Conditional extremes from heavy-tailed distributions: an application to the estimation of extreme rainfall return levels

By Laurent Gardes, Stéphane Girard. 2010

116 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/b80889d0cc7ae29279948fc2c6e277642bb2f534

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Citations in the corpus

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).”
Functional kernel estimators of large conditional quantiles (2011, methodology): “Nonparametric methods based on moving windows and nearest neighbors are introduced respectively in [25] and [26].”
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).”
Nonparametric regression estimation of conditional tails: the random covariate case (2014, methodology): “[12] Gardes L, Girard S.”
Estimation of High Conditional Quantiles for Heavy-Tailed Distributions (2012, background): “Gardes and Girard (2010) and Gardes, Girard, and Lekina (2010) proposed a local estimation method, where the estimation of an extreme quantile of Y given X = x∗ is based on observations in a small neighborhood of x∗.”
Kernel estimators of extreme level curves (2011, result): “We refer to [19, 20, 21] for a similar model in the fixed-design setting.”
On kernel smoothing for extremal quantile regression (2012, unknown): “Motivating examples include the study of extreme rainfall as a function of the geographical location [15], the estimation of factors of high risk in finance [32], the assessment of the”
Estimation of the conditional tail index using a smoothed local Hill estimator (2014, methodology): “Fully non parametric methods have been considered using splines (see Chavez-Demoulin and Davison [4]), local polynomials (see Davison and Ramesh [6]), a moving window approach (see Gardes and Girard [8]), or a nearest neighbor approach (see Gardes and Girard [9]), among others.”