Estimation of parameters from a mixture of bivariate copula models
EstMixtureCop(y, reg, family, max_iter, eps)(1 x reg) estimated parameter of the copula according to CRAN copula package (except for Frank copula, where theta = log(theta_R_Package)) for each component (except for degrees of freedom)
estimated degree of freedom, only for the Student copula
(1 x reg) estimated weights vector
(n x reg) conditional probabilities of being in regime k at time t given observations up to time t
estimated Kendall tau for each regime
(n x 2) matrix of Rosenblatt transforms
Cramer-von-Mises statistic for goodness-of-fit
(nx2) data matrix (observations or residuals) that will be transformed to pseudo-observations
number of regimes
'gaussian' , 't' , 'clayton' , 'frank' , 'gumbel'
maximum number of iterations of the EM algorithm
precision (stopping criteria); suggestion 0.0001.
Mamadou Yamar Thioub and Bruno Remillard, April 12, 2018
<doi::10.1002/cjs.11534>