Vinecop.aic

Vinecop.aic(self: pyvinecopulib.Vinecop, u: numpy.ndarray[numpy.float64[m, n]] = 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 Vinecop.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 \(n \times (d + k)\) or \(n \times 2d\) matrix of evaluation points, where \(k\) is the number of discrete variables (see select() or Vinecop.pdf()).

  • num_threads – 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:

The AIC as a double.