{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,2]],"date-time":"2025-11-02T10:40:15Z","timestamp":1762080015571,"version":"build-2065373602"},"reference-count":12,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,21]],"date-time":"2022-12-21T00:00:00Z","timestamp":1671580800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>In this paper, the LASSO method with extended Bayesian information criteria (EBIC) for feature selection in high-dimensional models is studied. We propose the use of the energy distance correlation in place of the ordinary correlation coefficient to measure the dependence of two variables. The energy distance correlation detects linear and non-linear association between two variables, unlike the ordinary correlation coefficient, which detects only linear association. EBIC is adopted as the stopping criterion. It is shown that the new method is more powerful than Luo and Chen\u2019s method for feature selection. This is demonstrated by simulation studies and illustrated by a real-life example. It is also proved that the new algorithm is selection-consistent.<\/jats:p>","DOI":"10.3390\/e25010014","type":"journal-article","created":{"date-parts":[[2022,12,22]],"date-time":"2022-12-22T01:33:12Z","timestamp":1671672792000},"page":"14","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Feature Selection in High-Dimensional Models via EBIC with Energy Distance Correlation"],"prefix":"10.3390","volume":"25","author":[{"given":"Isaac Xoese","family":"Ocloo","sequence":"first","affiliation":[{"name":"Department of Statistics, University of Georgia, Athens, GA 30602, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanfeng","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics, Bowling Green State University, Bowling Green, OH 43403, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1229","DOI":"10.1080\/01621459.2013.877275","article-title":"Sequential Lasso cum EBIC for feature selection with ultra-high dimensional feature space","volume":"109","author":"Luo","year":"2014","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the lasso","volume":"58","author":"Tibshirani","year":"1996","journal-title":"J. R. Stat. Soc. Ser. B (Methodol.)"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1348","DOI":"10.1198\/016214501753382273","article-title":"Variable selection via nonconcave penalized likelihood and its oracle properties","volume":"96","author":"Fan","year":"2001","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1418","DOI":"10.1198\/016214506000000735","article-title":"The adaptive lasso and its oracle properties","volume":"101","author":"Zou","year":"2006","journal-title":"J. Am. Stat. 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Stat."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1129","DOI":"10.1080\/01621459.2012.695654","article-title":"Feature screening via distance correlation learning","volume":"107","author":"Li","year":"2012","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1111\/j.1467-9868.2008.00674.x","article-title":"Sure independence screening for ultrahigh dimensional feature space","volume":"70","author":"Fan","year":"2008","journal-title":"J. R. Stat. Soc. Ser. B (Stat. Methodol.)"},{"key":"ref_11","first-page":"1603","article-title":"Adaptive Lasso for sparse high-dimensional regression models","volume":"18","author":"Huang","year":"2008","journal-title":"Stat. Sin."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1665","DOI":"10.1198\/016214508000001066","article-title":"Smoothly clipped absolute deviation on high dimensions","volume":"103","author":"Kim","year":"2008","journal-title":"J. Am. Stat. Assoc."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/25\/1\/14\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:47:10Z","timestamp":1760147230000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/25\/1\/14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,21]]},"references-count":12,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["e25010014"],"URL":"https:\/\/doi.org\/10.3390\/e25010014","relation":{},"ISSN":["1099-4300"],"issn-type":[{"type":"electronic","value":"1099-4300"}],"subject":[],"published":{"date-parts":[[2022,12,21]]}}}