Generalized additive modelling of sample extremes

By V. Chavez‐Demoulin, A. C. Davison. 2005

345 citations recorded by the source corpus.

Subfields: Finance, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/5929da69786c312ae8401b1f733601404af8b690

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Citations in the corpus

A moving window approach for nonparametric estimation of the conditional tail index (2008, methodology): “In a similar spirit, spline estimators are fitted in [9] through a penalized maximum likelihood method.”
Nonparametric regression estimation of conditional tails: the random covariate case (2014, methodology): “Fully nonparametric methods like local estimation and penalized likelihood estimation can be found in Davison and Ramesh,[7] Hall and Tajvidi,[8] Pauli and Coles,[9] Beirlant and Goegebeur,[10] Chavez-Demoulin and Davison,[11] Gardes and Girard [12,13] and Goegebeur and de Wet.”
Stationary and nonstationary generalized extreme value modelling of extreme precipitation over a mountainous area under climate change (2014, neutral): “Apart from the use of nonpolynomial functions of time, the possibilities include fitting fractional polynomials (Royston and Altman, 1994) or a nonparametric approach (Chavez-Demoulin and Davison, 2005).”
Quantitative models for operational risk: Extremes, dependence and aggregation (2006, methodology): “Chavez-Demoulin (1999), Chavez-Demoulin and Davison (2005) contain the relevant methodology.”
Conditional extremes from heavy-tailed distributions: an application to the estimation of extreme rainfall return levels (2010, methodology): “estimators are fitted in (Chavez-Demoulin and Davison 2005) through a penalized maximum likelihood method.”
Kernel estimators of extreme level curves (2011, methodology): “Similarly, spline estimators are fitted in [8] through a penalized maximum likelihood method.”
Likelihood-Based Inference for Max-Stable Processes (2009, methodology): “Non-parametric modelling of univariate extreme value responses has been recently proposed by Chavez-Demoulin and Davison (2005) , Yee and Stephenson (2007) and Padoan and Wand (2008).”
Inference for clustered data using the independence loglikelihood (2007, methodology): “…is currently a substantial amount of research into the analysis of dependent extremes (e.g. Heffernan and Tawn 2004 and references therein; also Chavez-Demoulin and Davison 2005, who adopt a bootstrap-based approach to inference in such situations), but also because it provides an opportunity…”
Geostatistics of extremes (2012, methodology): “A (2012) Pauli & Coles (2001), Chavez-Demoulin & Davison (2005) and Padoan & Wand (2008) describe approaches to more flexible modelling, applied by Ramesh & Davison (2002) and Butler et al. (2007).”
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.”
Functional kernel estimators of large conditional quantiles (2011, neutral): “Fully nonparametric estimators have been first introduced in [9, 6] through local polynomial and spline models.”
Non-Stationary Dependence Structures for Spatial Extremes (2014, background): “…to construct non-stationary models for marginal distributions, e.g., by letting the underlying parameters depend on covariates or splines (Chavez-Demoulin and Davison, 2005; Cooley et al., 2007; Northrop and Jonathan, 2011; Davison and Gholamrezaee, 2012), it is more difficult to model…”
Generalized Additive Models for Exceedances of High Thresholds With an Application to Return Level Estimation for U.S. Wind Gusts (2019, background): “Pauli and Coles (2001) proposed smooth models for extreme value distribution parameters based on penalized likelihood, while Chavez-Demoulin and Davison (2005) proposed a generalized additive model (GAM) framework for such parameters that could accommodate parametric and smooth terms.”