Dimension reduction for the estimation of the conditional tail-index
Résumé
We are interested in the relationship between the large values of a real random variable Y and its p-dimensional associated covariate X when the conditional distribution of Y given X=x is heavy-tailed with positive tail index. Estimating this index is a crucial step for the inference of the conditional distribution, but this task becomes more challenging as the dimension of the covariate increases. The objective of this work is to propose a dimension reduction method to obtain a more efficient estimator of the extreme value index. Specifically, we assume the existence of a linear subspace of dimension q
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https://hal.science/hal-04589742
Soumis le : lundi 27 mai 2024-17:49:51
Dernière modification le : jeudi 30 mai 2024-03:19:36
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- HAL Id : hal-04589742 , version 1
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Laurent Gardes, Alex Podgorny. Dimension reduction for the estimation of the conditional tail-index. 2024. ⟨hal-04589742⟩
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