The Minimum Description Length Guided Model Selection in Granger Causality Analysis

被引：0

作者：

Li, Fei ^{[1
]}

Lin, Qiang ^{[1
]}

Hu, Zhenghui ^{[1
]}

机构：

[1] Zhejiang Univ Technol, Coll Sci, Hangzhou 310023, Peoples R China

来源：

ISICDM 2018: PROCEEDINGS OF THE 2ND INTERNATIONAL SYMPOSIUM ON IMAGE COMPUTING AND DIGITAL MEDICINE | 2018年

关键词：

minimum description length; model selection; granger causality; linear model;

D O I：

10.1145/3285996.3286004

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

In data analysis and statistical modeling, the optimal model selected is the key to success. This article is mainly talked about the minimum description length applied in linear models to select and optimize models, which the minimum description principle could be also applied in many other model classes at the same time. The principle would give a more suitable methods in modeling and estimating. Combining with Granger causality, it's meanful to understand brain activities deeply.

引用

页码：37 / 41

页数：5

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