Extreme partial least-squares
| Autor: | Meryem Bousebata, Geoffroy Enjolras, Stéphane Girard |
|---|---|
| Přispěvatelé: | Girard, Stephane, IDEX UGA - - UGA2015 - ANR-15-IDEX-0002 - IDEX - VALID, Modèles statistiques bayésiens et des valeurs extrêmes pour données structurées et de grande dimension (STATIFY), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Centre d'études et de recherches appliquées à la gestion (CERAG), Chaire Stress Test - BNP Paribas/Ecole polytechnique/Fondation de l'X., ANR-15-IDEX-0002,UGA,IDEX UGA(2015) |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Statistics and Probability
Numerical Analysis [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] Dimension reduction Non-linear inverse regression Statistics Probability and Uncertainty Extreme-value analysis Partial Least Squares [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST] ComputingMilieux_MISCELLANEOUS |
| Zdroj: | EcoSta 2022-5th International Conference on Econometrics and Statistics EcoSta 2022-5th International Conference on Econometrics and Statistics, Jun 2022, Kyoto, Japan Journal of Multivariate Analysis Journal of Multivariate Analysis, 2023, 194, pp.105101. ⟨10.1016/j.jmva.2022.105101⟩ |
| ISSN: | 0047-259X 1095-7243 |
| Popis: | International audience; We propose a new approach, called Extreme-PLS, for dimension reduction in conditional extreme values settings. The objective is to find linear combinations of covariates 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect