Bicop.aic

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

Evaluates the Akaike information criterion (AIC).

The AIC is defined as

\[\mathrm{AIC} = -2\, \mathrm{loglik} + 2 p,\]

where \(\mathrm{loglik}\) is the log-liklihood (see Bicop.loglik()) and \(p\) is the (effective) number of parameters of the model. The AIC is a consistent model selection criterion even for nonparametric models.

Parameters:

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

Returns:

The AIC evaluated at u.