{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T15:56:17Z","timestamp":1787500577033,"version":"build-2736575974"},"reference-count":39,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2020,9,2]],"date-time":"2020-09-02T00:00:00Z","timestamp":1599004800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["Emmy\u00a0Noether\u00a0grant\u00a0KL\u00a03037\/1\u20101"],"award-info":[{"award-number":["Emmy\u00a0Noether\u00a0grant\u00a0KL\u00a03037\/1\u20101"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100005156","name":"Alexander von Humboldt-Stiftung","doi-asserted-by":"publisher","award":["Feodor\u00a0Lynen\u00a0Fellowship"],"award-info":[{"award-number":["Feodor\u00a0Lynen\u00a0Fellowship"]}],"id":[{"id":"10.13039\/100005156","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Biometrics"],"published-print":{"date-parts":[[2021,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>We propose a new highly flexible and tractable Bayesian approach to undertake variable selection in non\u2010Gaussian regression models. It uses a copula decomposition for the joint distribution of observations on the dependent variable. This allows the marginal distribution of the dependent variable to be calibrated accurately using a nonparametric or other estimator. The family of copulas employed are \u201cimplicit copulas\u201d that are constructed from existing hierarchical Bayesian models widely used for variable selection, and we establish some of their properties. Even though the copulas are high dimensional, they can be estimated efficiently and quickly using Markov chain Monte Carlo. A simulation study shows that when the responses are non\u2010Gaussian, the approach selects variables more accurately than contemporary benchmarks. A real data example in the Web Appendix illustrates that accounting for even mild deviations from normality can lead to a substantial increase in accuracy. To illustrate the full potential of our approach, we extend it to spatial variable selection for fMRI. Using real data, we show our method allows for voxel\u2010specific marginal calibration of the magnetic resonance signal at over 6000 voxels, leading to an increase in the quality of the activation\u00a0maps.<\/jats:p>","DOI":"10.1111\/biom.13355","type":"journal-article","created":{"date-parts":[[2020,8,28]],"date-time":"2020-08-28T05:52:22Z","timestamp":1598593942000},"page":"809-823","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Bayesian variable selection for non\u2010Gaussian responses: a marginally calibrated copula approach"],"prefix":"10.1093","volume":"77","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5072-5347","authenticated-orcid":false,"given":"Nadja","family":"Klein","sequence":"first","affiliation":[{"name":"School of Business and Economics Humboldt\u2010Universit\u00e4t zu Berlin Berlin Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7589-2037","authenticated-orcid":false,"given":"Michael Stanley","family":"Smith","sequence":"additional","affiliation":[{"name":"Melbourne Business School University of Melbourne Carlton Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2020,9,2]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1214\/18-BA1108"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1214\/10-BA523"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1111\/1467-9868.00144"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1198\/jasa.2009.tm08302"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2017.04.069"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1993.10476353"},{"key":"e_1_2_9_8_1","first-page":"339","article-title":"Approaches to Bayesian variable selection","volume":"7","author":"George E.","year":"1997","journal-title":"Statistica Sinica"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2007.00587.x"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1214\/13-EJS823"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1080\/10618600.2012.743437"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/cjs.10021"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1214\/17-BA1080"},{"key":"e_1_2_9_14_1","unstructured":"Griffin J. Latuszynski K.andSteel M.(2017) In search of lost (mixing) time: adaptive Markov chain Monte Carlo schemes for Bayesian variable selection with very largep.Arxiv. [Preprint] Available at:http:\/\/arxiv.org\/abs\/1708.05678."},{"key":"e_1_2_9_15_1","first-page":"1351","article-title":"The No\u2010U\u2010turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo","volume":"15","author":"Hoffman M.D.","year":"2014","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmva.2004.06.003"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2012.682536"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1214\/18-BA1138"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.csda.2016.12.009"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2014.881153"},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.1214\/14-BA873"},{"key":"e_1_2_9_22_1","doi-asserted-by":"publisher","DOI":"10.1198\/jasa.2010.tm08177"},{"key":"e_1_2_9_23_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214507000001337"},{"key":"e_1_2_9_24_1","doi-asserted-by":"publisher","DOI":"10.1201\/b10905-6"},{"key":"e_1_2_9_25_1","volume-title":"An Introduction to Copulas","author":"Nelsen R.","year":"2006"},{"key":"e_1_2_9_26_1","doi-asserted-by":"publisher","DOI":"10.1198\/1061860042958"},{"key":"e_1_2_9_27_1","doi-asserted-by":"publisher","DOI":"10.1214\/09-BA403"},{"key":"e_1_2_9_28_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2017.1371025"},{"key":"e_1_2_9_29_1","unstructured":"Sharma R.andDas S.(2018) Regularization and variable selection with copula prior.arXiv[Preprint] Available at:http:\/\/arxiv.org\/abs\/1709.05514v2."},{"key":"e_1_2_9_30_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2017.1330692"},{"key":"e_1_2_9_31_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10827-009-0180-4"},{"key":"e_1_2_9_32_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1053-8119(03)00360-4"},{"key":"e_1_2_9_33_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2014.04.003"},{"key":"e_1_2_9_34_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214506000001031"},{"key":"e_1_2_9_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/0304-4076(95)01763-1"},{"key":"e_1_2_9_36_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0304-4076(00)00018-X"},{"key":"e_1_2_9_37_1","doi-asserted-by":"publisher","DOI":"10.1111\/1467-9469.00191"},{"key":"e_1_2_9_38_1","first-page":"2406","article-title":"Heavy\u2010tailed process priors for selective shrinkage","author":"Wauthier F.L.","year":"2010","journal-title":"Advances in Neural Information Processing Systems 23"},{"key":"e_1_2_9_39_1","first-page":"2460","article-title":"Copula processes","author":"Wilson A.G.","year":"2010","journal-title":"Advances in Neural Information Processing Systems 23"},{"key":"e_1_2_9_40_1","doi-asserted-by":"publisher","DOI":"10.4310\/SII.2013.v6.n2.a9"}],"container-title":["Biometrics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/biom.13355","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1111\/biom.13355","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/biom.13355","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,26]],"date-time":"2024-01-26T13:11:12Z","timestamp":1706274672000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/biometrics\/article\/77\/3\/809-823\/7445028"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,2]]},"references-count":39,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2021,9]]}},"alternative-id":["10.1111\/biom.13355"],"URL":"https:\/\/doi.org\/10.1111\/biom.13355","archive":["Portico"],"relation":{},"ISSN":["0006-341X","1541-0420"],"issn-type":[{"value":"0006-341X","type":"print"},{"value":"1541-0420","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,2]]},"assertion":[{"value":"2019-11-21","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-08-05","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-09-02","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}