tools.eval_measures.aicc_sigma()

statsmodels.tools.eval_measures.aicc_sigma

statsmodels.tools.eval_measures.aicc_sigma(sigma2, nobs, df_modelwc, islog=False) [source]

Akaike information criterion (AIC) with small sample correction

Parameters:	sigma2 : float estimate of the residual variance or determinant of Sigma_hat in the multivariate case. If islog is true, then it is assumed that sigma is already log-ed, for example logdetSigma. nobs : int number of observations df_modelwc : int number of parameters including constant
Returns:	aicc : float information criterion

Notes

A constant has been dropped in comparison to the loglikelihood base information criteria. These should be used to compare for comparable models.

References

http://en.wikipedia.org/wiki/Akaike_information_criterion#AICc