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Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity

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Takayasu Shimizu

Kenichi Shimizu

Toshiyoshi Shimizu

Akiko Shimizu

Masumi Shimizu

 

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Python package for causal discovery based on LiNGAM., , , , и . J. Mach. Learn. Res., (2023)GroupLiNGAM: Linear non-Gaussian acyclic models for sets of variables, , , и . CoRR, (2010)Estimation of linear non-Gaussian acyclic models for latent factors., , и . Neurocomputing, 72 (7-9): 2024-2027 (2009)Intelligent Small Object Detection for Digital Twin in Smart Manufacturing With Industrial Cyber-Physical Systems., , , , , , и . IEEE Trans. Ind. Informatics, 18 (2): 1377-1386 (2022)Counterfactual Explanations of Black-box Machine Learning Models using Causal Discovery with Applications to Credit Rating., , и . CoRR, (2024)Nonlinear Causal Discovery for High-Dimensional Deterministic Data., , , , , и . IEEE Trans. Neural Networks Learn. Syst., 34 (5): 2234-2245 (мая 2023)Discovery of Exogenous Variables in Data with More Variables Than Observations., , , , , и . ICANN (1), том 6352 из Lecture Notes in Computer Science, стр. 67-76. Springer, (2010)A Linear Non-Gaussian Acyclic Model for Causal Discovery., , , и . J. Mach. Learn. Res., (2006)Learning Instrumental Variables with Structural and Non-Gaussianity Assumptions., и . J. Mach. Learn. Res., (2017)Testing Significance of Mixing and Demixing Coefficients in ICA., , , , и . ICA, том 3889 из Lecture Notes in Computer Science, стр. 901-908. Springer, (2006)