Add to ORKGA family of dimension reduction methods was developed by Cook and Ni [Sufficient dimension reduction via inverse regression: a minimum discrepancy approach. J. Amer. Statist. Assoc. 100, 410-428.] via minimizing a quadratic objective function. Its optimal member called the inverse regression estimator (IRE) was proposed. However, its calculation involves higher order moments of the predictors. In this article, we propose a robust version of the IRE that only uses second moments of the predictor for estimation and inference, leading to better small sample results. (c) 2006 Elsevier B.V. All rights reserved
We propose a general framework for dimension reduction in regression to fill the gap between linear ...
A semiparametric regression model of a q-dimensional multivariate response y on a p-dimensional cova...
International audienceSliced Inverse Regression (SIR) has been extensively used to reduce the dimens...
A family of dimension reduction methods was developed by Cook and Ni [Sufficient dimension reduction...
A family of dimension-reduction methods, the inverse regression (IR) family, is developed by minimiz...
A family of dimension-reduction methods, the inverse regression (IR) family, is developed by minimiz...
Abstract The problem of approximating high-dimensional data with a low-dimensional representa-tion i...
The family of inverse regression estimators that was recently proposed by Cook and Ni has proven eff...
International audienceSliced Inverse Regression (SIR) is an effective method for dimension reduction...
Sliced Inverse Regression (SIR) is a promising technique for the purpose of dimension reduction. Sev...
RT: Linear smoothers for dimension estimation Abstract: Sliced Inverse Regression (Li, 1991) is a si...
Summary. The family of inverse regression estimators recently proposed by Cook and Ni (2005) have pr...
Sliced inverse regression is one of the widely used dimension reduction methods. Chiaromonte and co-...
Sliced Inverse Regression (SIR) is a promising technique for the purpose of dimension reduction. Sev...
Summary. Sliced inverse regression is one of the widely used dimension reduction methods. Chiaromont...
We propose a general framework for dimension reduction in regression to fill the gap between linear ...
A semiparametric regression model of a q-dimensional multivariate response y on a p-dimensional cova...
International audienceSliced Inverse Regression (SIR) has been extensively used to reduce the dimens...
A family of dimension reduction methods was developed by Cook and Ni [Sufficient dimension reduction...
A family of dimension-reduction methods, the inverse regression (IR) family, is developed by minimiz...
A family of dimension-reduction methods, the inverse regression (IR) family, is developed by minimiz...
Abstract The problem of approximating high-dimensional data with a low-dimensional representa-tion i...
The family of inverse regression estimators that was recently proposed by Cook and Ni has proven eff...
International audienceSliced Inverse Regression (SIR) is an effective method for dimension reduction...
Sliced Inverse Regression (SIR) is a promising technique for the purpose of dimension reduction. Sev...
RT: Linear smoothers for dimension estimation Abstract: Sliced Inverse Regression (Li, 1991) is a si...
Summary. The family of inverse regression estimators recently proposed by Cook and Ni (2005) have pr...
Sliced inverse regression is one of the widely used dimension reduction methods. Chiaromonte and co-...
Sliced Inverse Regression (SIR) is a promising technique for the purpose of dimension reduction. Sev...
Summary. Sliced inverse regression is one of the widely used dimension reduction methods. Chiaromont...
We propose a general framework for dimension reduction in regression to fill the gap between linear ...
A semiparametric regression model of a q-dimensional multivariate response y on a p-dimensional cova...
International audienceSliced Inverse Regression (SIR) has been extensively used to reduce the dimens...