Bicop.mbic

Bicop.mbic(self, u: numpy.ndarray[dtype=float64, shape=(*, *), order='F'] = array([], shape=(0, 2), dtype=float64), psi0: float = 0.9) → float

Evaluates the modified Bayesian information criterion (mBIC).

The mBIC is defined as

$$\mathrm{BIC}=-2\mathrm{loglik}+p\log (n)-2(I\log ({\psi }_0)+(1-I)\log (1-$$

psi_0),

where $\mathrm{loglik}$ is the log-liklihood (see Bicop.loglik()), $p$ is the (effective) number of parameters of the model, and ${\psi }_0$ is the prior probability of having a non-independence copula and $I$ is an indicator for the family being non-independence.

Parameters:

• u – An $n\times (2+k)$ matrix of observations contained in $(0,1)$, where $k$ is the number of discrete variables.

• psi0 – Prior probability of a non-independence copula.

Returns:

The mBIC evaluated at u.