{"id":28583609,"url":"https://github.com/majianthu/aps2020","last_synced_at":"2025-06-11T05:09:32.547Z","repository":{"id":57948376,"uuid":"283475682","full_name":"majianthu/aps2020","owner":"majianthu","description":"Code for the paper 'Variable Selection with Copula Entropy' published on Chinese Journal of Applied Probability and Statistics","archived":false,"fork":false,"pushed_at":"2022-05-09T12:32:39.000Z","size":89,"stargazers_count":14,"open_issues_count":0,"forks_count":3,"subscribers_count":1,"default_branch":"master","last_synced_at":"2023-03-08T07:24:03.643Z","etag":null,"topics":["copula-entropy","distance-correlation","feature-engineering","feature-selection","hsic","mutual-information","variable-importance","variable-selection"],"latest_commit_sha":null,"homepage":"","language":"R","has_issues":false,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/majianthu.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2020-07-29T11:03:25.000Z","updated_at":"2023-02-15T06:56:44.000Z","dependencies_parsed_at":"2022-09-18T12:50:16.719Z","dependency_job_id":null,"html_url":"https://github.com/majianthu/aps2020","commit_stats":null,"previous_names":[],"tags_count":null,"template":null,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/majianthu%2Faps2020","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/majianthu%2Faps2020/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/majianthu%2Faps2020/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/majianthu%2Faps2020/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/majianthu","download_url":"https://codeload.github.com/majianthu/aps2020/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/majianthu%2Faps2020/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":259204818,"owners_count":22821162,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["copula-entropy","distance-correlation","feature-engineering","feature-selection","hsic","mutual-information","variable-importance","variable-selection"],"created_at":"2025-06-11T05:09:27.767Z","updated_at":"2025-06-11T05:09:32.524Z","avatar_url":"https://github.com/majianthu.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"# aps2020\nThis is the code for the paper 'Variable Selection with Copula Entropy' published on Chinese Journal of Applied Probability and Statistics. The preprint paper is available at [here](https://arxiv.org/abs/1910.12389) on ArXiv.\n\n* Ma, Jian. “Variable Selection with Copula Entropy.” Chinese Journal of Applied Probability and Statistics, 2021, 37(4): 405-420. See also arXiv preprint arXiv:1910.12389 (2019).\n\nIn the paper, three methods for variable selection are compared on the UCI [heart disease data](http://archive.ics.uci.edu/ml/datasets/heart+disease):\n* Copula Entropy [1],\n* Hilbert-Schimdt Independence Criterion (HSIC) [2,3],\n* Distance Correlation [4].\n\n The following additional independence measures are also considered in this version of comparison experiment:\n* Heller-Heller-Gorfine Tests of Independence [5],\n* Hoeffding's D test [6],\n* Bergsma-Dassios T* sign covariance [7],\n* Ball correlation [8],\n* BET: Binary Expansion Testing [9],\n* qad: Quantification of Asymmetric Dependence [10],\n* MixedIndTests [11],\n* NNS: Nonlinear Nonparametric Statistics [12],\n* subcopula based dependence measures [13],\n* MDM: Mutual Independence Measure [14].\n\nCopula Entropy does better than all the others measures in terms of predictibility and interpretability.\n\n#### References\n1. Ma, J., \u0026 Sun, Z. (2011). Mutual Information Is Copula Entropy. Tsinghua Science \u0026 Technology, 16(1), 51–54. See also arXiv preprint arXiv:0808.0845 (2008).\n2. Gretton, A., Fukumizu, K., Teo, C. H., Song, L., Schölkopf, B., \u0026 Smola, A. J. (2007). A Kernel Statistical Test of Independence. In Advances in Neural Information Processing Systems 20 (Vol. 20, pp. 585–592).\n3. Pfister, N., Bühlmann, P., Schölkopf, B., \u0026 Peters, J. (2018). Kernel-based Tests for Joint Independence. Journal of The Royal Statistical Society Series B-Statistical Methodology, 80(1), 5–31.\n4. Székely, G. J., Rizzo, M. L., \u0026 Bakirov, N. K. (2007). Measuring and testing dependence by correlation of distances. Annals of Statistics, 35(6), 2769–2794.\n5. Heller, R., Heller, Y., Kaufman, S., Brill, B., \u0026 Gorfine, M. (2016). Consistent distribution-free K-sample and independence tests for univariate random variables. Journal of Machine Learning Research, 17(1), 978–1031.\n6. Hoeffding, W. (1948). A Non-Parametric Test of Independence. Annals of Mathematical Statistics, 19(4), 546–557.\n7. Bergsma, W., \u0026 Dassios, A. (2014). A consistent test of independence based on a sign covariance related to Kendall’s tau. Bernoulli, 20(2), 1006–1028.\n8. Wenliang Pan, Xueqin Wang, Heping Zhang, Hongtu Zhu \u0026 Jin Zhu (2019). Ball Covariance: A Generic Measure of Dependence in Banach Space. Journal of the American Statistical Association, 115, 307-317.\n9. Zhang, K. (2019).BET on Independence. Journal of the American Statistical Association, Taylor \u0026 Francis, 114, 1620-1637.\n10. Junker, R. R.; Griessenberger, F. \u0026 Trutschnig, W. (2021). Estimating scale-invariant directed dependence of bivariate distributions. Computational Statistics \u0026 Data Analysis, 153, 107058.\n11. Genest, C.; Nešlehová, J. G.; Rémillard, B. \u0026 Murphy, O. A. Testing for independence in arbitrary distributions. Biometrika, 2019, 106, 47-68.\n12. Viole, Fred and Nawrocki, David N., Deriving Nonlinear Correlation Coefficients from Partial Moments (September 18, 2012). Available at SSRN: https://ssrn.com/abstract=2148522 or http://dx.doi.org/10.2139/ssrn.2148522\n13. Arturo Erdely. A subcopula based dependence measure. Kybernetika, 53(2), 231-243, 2017.\n14. Ze Jin, David S. Matteson. Generalizing Distance Covariance to Measure and Test Multivariate Mutual Dependence. arXiv preprint arXiv:1709.02532, 2017.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmajianthu%2Faps2020","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmajianthu%2Faps2020","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmajianthu%2Faps2020/lists"}