A Python Package for Sampling from Copulae: clayton

The package \textsf{clayton} is designed to be intuitive, user-friendly, and efficient. It offers a wide range of copula models, including Archimedean, Elliptical, and Extreme. The package is implemented in pure \textsf{Python}, making it easy to install and use. In addition, we provide detailed documentation and examples to help users get started quickly. We also conduct a performance comparison with existing \textsf{R} packages, demonstrating the efficiency of our implementation. The \textsf{clayton} package is a valuable tool for researchers and practitioners working with copulas in \textsf{Python}.

• Publication year

  2022

• Venue

  Unknown venue

• Publication date

  2022-03-31

• Fields of study

  Mathematics, Computer Science

• Identifiers
  arXiv 2203.17177
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Non-parametric estimator of a multivariate madogram for missing-data and extreme value framework

  2021influential reference
• Empirical Tail Copulas for Functional Data

  2020cited by this paper
• Array programming with NumPy

  2020cited by this paper
• Linking representations for multivariate extremes via a limit set

  2020cited by this paper
• A geometric investigation into the tail dependence of vine copulas

  2020cited by this paper
• Estimation of Copulas via Maximum Mean Discrepancy

  2020cited by this paper
• A regionalisation approach for rainfall based on extremal dependence

  2019cited by this paper
• Calibration estimation of semiparametric copula models with data missing at random

  2019cited by this paper
• Extreme Value Theory

  2019cited by this paper
• SciPy 1.0: fundamental algorithms for scientific computing in Python

  2019cited by this paper
• Learning Vine Copula Models For Synthetic Data Generation

  2018cited by this paper
• Book review: quantitative risk management: concepts, techniques and tools, revised edition, by A.F. McNeil, R. Frey and P. Embrechts. Princeton University Press, 2015, ISBN 978-0-691-16627-8, xix + 700 pp.

  2017cited by this paper
• Vine copula classifiers for the mind reading problem

  2016cited by this paper
• Multi-task Sparse Structure Learning with Gaussian Copula Models

  2016cited by this paper
• Doubly Robust Inference for the Distribution Function in the Presence of Missing Survey Data

  2016cited by this paper
• Estimation of a Copula when a Covariate Affects only Marginal Distributions

  2015cited by this paper
• Copula Graphical Models for Wind Resource Estimation

  2015cited by this paper
• Spatial clustering of summer temperature maxima from the CNRM-CM5 climate model ensembles & E-OBS over Europe

  2015cited by this paper
• Exact simulation of max-stable processes.

  2015cited by this paper
• On the weak convergence of the empirical conditional copula under a simplifying assumption

  2015cited by this paper
• Multivariate nonparametric estimation of the Pickands dependence function using Bernstein polynomials

  2014cited by this paper
• Gaussian Process Vine Copulas for Multivariate Dependence

  2013cited by this paper
• Clustering of Maxima: Spatial Dependencies among Heavy Rainfall in France

  2013cited by this paper
• A review of copula models for economic time series

  2012cited by this paper
• Likelihood inference for Archimedean copulas in high dimensions under known margins

  2011cited by this paper
• Drought modeling-A review

  2011cited by this paper
• New estimators of the Pickands dependence function and a test for extreme-value dependence

  2011cited by this paper
• Modeling Multivariate Distributions with Continuous Margins Using the copula R Package

  2010cited by this paper
• Multivariate Archimedean copulas, $d$-monotone functions and $\ell_1$-norm symmetric distributions

  2009cited by this paper
• Extreme-value copulas

  2009cited by this paper
• Modelling pairwise dependence of maxima in space

  2009cited by this paper
• Tails of multivariate Archimedean copulas

  2009cited by this paper
• Modélisation et statistique spatiales

  2008influential reference
• Enjoy the Joy of Copulas : With a Package copula

  2007cited by this paper
• RANK-BASED INFERENCE FOR BIVARIATE EXTREME-VALUE COPULAS

  2007cited by this paper
• Asymptotic Normality of Extreme Value Estimators on C[0,1]

  2006cited by this paper
• Quantitative Risk Management: Concepts, Techniques, and Tools

  2005cited by this paper
• The t Copula and Related Copulas

  2005cited by this paper
• Weak Convergence of Empirical Copula Processes

  2004cited by this paper
• Inverse probability weighted estimation for general missing data problems

  2004cited by this paper
• Simulating Multivariate Extreme Value Distributions of Logistic Type

  2003cited by this paper
• An Introduction to Copulas

  2000cited by this paper
• Understanding Relationships Using Copulas

  1998cited by this paper
• Multivariate Models and Multivariate Dependence Concepts

  1997cited by this paper
• A semiparametric estimation procedure of dependence parameters in multivariate families of distributions

  1995cited by this paper
• Families of min-stable multivariate exponential and multivariate extreme value distributions

  1990cited by this paper
• Maxima of normal random vectors: between independence and complete dependence

  1989cited by this paper
• Bivariate extreme value theory: Models and estimation

  1988cited by this paper
• Families of Multivariate Distributions

  1988cited by this paper
• A class of bivariate distri-butions including the bivariate logistic

  1978cited by this paper
• On the simultaneous associativity of F(x, y) and x+y-F(x, y). (Short Communication).

  1978influential reference
• A model for association in bivariate life tables and its application in epidemiological studies of familial tendency in chronic disease incidence

  1978cited by this paper
• Fonctions de repartition a n dimensions et leurs marges

  1959cited by this paper

• No citing papers are available for this paper.