Kernel estimators of extreme level curves

By Abdelaati Daouia, Laurent Gardes, Stéphane Girard, Alexandre Lekina. 2011

136 citations recorded by the source corpus.

Subfields: Finance, Global and Planetary Change, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/dee532bb5c1425c40134eed3a327a31f96ec5c49

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Citations in the corpus

Estimation of the conditional tail index using a smoothed local Hill estimator (2014, unknown): “[5] who use a fixed number of non parametric conditional quantile estimators to estimate the conditional tail index.”
Evaluation of classical spatial-analysis schemes of extreme rainfall (2012, background): “…several shorter ones, numerous methods have been elaborated to gather in the same set rainfall series with common statistical properties (Carreau and Girard, 2011; Cunnane, 1988; Daouia et al., 2011; Hosking and Wallis, 1997; Gardes and Girard, 2008; Gardes and Girard, 2010; Neppel et al., 2011).”
Estimation of Extreme Conditional Quantiles Through Power Transformation (2013, methodology): “The bandwidth parameter involved in the KER method is chosen by using the cross-validation method proposed in Daouia et al. (2011).”
On kernel smoothing for extremal quantile regression (2012, unknown): “Daouia et al. [7] have extended the asymptotics further into the tails in the particular setting of a heavy-tailed conditional distribution, while [16, 17] have analyzed the case α n = 1 /n in the particular situation where the response Y given X = x is uniformly distributed.”
Nonparametric regression estimation of conditional tails: the random covariate case (2014, methodology): “This criterion was introduced inYao,[28] implemented by Gannoun et al.[29] and considered in an extreme value context by Daouia et al.[17,30] Using this bandwidth we determine in the second step the number of extremes k with a stability criterion of the estimates for γ (x).”
Functional kernel estimators of large conditional quantiles (2011, methodology): “- The smoothing parameter h is selected using the cross-validation approach introduced in [46] and implemented for instance in [8, 23]: h = argmin ”