statsmodels.distributions.copula.api.FrankCopula¶

class statsmodels.distributions.copula.api.FrankCopula(theta=None, k_dim=2)[source]¶

Frank copula.

Dependence is symmetric.

$$C_{\theta }(\mathbf{u})=-\frac{1}{\theta }\log [1-\frac{\prod _j(1-\exp (-\theta u_j))}{(1-\exp (-\theta )-1{)}^{d-1}}]$$

with $\theta \in \mathbb{R}\mathrm{\setminus }\{0\},\mathbf{u}\in [0,1{]}^d$.

Methods

cdf(u[, args])	Evaluate cdf of Archimedean copula.
cdfcond_2g1(u[, args])	Conditional cdf of second component given the value of first.
fit_corr_param(data)	Copula correlation parameter using Kendall's tau of sample data.
logpdf(u[, args])	Evaluate log pdf of multivariate Archimedean copula.
pdf(u[, args])	Evaluate pdf of Archimedean copula.
plot_pdf([ticks_nbr, ax])	Plot the PDF.
plot_scatter([sample, nobs, random_state, ax])	Sample the copula and plot.
ppfcond_2g1(q, u1[, args])	Conditional pdf of second component given the value of first.
rvs([nobs, args, random_state])	Draw n in the half-open interval [0, 1).
tau([theta])
tau_simulated([nobs, random_state])	Kendall's tau based on simulated samples.
theta_from_tau(tau)