Estimation of High Conditional Quantiles for Heavy-Tailed Distributions

By H. Wang, Deyuan Li, Xuming He. 2012

158 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/5470fa60586486e682bc7438f8607baee7b1709a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Global and Planetary Change

1. Laurent Gardes
2. Stéphane Girard
3. Gilles Stupfler
4. Armelle Guillou
5. Antoine Schorgen
6. Yuri Goegebeur

In Statistics and Probability

1. Laurent Gardes
2. Stéphane Girard
3. J. E. Methni
4. Gilles Stupfler
5. Armelle Guillou
6. Antoine Schorgen
7. Yuri Goegebeur
8. Abdelaati Daouia

Citations in the corpus

• Non‐parametric Estimation of Extreme Risk Measures from Conditional Heavy‐tailed Distributions (2014, background): “˛njx/ is the extreme conditional quantile of level ˛n 2 .0; 1/ (for instance, Beirlant et al., 2004; Daouia et al., 2011, 2013; Gardes & Girard, 2010, 2012; Wang et al., 2012).”
• Single-Index-Based CoVaR With Very High-Dimensional Covariates (2018, unknown): “For the CQR estimation in case of K > 1, it means that F−1 Y |X(τk) = g(β ∗>X) + c(τk) for all τ1 ≤ τk ≤ τK , where c(τk) is only a constant depending on τk, this is a similar condition as Wang et al. (2012). For K = 1 the assumption E{ψw(ε)|X} = 0 a.”