The Minimum Description Length Guided Model Selection in Granger Causality Analysis
Authors: 
Fei Li, Qiang Lin, Zhenghui HuAuthors Info & Claims
Fei Li, Qiang Lin, Zhenghui HuAuthors Info & ClaimsPages 37 - 41
Abstract
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.
References
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Rissanen, Jorma. 2012. Optimal estimation of parameters. Cambridge University Press, (2012).
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Hu, Meng, W. Li, and H. Liang. 2018. A Copula-Based Granger Causality Measure for the Analysis of Neural Spike Train Data. IEEE/ACM Transactions on Computational Biology and Bioinformatics 15.2(2018):562--569.
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- The Minimum Description Length Guided Model Selection in Granger Causality Analysis
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Published In
October 2018
166 pages
ISBN:9781450365338
DOI:10.1145/3285996
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- University of Electronic Science and Technology of China: University of Electronic Science and Technology of China
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 13 October 2018
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ISICDM 2018
ISICDM 2018: The 2nd International Symposium on Image Computing and Digital Medicine 2018
October 13 - 14, 2018
Chengdu, China
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