{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T21:01:24Z","timestamp":1765486884612,"version":"3.37.3"},"reference-count":24,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2023,3,16]],"date-time":"2023-03-16T00:00:00Z","timestamp":1678924800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,3,16]],"date-time":"2023-03-16T00:00:00Z","timestamp":1678924800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100011141","name":"Agricultural Research Division, Institute of Agriculture and Natural Resources","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100011141","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Comput Stat"],"published-print":{"date-parts":[[2024,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Choosing a shrinkage method can be done by selecting a penalty from a list of pre-specified penalties or by constructing a penalty based on the data. If a list of penalties for a class of linear models is given, we introduce a predictive stability criterion based on data perturbation to select a shrinkage method from the list. Simulation studies show that our predictive method identifies shrinkage methods that usually agree with existing literature and help explain heuristically when a given shrinkage method can be expected to perform well. If the preference is to construct a penalty customized for a given problem, then we propose a technique based on genetic algorithms, again using a predictive criterion. We find that, in general, a custom penalty never performs worse than any commonly used penalties and there are cases the custom penalty reduces to a recognizable penalty. Since penalty selection is mathematically equivalent to prior selection, our method also constructs priors. Our methodology allows us to observe that the oracle property typically holds for penalties that satisfy basic regularity conditions and therefore is not restrictive enough to play a direct role in penalty selection. In addition, our methodology, can be immediately applied to real data problems, and permits us to take model mis-specification into account.<\/jats:p>","DOI":"10.1007\/s00180-023-01342-8","type":"journal-article","created":{"date-parts":[[2023,3,26]],"date-time":"2023-03-26T23:51:13Z","timestamp":1679874673000},"page":"1241-1280","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Predictive stability criteria for penalty selection in linear models"],"prefix":"10.1007","volume":"39","author":[{"given":"Dean","family":"Dustin","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5793-7265","authenticated-orcid":false,"given":"Bertrand","family":"Clarke","sequence":"additional","affiliation":[]},{"given":"Jennifer","family":"Clarke","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,3,16]]},"reference":[{"key":"1342_CR1","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1007\/s00180-013-0436-3","volume":"29","author":"P B\u00fchlmann","year":"2014","unstructured":"B\u00fchlmann P, Mandozzi J (2014) High-dimensional variable screening and bias in subsequent inference, with an empirical comparison. Comput Stat 29:407\u2013430","journal-title":"Comput Stat"},{"key":"1342_CR2","doi-asserted-by":"publisher","first-page":"477","DOI":"10.1214\/12-BA716","volume":"7","author":"G Celeux","year":"2012","unstructured":"Celeux G, Anbari M, Marin J et al (2012) Regularization in regression: comparing Bayesian and frequentist methods in a poorly informative situation. Bayesian Anal 7:477\u2013502","journal-title":"Bayesian Anal"},{"key":"1342_CR3","doi-asserted-by":"publisher","first-page":"1348","DOI":"10.1198\/016214501753382273","volume":"96","author":"J Fan","year":"2001","unstructured":"Fan J, Li R (2001) Variable selection via concave penalized likelihood and its oracle properties. J Am Stat Assoc 96:1348\u20131360","journal-title":"J Am Stat Assoc"},{"key":"1342_CR4","doi-asserted-by":"publisher","first-page":"1044","DOI":"10.1080\/01621459.2013.803972","volume":"108","author":"J Fan","year":"2013","unstructured":"Fan J, Lv J (2013) Asymptotic equivalence of regularization methods in thresholded parameter spaces. J Am Stat Assoc 108:1044\u20131061","journal-title":"J Am Stat Assoc"},{"issue":"1","key":"1342_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v033.i01","volume":"33","author":"J Friedman","year":"2010","unstructured":"Friedman J, Hastie T, Tibshirani R (2010) Regularization paths for generalized linear models via coordinate descent. J Stat Softw 33(1):1\u201322","journal-title":"J Stat Softw"},{"key":"1342_CR6","volume-title":"Computational statistics","author":"G Givens","year":"2013","unstructured":"Givens G, Hoeting J (2013) Computational statistics, 2nd edn. Wiley, Hoboken","edition":"2"},{"key":"1342_CR7","first-page":"1348","volume":"96","author":"K Hamidieh","year":"2018","unstructured":"Hamidieh K (2018) A data-driven statistical model for predicting the critical temperature of a superconductor. J Am Stat Assoc 96:1348\u20131360","journal-title":"J Am Stat Assoc"},{"issue":"4","key":"1342_CR8","first-page":"579","volume":"35","author":"T Hastie","year":"2020","unstructured":"Hastie T, Tibshirani R, Tibshirani RJ (2020) Best subset, forward stepwise or lasso? analysis and recommendations based on extensive comparisons. Stat Sci 35(4):579\u20135920","journal-title":"Stat Sci"},{"key":"1342_CR9","first-page":"54","volume":"58","author":"A Hoerl","year":"1962","unstructured":"Hoerl A (1962) Application of ridge analysis to regression problems. Chem Eng Prog 58:54\u201359","journal-title":"Chem Eng Prog"},{"key":"1342_CR10","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1198\/004017005000000319","volume":"48","author":"X Luo","year":"2006","unstructured":"Luo X, Stefanski L, Boos D (2006) Variable selection via concave penalized likelihood and its oracle properties. Technometrics 48:165\u2013175","journal-title":"Technometrics"},{"key":"1342_CR11","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1007\/s10463-012-0370-0","volume":"65","author":"W Qian","year":"2013","unstructured":"Qian W, Yang Y (2013) Model selection via standard error adjusted adaptive lasso. Ann Inst Stat Math 65:295\u2013318","journal-title":"Ann Inst Stat Math"},{"key":"1342_CR12","volume-title":"Real analysis","author":"H Royden","year":"2010","unstructured":"Royden H, FitzPatrick P (2010) Real analysis, 4th edn. Prentice-Jall, Hoboken","edition":"4"},{"key":"1342_CR13","doi-asserted-by":"publisher","unstructured":"Rudolph G (1996) Convergence of evolutionary algorithms in general search spaces. In: Proceedings of IEEE international conference on evolutionary computation, pp 50\u201354. https:\/\/doi.org\/10.1109\/ICEC.1996.542332","DOI":"10.1109\/ICEC.1996.542332"},{"key":"1342_CR14","doi-asserted-by":"crossref","unstructured":"Sj\u00f6burg J, Ljung L (1992) Overtraining, regularization, and searching for minimum in neural networks. In: Proceedings of the 4th IFAC symposium on adaptive systems in control and signal processing, pp 73\u201378","DOI":"10.1016\/B978-0-08-041717-2.50018-5"},{"key":"1342_CR15","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","volume":"58","author":"R Tibshirani","year":"1996","unstructured":"Tibshirani R (1996) Regression shrinkage and selection via the Lasso. J R Stat Soc Ser B 58:267\u2013288","journal-title":"J R Stat Soc Ser B"},{"key":"1342_CR16","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1198\/073500106000000251","volume":"25","author":"H Wang","year":"2007","unstructured":"Wang H, Li G, Giang G (2007) Robust regression shrinkage and consistent variable selection through the LAD-Lasso. J Bus Econ Stat 25:347\u2013355","journal-title":"J Bus Econ Stat"},{"key":"1342_CR17","doi-asserted-by":"publisher","first-page":"697","DOI":"10.1007\/s11222-019-09914-9","volume":"30","author":"W Wang","year":"2020","unstructured":"Wang W, Mukherjee S, Richardson S et al (2020) High-dimensional regression in practice: an empirical study of finite-sample prediction, variable selection and ranking. Stat Comput 30:697\u2013719","journal-title":"Stat Comput"},{"key":"1342_CR18","unstructured":"Wang Z, Zhu Z, Yu C (2020b) Variable selection in macroeconomic forecasting with many predictors. Submitted arXiv:2007.10160"},{"key":"1342_CR19","unstructured":"Willighagen E, Ballings M (2015) genalg: R based genetic algorithm. https:\/\/CRAN.R-project.org\/package=genalg, R package version 0.2.0"},{"key":"1342_CR20","doi-asserted-by":"publisher","first-page":"894","DOI":"10.1214\/09-AOS729","volume":"38","author":"CH Zhang","year":"2010","unstructured":"Zhang CH (2010) Nearly unbiased variable selection under minimax concave penalty. Ann Stat 38:894\u2013942","journal-title":"Ann Stat"},{"key":"1342_CR21","first-page":"2541","volume":"7","author":"P Zhao","year":"2006","unstructured":"Zhao P, Yu B (2006) On model selection consistency selection of Lasso. J Mach Learn Res 7:2541\u20132563","journal-title":"J Mach Learn Res"},{"key":"1342_CR22","doi-asserted-by":"publisher","first-page":"1418","DOI":"10.1198\/016214506000000735","volume":"101","author":"H Zou","year":"2006","unstructured":"Zou H (2006) The adaptive lasso and its oracle properties. J Am Stat Assoc 101:1418\u20131429","journal-title":"J Am Stat Assoc"},{"key":"1342_CR23","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","volume":"67","author":"H Zou","year":"2005","unstructured":"Zou H, Hastie T (2005) Regularization and variable selection via the elastic net. J R Stat Soc Ser B 67:301\u2013320","journal-title":"J R Stat Soc Ser B"},{"key":"1342_CR24","doi-asserted-by":"publisher","first-page":"1733","DOI":"10.1214\/08-AOS625","volume":"37","author":"H Zou","year":"2009","unstructured":"Zou H, Zhang HH (2009) On the adaptive elastic-net with a diverging number of parameters. Ann Stat 37:1733\u20131751","journal-title":"Ann Stat"}],"container-title":["Computational Statistics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00180-023-01342-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00180-023-01342-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00180-023-01342-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T01:47:59Z","timestamp":1729129679000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00180-023-01342-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,16]]},"references-count":24,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,5]]}},"alternative-id":["1342"],"URL":"https:\/\/doi.org\/10.1007\/s00180-023-01342-8","relation":{},"ISSN":["0943-4062","1613-9658"],"issn-type":[{"type":"print","value":"0943-4062"},{"type":"electronic","value":"1613-9658"}],"subject":[],"published":{"date-parts":[[2023,3,16]]},"assertion":[{"value":"26 August 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 February 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 March 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}