statsmodels.distributions.copula.api.StudentTCopula

class statsmodels.distributions.copula.api.StudentTCopula(corr=None, df=None, k_dim=2)

Student t copula.

Parameters:

corrfloat or array_like, optional

Correlation or scatter matrix for the elliptical copula. In the bivariate case, corr can be a scalar and is then considered as the correlation coefficient. If corr is None, then the scatter matrix is the identity matrix.

dffloat, optional

Degrees of freedom of the multivariate t distribution.

k_dimint, optional

Dimension, number of components in the multivariate random variable.

Methods

cdf(u[, args])	Evaluate the cdf of the copula.
corr_from_tau(tau)	Pearson correlation from kendall's tau.
dependence_tail([corr])	Bivariate tail dependence parameter.
fit_corr_param(data)	Copula correlation parameter using Kendall's tau of sample data.
logpdf(u[, args])	Log of copula pdf, loglikelihood.
pdf(u[, args])	Evaluate the pdf of the copula.
plot_pdf([ticks_nbr, ax])	Plot the PDF.
plot_scatter([sample, nobs, rng, ax])	Sample the copula and plot.
rvs([nobs, args, rng])	Generate random variates from the copula.
spearmans_rho([corr])	Bivariate Spearman's rho based on correlation coefficient.
tau([corr])	Bivariate kendall's tau based on correlation coefficient.
tau_simulated([nobs, rng])	Kendall's tau based on simulated samples.

Notes

Elliptical copulas require that copula parameters are set when the instance is created. Those parameters currently cannot be provided in the call to methods. (This will most likely change in future versions.) If non-empty args are provided in methods, then a ValueError is raised. The args keyword is provided for a consistent interface across copulas.

References

[1]

Joe, Harry, 2014, Dependence modeling with copulas. CRC press. p. 181

Methods

cdf(u[, args])	Evaluate the cdf of the copula.
corr_from_tau(tau)	Pearson correlation from kendall's tau.
dependence_tail([corr])	Bivariate tail dependence parameter.
fit_corr_param(data)	Copula correlation parameter using Kendall's tau of sample data.
logpdf(u[, args])	Log of copula pdf, loglikelihood.
pdf(u[, args])	Evaluate the pdf of the copula.
plot_pdf([ticks_nbr, ax])	Plot the PDF.
plot_scatter([sample, nobs, rng, ax])	Sample the copula and plot.
rvs([nobs, args, rng])	Generate random variates from the copula.
spearmans_rho([corr])	Bivariate Spearman's rho based on correlation coefficient.
tau([corr])	Bivariate kendall's tau based on correlation coefficient.
tau_simulated([nobs, rng])	Kendall's tau based on simulated samples.