Inference for clusters of extreme values

By C. Ferro, Johan Segers. 2003

445 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/eaabef5260b92ac34f77b11ecf1ebc0831460eb9

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Citations in the corpus

Semi-parametric and Parametric Inference of Extreme Value Models for Rainfall Data (2010, methodology): “The bootstrap has been frequently used in practical applications for determining the accuracy (or uncertainty) of statistical analyses (Dunn 2001; Kharin and Zwiers 2000; Dixon 2002; Khaliq et al. 2005; Ferro and Segers 2003; Zwiers 1990; Zhang et al. 2005; Wilks 1997; Zwiers and Ross 1991).”
Modelling Time Series Extremes (2012, methodology): “In subsequent work Ferro & Segers (2003) proposed the intervals estimator, based on a limiting characterization of the rescaled inter-exceedance intervals: with probability θ an arbitrary exceedance is the last of a cluster, and then the time to the next exceedance has an exponential distribution…”
A Hierarchical Model for Serially-Dependent Extremes: A Study of Heat Waves in the Western US (2014, background): “Second, complications arising from the need to identify clusters (Ferro and Segers 2003, e.g.) are completely eliminated.”
An efficient semiparametric maxima estimator of the extremal index (2015, methodology): “Recent advances in the estimation of θ (Ferro and Segers, 2003; Süveges, 2007; Süveges and Davison, 2010; Laurini and Tawn, 2003; Robert, 2013) have concentrated on threshold methods, based on exceedences of a threshold. The improvement of maxima methods (Gomes, 1993; Ancona-Navarrete and Tawn, 2000”
Inference for the limiting cluster size distribution of extreme values (2008, methodology): “In order to eliminate the cluster identification scheme sequences, [16] (see also [15]) proposes estimators which are based on the sequence of the thresholds urn(τ ) and on inter-exceedance times: a least-squares estimator, a maximum-likelihood estimator and a moment estimator.”
Generalized Additive Models for Exceedances of High Thresholds With an Application to Return Level Estimation for U.S. Wind Gusts (2019, background): “5), Ferro and Segers (2003), and Eastoe and Tawn (2012).”
Likelihood estimation of the extremal index (2007, methodology): “The validity of these estimators can be extended to dependent processes satisfying the long-range approximate independence condition of Ferro and Segers (2003) and the condition D(2)(un) of Leadbetter and Nandagopalan (1989) and Chernick et al. (1991).”
Estimating value-at-risk: a point process approach (2005, methodology): “The inter-arrival times between successive exceedances can also be used to compute an estimator of a parameter known as the extremal index (Ferro and Segers 2003) which has an interpretation as the inverse of the limiting cluster size and takes the value 1 for independent series.”
A Horse Race between the Block Maxima Method and the Peak–over–Threshold Approach (2018, background): “(2008); Genest and Segers (2009); Gudendorf and Segers (2012) for nonparametric estimators and Genest et al. (1995); Dombry et al. (2016) for parametric ones, among many others.”
Estimating return levels from serially dependent extremes (2012, methodology): “Instead, we extend the bootstrap procedure described by Ferro and Segers (2003) and implemented for the extremal index estimator O Œ5 in Sections 4.1 and 4.2.”