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

args : tuple¶

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

k_dim : int¶

Currently only bivariate extreme value copulas are supported.

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.

See also

dep_func_ev

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.