Results 31 to 40 of about 41,008 (203)

Compounding joint impact of rainfall, storm surge and river discharge on coastal flood risk: an approach based on 3D fully nested Archimedean copulas

open access: yesEnvironmental Earth Sciences, 2023
Compound flooding is a multidimensional consequence of the joint impact of multiple intercorrelated drivers, such as oceanographic, hydrologic, and meteorological. These individual drivers exhibit interdependence due to common forcing mechanisms. If they
Shahid Latif, S. Simonovic
semanticscholar   +1 more source

Convergence of Archimedean copulas [PDF]

open access: yesStatistics & Probability Letters, 2006
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Charpentier, Arthur, Segers, Johan
openaire   +5 more sources

On convergence of associative copulas and related results

open access: yesDependence Modeling, 2021
Triggered by a recent article establishing the surprising result that within the class of bivariate Archimedean copulas ๐’žar different notions of convergence - standard uniform convergence, convergence with respect to the metric D1, and so-called weak ...
Kasper Thimo M.   +2 more
doaj   +1 more source

A New Bivariate Family Based on Archimedean Copulas: Simulation, Regression Model and Application

open access: yesSymmetry, 2023
We use the Clayton and Frank copulas and the exponentiated odd log-logistic family to define a new flexible bivariate model to fit bimodal and asymmetry data.
Gabriela M. Rodrigues   +3 more
semanticscholar   +1 more source

Deep Archimedean Copulas

open access: yesCoRR, 2020
A central problem in machine learning and statistics is to model joint densities of random variables from data. Copulas are joint cumulative distribution functions with uniform marginal distributions and are used to capture interdependencies in isolation from marginals.
Chun Kai Ling   +2 more
openaire   +3 more sources

A Mixture of Clayton, Gumbel, and Frank Copulas: A Complete Dependence Model

open access: yesJournal of Probability and Statistics, 2022
Knowledge of the dependence between random variables is necessary in the area of risk assessment and evaluation. Some of the existing Archimedean copulas, namely the Clayton and the Gumbel copulas, allow for higher correlations on the extreme left and ...
M. A. Boateng   +3 more
doaj   +1 more source

EVALUATION OF ERA5 AND IMERG PRECIPITATION DATA FOR RISK ASSESSMENT OF WATER CYCLE VARIABLES OF A LARGE RIVER BASIN IN SOUTH ASIA USING SATELLITE DATA AND ARCHIMEDEAN COPULAS

open access: yesWater Conservation & Management, 2022
Precipitation as a major water cycle variable influences the occurrences and distribution of terrestrial water storage change (TWSC), evapotranspiration (ET), and river discharge (Q) of a large river basin.
S. Barma   +4 more
semanticscholar   +1 more source

Properties of hierarchical Archimedean copulas [PDF]

open access: yesStatistics & Risk Modeling, 2013
Abstract In this paper we analyse the properties of hierarchical Archimedean copulas. This class is a generalisation of the Archimedean copulas and allows for general non-exchangeable dependency structures. We show that the structure of the copula can be uniquely recovered from all bivariate margins.
Ostap Okhrin   +2 more
openaire   +4 more sources

Archimedean copulae and positive dependence [PDF]

open access: yesJournal of Multivariate Analysis, 2005
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
MUELLER A, SCARSINI, MARCO
openaire   +4 more sources

Convexity of linear joint chance constrained optimization with elliptically distributed dependent rows

open access: yesResults in Control and Optimization, 2023
In this paper, we study the convexity of the linear joint chance constraints. We assume that the constraint row vectors are elliptically distributed. Further, the dependence of the rows is modeled by a family of Archimedean copulas, namely, the Gumbel ...
Hoang Nam Nguyen, Abdel Lisser, Jia Liu
doaj   +1 more source

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