Skip to Main content Skip to Navigation
Journal articles

Dimension reduction in multivariate extreme value analysis

Abstract : Non-parametric assessment of extreme dependence structures between an arbitrary number of variables, though quite well-established in dimension 2 and recently extended to moderate dimensions such as 5, still represents a statistical challenge in larger dimensions. Here, we propose a novel approach that combines clustering techniques with angular/spectral measure analysis to find groups of variables (not necessarily disjoint) exhibiting asymptotic dependence, thereby reducing the dimension of the initial problem. A heuristic criterion is proposed to choose the threshold over which it is acceptable to consider observations as extreme and the appropriate number of clusters. When empirically evaluated through numerical experiments, the approach we promote here is found to be very efficient under some regularity constraints, even in dimension 20. For illustration purpose, we also carry out a case study in dietary risk assessment.
Document type :
Journal articles
Complete list of metadata

Cited literature [31 references]  Display  Hide  Download
Contributor : Bibliothèque MINES ParisTech Connect in order to contact the contributor
Submitted on : Thursday, January 14, 2016 - 11:32:05 AM
Last modification on : Wednesday, November 17, 2021 - 12:31:24 PM
Long-term archiving on: : Saturday, April 16, 2016 - 10:24:17 AM


Publisher files allowed on an open archive


Distributed under a Creative Commons Attribution 4.0 International License



Emilie Chautru. Dimension reduction in multivariate extreme value analysis. Electronic Journal of Statistics , Shaker Heights, OH : Institute of Mathematical Statistics, 2015, 9 (1), pp.383-418. ⟨10.1214/15-EJS1002⟩. ⟨hal-01256008⟩



Record views


Files downloads