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From |
Enzo Coviello <enzo.coviello@tin.it> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
st: Akaike formula |

Date |
Thu, 03 Apr 2003 20:55:22 +0200 |

Dear Stata Users,

in glm output Akaike Information Criterion is computed dividing the usual formula by N (number of observations).

When a covariate is included in a model (for example a Poisson model), the number of observations can obviously change. So there's two possibilities:

Compare two modes with the usual AIC, limiting observations to the smaller sample of observations (that one with new covariate included),

Compare two models with AIC as in the glm output, ignoring the different number of observations between two models. In this case models are not nested, but AIC is specifically addressed to compare non nested models.

Using two strategies above, conclusions can change and different models can be selected.

So the question is :

In comparing two models should we use second AIC (as in glm output) and second strategy or there's some case where first strategy has to be preferred and vice versa?

Any hint or comment?

Enzo

//////////////

Enzo Coviello, MD

Dipartimento di Prevenzione ASL BA/1

70055 Minervino Murge (Ba)

Italy

tel - fax +39 0883 691053

tel (home) +39 0883 695055

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