应用数学研究所
学术报告


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Speaker:

林路 教授,山东大学

Inviter: 王启华 研究员
Title:
Online Updating Statistics for Heterogenous Updating Regressions via Homogenization Techniques
Time & Venue:

2021.3.8 15:00 N202

Abstract:

Under the environment of big data streams, it is a common situation that the variable set of a model under study changes according to data streams. 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.

Affiliation:  

学术报告中国科学院数学与系统科学研究院应用数学研究所
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