Analysis of inter-gauge dependence by Kendall’s τK, upper tail dependence coefficient, and 2-copulas with application to rainfall fields

By F. Serinaldi. 2008

93 citations recorded by the source corpus.

Subfields: Finance, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/dcd8c9f34db2ee3408ef0ec4a0198fb1ab1113e0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Finance

In Global and Planetary Change

Citations in the corpus

Application of copulas for derivation of drought severity–duration–frequency curves (2012, methodology): “Serinaldi (2008) studied inter-gauge dependence of precipitation using non-parametric Kendall’s rank correlation and upper tail dependence coefficient.”
Bivariate drought frequency analysis using the copula method (2012, background): “(2005) have shown that this assumption is not strong and that the estimator performs well even when the copula does not belong to the extreme value class (Serinaldi 2008).”
Identification of suitable copulas for bivariate frequency analysis of flood peak and flood volume data (2011, methodology): “Although, quantitative estimates of tail dependence are not obtained in this study, such estimators are available in literature and have been employed by Schmidt & Stadtmüller (2006), Poulin et al. (2007), Serinaldi (2008, 2009), and Villarini et al. (2008).”
Copula-based statistical refinement of precipitation in RCM simulations over complex terrain (2011, methodology): “Serinaldi (2008) studied the dependence of rain gauge data using the non-parametric Kendall’s rank correlation and the upper tail dependence coefficient (TDC).”
Copula based multisite model for daily precipitation simulation (2009, result): “In contrast to the work of (Serinaldi, 2008) who concentrated on the upper-tail dependence structure of 2-copulas, we are concerned about the joint interdependence of clusters of rainfall stations.”
Modelling bivariate rainfall distribution and generating bivariate correlated rainfall data in neighbouring meteorological subdivisions using copula (2010, background): “(2005) have shown that this assumption is not strong and the estimator performs well even, when the copula does not belong to EV class (Serinaldi, 2008).”
Copula-based assimilation of radar and gauge information to derive bias-corrected precipitation fields (2012, methodology): “Serinaldi (2008) studied the dependence of rain gauge data using the non parametric Kendall’s rank correlation and the upper tail dependence coefficient (TDC).”
Upper tail dependence in rainfall extremes: would we know it if we saw it? (2015, methodology): “15 of Serinaldi (2008) and confirm the need for a thorough re-assessment of those analyses (which is done in the next sections) as well as all the studies whose conclusions are based on these estimators.”