Bicop.aic

Bicop.aic(self, u: numpy.ndarray = 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 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:

undarray, shape (n, m), dtype float

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

Returns:

float

The AIC evaluated at u.