estimate the corrected Akaike Information Criterion

        relies on the estimated degrees of freedom, which must be computed
        before.

        Parameters
        ----------
        y : array-like of shape (n_samples,)
            output data vector
        mu : array-like of shape (n_samples,)
            expected value of the targets given the model and inputs
        weights : array-like shape (n_samples,) or None, optional
            containing sample weights
            if None, defaults to array of ones

        Returns
        -------
        None

def _estimate_AICc(self, y, mu, weights=None):
        """
        estimate the corrected Akaike Information Criterion

        relies on the estimated degrees of freedom, which must be computed
        before.

        Parameters
        ----------
        y : array-like of shape (n_samples,)
            output data vector
        mu : array-like of shape (n_samples,)
            expected value of the targets given the model and inputs
        weights : array-like shape (n_samples,) or None, optional
            containing sample weights
            if None, defaults to array of ones

        Returns
        -------
        None
        """
        edof = self.statistics_['edof']
        if self.statistics_['AIC'] is None:
            self.statistics_['AIC'] = self._estimate_AIC(y, mu, weights)
        return self.statistics_['AIC'] + 2*(edof + 1)*(edof + 2)/(y.shape[0] - edof -2)