An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Computational Methods for Inverse Problems
Computational Methods for Inverse Problems
Asymptotics for pooled marginal slicing estimator based on SIRα approach
Journal of Multivariate Analysis
Dimension reduction in functional regression with applications
Computational Statistics & Data Analysis
Functional nonparametric estimation of conditional extreme quantiles
Journal of Multivariate Analysis
Dimension reduction for model-based clustering
Statistics and Computing
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Sliced Inverse Regression (SIR) is an effective method for dimension reduction in high-dimensional regression problems. The original method, however, requires the inversion of the predictors covariance matrix. In case of collinearity between these predictors or small sample sizes compared to the dimension, the inversion is not possible and a regularization technique has to be used. Our approach is based on a Fisher Lecture given by R.D. Cook where it is shown that SIR axes can be interpreted as solutions of an inverse regression problem. We propose to introduce a Gaussian prior distribution on the unknown parameters of the inverse regression problem in order to regularize their estimation. We show that some existing SIR regularizations can enter our framework, which permits a global understanding of these methods. Three new priors are proposed leading to new regularizations of the SIR method. A comparison on simulated data as well as an application to the estimation of Mars surface physical properties from hyperspectral images are provided.