Multivariate functions encountered in high-dimensional uncertainty quantification problems
often vary most strongly along a few dominant directions in the input parameter space. We …

We review sufficient dimension reduction (SDR) estimators with multivariate response in this
paper. A wide range of SDR methods are characterized as inverse regression SDR …

Pooled marginal slicing (PMS) is a semiparametric method, based on sliced inverse
regression (SIR) approach, for achieving dimension reduction in regression problems when …

In this article, we focus on data arriving sequentially by blocks in a stream. A semiparametric
regression model involving a common effective dimension reduction (EDR) direction β β is …

Since its introduction in the early 90s, the Sliced Inverse Regression (SIR) methodology has
evolved adapting to increasingly complex data sets in contexts combining linear dimension …

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 …

Abstract Sliced Inverse Regression (SIR) has been extensively used to reduce the
dimension of the predictor space before performing regression. SIR is originally a model free …

Cet ouvrage, consacré aux approches non paramétriques et semi-paramétriques en
régression, propose au lecteur une exploration, une synthèse et une analyse des …

To reduce the dimensionality of regression problems, sliced inverse regression approaches
make it possible to determine linear combinations of a set of explanatory variables X related …

Nuclear steam generators are subject to clogging of their internal parts which causes safety
issues. Diagnosis methodologies are needed to optimize maintenance operations. Clogging …