Estimating sufficient reductions of the predictors in abundant high-dimensional regressions

dc.creatorCook, R. Dennis
dc.creatorForzani, Liliana Maria
dc.creatorRothman, Adam
dc.date.accessioned2018-09-20T18:31:11Z
dc.date.accessioned2018-11-06T11:25:47Z
dc.date.available2018-09-20T18:31:11Z
dc.date.available2018-11-06T11:25:47Z
dc.date.created2018-09-20T18:31:11Z
dc.date.issued2012-02
dc.identifierCook, R. Dennis; Forzani, Liliana Maria; Rothman, Adam; Estimating sufficient reductions of the predictors in abundant high-dimensional regressions; Institute of Mathematical Statistics; Annals Of Statistics, The; 40; 1; 2-2012; 353-384
dc.identifier0090-5364
dc.identifierhttp://hdl.handle.net/11336/60500
dc.identifierCONICET Digital
dc.identifierCONICET
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1851477
dc.description.abstractWe study the asymptotic behavior of a class of methods for sufficient dimension reduction in high-dimension regressions, as the sample size and number of predictors grow in various alignments. It is demonstrated that these methods are consistent in a variety of settings, particularly in abundant regressions where most predictors contribute some information on the response, and oracle rates are possible. Simulation results are presented to support the theoretical conclusion.
dc.languageeng
dc.publisherInstitute of Mathematical Statistics
dc.relationinfo:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1214/11-AOS962
dc.rightshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectCENTRAL SUBSPACE
dc.subjectORACLE PROPERTY
dc.subjectPRINCIPAL FITTED COMPONENTS
dc.subjectSPARSITY
dc.subjectSPICE
dc.subjectSUFFICIENT DIMENSION REDUCTION
dc.titleEstimating sufficient reductions of the predictors in abundant high-dimensional regressions
dc.typeArtículos de revistas
dc.typeArtículos de revistas
dc.typeArtículos de revistas


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