Vinecop.aic

Vinecop.aic(self, u: numpy.ndarray = array([], shape=(0, 0), dtype=float64), num_threads: int = 1) → 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 \(n \times (d + k)\)or \(n \times 2d\)matrix of evaluation points, where \(k\)is the number of discrete variables (see select() or pdf()).

num_threadsint

The number of threads to use for computations; if greater than 1, the function will be applied concurrently to num_threads batches of u.

Returns:

float

The AIC as a double.