A new sliced inverse regression method for multivariate response
COUDRET, Raphaël
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
Environnements et Paléoenvironnements OCéaniques [EPOC]
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
Environnements et Paléoenvironnements OCéaniques [EPOC]
SARACCO, Jerome
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
Ecole Nationale Supérieure de Cognitique [ENSC]
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
Ecole Nationale Supérieure de Cognitique [ENSC]
COUDRET, Raphaël
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
Environnements et Paléoenvironnements OCéaniques [EPOC]
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
Environnements et Paléoenvironnements OCéaniques [EPOC]
SARACCO, Jerome
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
Ecole Nationale Supérieure de Cognitique [ENSC]
< Reduce
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
Ecole Nationale Supérieure de Cognitique [ENSC]
Language
en
Article de revue
This item was published in
Computational Statistics and Data Analysis. 2014-09, vol. 77, p. 285-299
Elsevier
English Abstract
A semiparametric regression model of a q-dimensional multivariate response y on a p-dimensional covariate x is considered. A new approach is proposed based on sliced inverse regression (SIR) for estimating the effective ...Read more >
A semiparametric regression model of a q-dimensional multivariate response y on a p-dimensional covariate x is considered. A new approach is proposed based on sliced inverse regression (SIR) for estimating the effective dimension reduction (EDR) space without requiring a prespecified parametric model. The convergence at rate square root of n of the estimated EDR space is shown. The choice of the dimension of the EDR space is discussed. Moreover, a way to cluster components of y related to the same EDR space is provided. Thus, the proposed multivariate SIR method can be used properly on each cluster instead of blindly applying it on all components of y. The numerical performances of multivariate SIR are illustrated on a simulation study. Applications to a remote sensing dataset and to the Minneapolis elementary schools data are also provided. Although the proposed methodology relies on SIR, it opens the door for new regression approaches with a multivariate response.Read less <
Italian Keywords
Semiparametric regression model
Dimension reduction
Sliced inverse regression
Multivariate response
Origin
Hal imported