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

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

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.