{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T22:16:41Z","timestamp":1777501001856,"version":"3.51.4"},"reference-count":11,"publisher":"AIP Publishing","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1063\/5.0081504","type":"proceedings-article","created":{"date-parts":[[2022,4,6]],"date-time":"2022-04-06T20:30:33Z","timestamp":1649277033000},"page":"190002","source":"Crossref","is-referenced-by-count":2,"title":["Normalized entropy: A comparison with traditional techniques in variable selection"],"prefix":"10.1063","volume":"2425","author":[{"given":"Pedro","family":"Macedo","sequence":"first","affiliation":[]},{"given":"Maria Concei\u00e7\u00e3o","family":"Costa","sequence":"additional","affiliation":[]},{"given":"Jo\u00e3o Pedro","family":"Cruz","sequence":"additional","affiliation":[]}],"member":"317","reference":[{"key":"10.1063\/5.0081504_c1","unstructured":"A. Golan, G. Judge, and D. Miller, Maximum Entropy Econometrics: Robust Estimation with Limited Data (Wiley, Chichester, 1996)."},{"key":"10.1063\/5.0081504_c2","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRev.106.620"},{"key":"10.1063\/5.0081504_c3","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRev.108.171"},{"key":"10.1063\/5.0081504_c4","doi-asserted-by":"crossref","unstructured":"A. Golan, Foundations of Info-Metrics: Modeling, Inference, and Imperfect Information (Oxford University Press, New York, 2018).","DOI":"10.1093\/oso\/9780199349524.001.0001"},{"key":"10.1063\/5.0081504_c5","doi-asserted-by":"publisher","DOI":"10.3390\/e15051756"},{"key":"10.1063\/5.0081504_c6","doi-asserted-by":"crossref","unstructured":"P. Macedo, \u201cFreedman\u2019s paradox: a solution based on normalized entropy,\u201d (Springer, Cham, 2020) (to appear).","DOI":"10.1007\/978-3-030-56219-9_16"},{"key":"10.1063\/5.0081504_c7","unstructured":"P. Macedo, \u201cRidge regression and generalized maximum entropy: an improved version of the Ridge-GME parameter estimator,\u201d Commun. Stat. - Simul. Comput. 46, 3527\u20133539 (2017)."},{"key":"10.1063\/5.0081504_c8","doi-asserted-by":"publisher","DOI":"10.1016\/S0022-5347(17)41175-X"},{"key":"10.1063\/5.0081504_c9","doi-asserted-by":"crossref","unstructured":"R. Tibshirani, \u201cRegression shrinkage and selection via the lasso,\u201d J. Royal Stat. Soc. B 58, 267\u2013288 (1996).","DOI":"10.1111\/j.2517-6161.1996.tb02080.x"},{"key":"10.1063\/5.0081504_c10","doi-asserted-by":"crossref","unstructured":"T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference and Prediction (Springer, 2009).","DOI":"10.1007\/978-0-387-84858-7"},{"key":"10.1063\/5.0081504_c11","doi-asserted-by":"crossref","unstructured":"J. Wakefield, Bayesian and Frequentist Regression Methods (Springer, New York, 2013).","DOI":"10.1007\/978-1-4419-0925-1"}],"event":{"name":"INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2020","location":"Rhodes, Greece"},"container-title":["AIP Conference Proceedings","INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2020"],"original-title":[],"link":[{"URL":"http:\/\/aip.scitation.org\/doi\/pdf\/10.1063\/5.0081504","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,19]],"date-time":"2023-04-19T16:10:22Z","timestamp":1681920622000},"score":1,"resource":{"primary":{"URL":"https:\/\/pubs.aip.org\/aip\/acp\/article\/2823182"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":11,"URL":"https:\/\/doi.org\/10.1063\/5.0081504","relation":{},"ISSN":["0094-243X"],"issn-type":[{"value":"0094-243X","type":"print"}],"subject":[],"published":{"date-parts":[[2022]]}}}