{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T05:05:24Z","timestamp":1791263124351,"version":"4.1.0"},"reference-count":0,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2001,7,1]],"date-time":"2001-07-01T00:00:00Z","timestamp":993945600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2001,7,1]]},"abstract":"<jats:title>Summary<\/jats:title>\n               <jats:p>A new estimation method for the dimension of a regression at the outset of an analysis is proposed. A linear subspace spanned by projections of the regressor vector X, which contains part or all of the modelling information for the regression of a vector Y on X, and its dimension are estimated via the means of parametric inverse regression. Smooth parametric curves are fitted to the p inverse regressions via a multivariate linear model. No restrictions are placed on the distribution of the regressors. The estimate of the dimension of the regression is based on optimal estimation procedures. A simulation study shows the method to be more powerful than sliced inverse regression in some situations.<\/jats:p>","DOI":"10.1111\/1467-9868.00292","type":"journal-article","created":{"date-parts":[[2003,3,12]],"date-time":"2003-03-12T19:36:38Z","timestamp":1047497798000},"page":"393-410","source":"Crossref","is-referenced-by-count":100,"title":["Estimating the Structural Dimension of Regressions Via Parametric Inverse Regression"],"prefix":"10.1093","volume":"63","author":[{"given":"Efstathia","family":"Bura","sequence":"first","affiliation":[{"name":"George Washington University , Washington DC , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"R. Dennis","family":"Cook","sequence":"additional","affiliation":[{"name":"University of Minnesota , St Paul , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2002,1,6]]},"container-title":["Journal of the Royal Statistical Society Series B: Statistical Methodology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.wiley.com\/onlinelibrary\/tdm\/v1\/articles\/10.1111%2F1467-9868.00292","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.wiley.com\/onlinelibrary\/tdm\/v1\/articles\/10.1111%2F1467-9868.00292","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/academic.oup.com\/jrsssb\/article-pdf\/63\/2\/393\/49590406\/jrsssb_63_2_393.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/jrsssb\/article-pdf\/63\/2\/393\/49590406\/jrsssb_63_2_393.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,22]],"date-time":"2023-03-22T09:43:32Z","timestamp":1679478212000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/jrsssb\/article\/63\/2\/393\/7083346"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2001,7,1]]},"references-count":0,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2002,1,6]]},"published-print":{"date-parts":[[2001,7,1]]}},"URL":"https:\/\/doi.org\/10.1111\/1467-9868.00292","relation":{},"ISSN":["1369-7412","1467-9868"],"issn-type":[{"value":"1369-7412","type":"print"},{"value":"1467-9868","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2001,7]]},"published":{"date-parts":[[2001,7,1]]}}}