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dc.contributor.author
Cook, R. Dennis  
dc.contributor.author
Forzani, Liliana Maria  
dc.contributor.author
Rothman, Adam  
dc.date.available
2018-09-20T18:31:11Z  
dc.date.issued
2012-02  
dc.identifier.citation
Cook, 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.identifier.issn
0090-5364  
dc.identifier.uri
http://hdl.handle.net/11336/60500  
dc.description.abstract
We 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.format
application/pdf  
dc.language.iso
eng  
dc.publisher
Institute of Mathematical Statistics  
dc.rights
info:eu-repo/semantics/openAccess  
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/  
dc.subject
Central Subspace  
dc.subject
Oracle Property  
dc.subject
Principal Fitted Components  
dc.subject
Sparsity  
dc.subject
Spice  
dc.subject
Sufficient Dimension Reduction  
dc.subject.classification
Matemática Pura  
dc.subject.classification
Matemáticas  
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS  
dc.title
Estimating sufficient reductions of the predictors in abundant high-dimensional regressions  
dc.type
info:eu-repo/semantics/article  
dc.type
info:ar-repo/semantics/artículo  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.date.updated
2018-09-18T16:22:29Z  
dc.journal.volume
40  
dc.journal.number
1  
dc.journal.pagination
353-384  
dc.journal.pais
Estados Unidos  
dc.description.fil
Fil: Cook, R. Dennis. University of Minnesota; Estados Unidos  
dc.description.fil
Fil: Forzani, Liliana Maria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; Argentina  
dc.description.fil
Fil: Rothman, Adam. University of Minnesota; Estados Unidos  
dc.journal.title
Annals Of Statistics, The  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1214/11-AOS962  

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