Importance of Tail Dependence in Bivariate Frequency Analysis

By Annie Poulin, David Huard, Anne-Catherine Favre, S. Pugin. 2007

185 citations recorded by the source corpus.

Subfields: Finance, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/6221b17b17d0d44177ffe044e781df7716df8a3d

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Citations in the corpus

Bivariate Flood Frequency Analysis with Historical Information Based on Copula (2013, methodology): “The upper tail dependence coefficients (TDC) of GH copula (λU ¼ 2 − 21=θ) and student’s t copula [λU ¼ 2tνþ1ð− ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiðν þ 1Þð1 − θÞ=ð1þ θÞÞp ] are computed by the es”
Maximum entropy-Gumbel-Hougaard copula method for simulation of monthly streamflow in Xiangxi river, China (2015, methodology): “(9), the parametric upper tail dependence coefficient kGHU (Frahm et al. 2005; Poulin et al. 2007) may be estimated by kGHU ¼ 2 2 1 h ð17Þ where h is the parameter of GHC estimated by Eq.”
A copula-based joint deficit index for droughts. (2010, background): “…runoff (Kao and Govindaraju, 2007b), multivariate L-moment homogeneity test (Chebana and Ouarda, 2007), remote sensing data (Gebremichael and Krajewski, 2007), tail dependence (Poulin et al., 2007), rainfall IDF curves (Singh and Zhang, 2007), and sea storm analysis (De Michele et al., 2007).”
Modeling the spatial dependence of floods using the Fisher copula (2019, background): “An empirical correlogram was set up which relates the entries of the matrix corresponding to pairs of stations to the distance between these locations (Fig.”
A comparative copula‐based bivariate frequency analysis of observed and simulated storm events: A case study on Bartlett‐Lewis modeled rainfall (2011, background): “…obvious that when the copula is not fitted well in the upper right tail, the results of the frequency analysis will be unreliable: It is therefore of major importance to properly model the dependence of extremes, or tail dependence [Salvadori et al., 2007; Poulin et al., 2007; Frahm et al., 2005].”
Frequency analysis of droughts using the Plackett copula and parameter estimation by genetic algorithm (2010, background): “Poulin et al. (2007) highlighted the importance of tail dependence in bivariate frequency analysis.”
Bivariate drought frequency analysis using the copula method (2012, methodology): “Poulin et al. (2007) showed the importance of taking into account the tail dependence in bivariate frequency analysis (based on copulas) for risk estimations.”
Probabilistic characterization of drought properties through copulas (2009, background): “Joe, 1997), which describes the probability to observe a high value for a variable given that the other variable assumed a high value (Cherubini et al., 2004; Poulin et al., 2007).”
Trivariate Frequency Analyses of Peak Discharge, Hydrograph Volume and Suspended Sediment Concentration Data Using Copulas (2014, methodology): “This copula was selected also due to the fact that Frank and Clayton copula can underestimate the risk of an event because they cannot model the tail dependence efficiently (Poulin et al. 2007).”
Bivariate frequency analysis of nonstationary low‐flow series based on the time‐varying copula (2015, background): “The tail dependence is very important in estimating the risk of the multivariate hydrological extremes (Poulin et al., 2007).”
Meta-elliptical copulas for drought frequency analysis of periodic hydrologic data (2010, background): “Kotz and Nadarajah (2001) and Nadarajah and Kotz (2005) derived a representation of the pdf and the cdf of the bivariate symmetric Kotz type distribution in terms of hypergeometric functions.”
A multivariate copula‐based framework for dealing with hazard scenarios and failure probabilities (2016, background): “…Position p-Value Q (m3/s) 0.3678 36.21 59.36 77% (0.11) (5.04) (5.71) V (106 m3) 0.6146 1.5242 1.7229 91% (0.13) (0.25) (0.24) aThe values in parentheses indicate estimated standard errors; also shown are Goodness-of-Fit test approximate p-Values (see the ‘‘R’’ package evir [Pfaff et al., 2011].”
Identification of suitable copulas for bivariate frequency analysis of flood peak and flood volume data (2011, methodology): “Poulin et al. (2007) emphasized this need by considering seven copulas from three copula families – Archimedean, extreme value and meta-elliptical – while selecting copulas for joint distribution of flood peak flow and volume.”
Modelling bivariate rainfall distribution and generating bivariate correlated rainfall data in neighbouring meteorological subdivisions using copula (2010, background): “If F1, . . . , Fp are continuous then the copula C is unique and has the representation (Poulin et al., 2007): C u1, . . . , up D F F 11 u1 , . . . , F 1p up , 0 u1, . . . , up 1 2 Copyright 2010 John Wiley & Sons, Ltd. where the quantile function F 1i is defined by F 1 i ui D inffx 2 jFi x ½ uig.”