statsmodels.distributions.copula.api.ExtremeValueCopula

class statsmodels.distributions.copula.api.ExtremeValueCopula(transform, args=(), k_dim=2)[source]

Extreme value copula constructed from Pickand’s dependence function.

Currently only bivariate copulas are available.

Parameters:
transform: instance of transformation class

Pickand’s dependence function with required methods including first and second derivatives

argstuple

Optional copula parameters. Copula parameters can be either provided when creating the instance or as arguments when calling methods.

k_dimint

Currently only bivariate extreme value copulas are supported.

See also

dep_func_ev

Notes

currently the following dependence function and copulas are available

  • AsymLogistic

  • AsymNegLogistic

  • AsymMixed

  • HR

TEV and AsymBiLogistic currently do not have required derivatives for pdf.

Methods

cdf(u[, args])

Evaluate cdf of bivariate extreme value copula.

conditional_2g1(u[, args])

conditional distribution

fit_corr_param(data)

Copula correlation parameter using Kendall's tau of sample data.

logpdf(u[, args])

Evaluate log-pdf of bivariate extreme value copula.

pdf(u[, args])

Evaluate pdf of bivariate extreme value copula.

plot_pdf([ticks_nbr, ax])

Plot the PDF.

plot_scatter([sample, nobs, random_state, ax])

Sample the copula and plot.

rvs([nobs, args, random_state])

Draw n in the half-open interval [0, 1).

tau_simulated([nobs, random_state])

Kendall's tau based on simulated samples.
Last update: Nov 14, 2024