of data batches. The behavior of the method is further illustrated by various numerical examples from simulation experiments.

报告人简介:

林路是山东大学金融研究院教授、博士生导师；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；从事大数据、高维统计、非参数和半参数统计以及金融统计等方的研究，在国内外统计学、机器学习等顶级期刊(包括Annals of Statistics, Journal of Machine Learning Research, 中国科学)和其它重要期刊发表研究论文120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。

联系人：张立新 stazlx@zju.edu.cn