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  Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity

Hyvärinen, A., Zhang, K., Shimizu, S., & Hoyer, P. (2010). Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity. Journal of Machine Learning Research, 11, 1709-1731.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-C01C-9 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-7686-2
Genre: Journal Article

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 Creators:
Hyvärinen, A, Author
Zhang, K1, Author              
Shimizu, S, Author
Hoyer, P, Author
Affiliations:
1University of Helsinki, ou_persistent22              

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 Abstract: Analysis of causal effects between continuous-valued variables typically uses either autoregressive models or structural equation models with instantaneous effects. Estimation of Gaussian, linear structural equation models poses serious identifiability problems, which is why it was recently proposed to use non-Gaussian models. Here, we show how to combine the non-Gaussian instantaneous model with autoregressive models. This is effectively what is called a structural vector autoregression (SVAR) model, and thus our work contributes to the long-standing problem of how to estimate SVARlsquo;s. We show that such a non-Gaussian model is identifiable without prior knowledge of network structure. We propose computationally efficient methods for estimating the model, as well as methods to assess the significance of the causal influences. The model is successfully applied on financial and brain imaging data.

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 Dates: 2010-05
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Method: -
 Identifiers: BibTex Citekey: 6627
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Title: Journal of Machine Learning Research
Source Genre: Journal
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Publ. Info: Brookline, MA : Microtome Publishing
Pages: - Volume / Issue: 11 Sequence Number: - Start / End Page: 1709 - 1731 Identifier: ISSN: 1532-4435
CoNE: https://pure.mpg.de/cone/journals/resource/111002212682020