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 – information criterion

Return type:

float

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

© 2009–2012 Statsmodels Developers
© 2006–2008 Scipy Developers
© 2006 Jonathan E. Taylor
Licensed under the 3-clause BSD License.
http://www.statsmodels.org/stable/generated/statsmodels.tools.eval_measures.aicc_sigma.html