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Extreme Partial Least-Squares regression

Abstract : We propose a new approach, called Extreme-PLS, for dimension reduction in regression and adapted to distribution tails. The objective is to find linear combinations of predictors that best explain the extreme values of the response variable in a non-linear inverse regression model. The asymptotic normality of the Extreme-PLS estimator is established in the single-index framework and under mild assumptions. The performance of the method is assessed on simulated data. A statistical analysis of French farm income data, considering extreme cereal yields, is provided as an illustration.
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https://hal.inria.fr/hal-03165399
Contributor : Stephane Girard <>
Submitted on : Wednesday, March 10, 2021 - 4:09:10 PM
Last modification on : Tuesday, April 13, 2021 - 10:31:40 AM

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  • HAL Id : hal-03165399, version 1

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Meryem Bousebata, Geoffroy Enjolras, Stéphane Girard. Extreme Partial Least-Squares regression. 2021. ⟨hal-03165399⟩

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