st: RE: Model selection by using BIC values after using glm command

 From "Martin Weiss" To Subject st: RE: Model selection by using BIC values after using glm command Date Sun, 15 Nov 2009 23:54:14 +0100

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Have a look at [R], p. 526, at the bottom: "Inferences based on comparison
of IC values reported by glm
for different GLM models will be equivalent to those based on comparison of
IC values reported by
estat ic after glm."

HTH
Martin

-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Abdul Q Memon
Sent: Sonntag, 15. November 2009 23:23
To: statalist@hsphsun2.harvard.edu
Subject: st: Model selection by using BIC values after using glm command

Hi

This is my first email on the group. I hope some one will be able to give
me valueable comments for which i will be thankful.

My querry is about the BIC (Bayesian information criteria). I have seen
two version of formulas one is using the likelihood and other is using the
deviance of the model.

1. BIC=D+p*LN(N) after glm command
2.BIC=-2L+p*LN(N) after estat command

I am using glm command and negative binomial regression on count data
(data is daily observations of road accidents from 1st Jan 1991 to 31st
dec 2005). Total observations are 5429. I am estimating the value of alpha
(k) and using nbreg command in STATA.

I was expecting that both the formulas will give a same pattern for model
selection( i have 20 models on the data and i am sorting the results by
BIC). Although likelihood model formula is bit easy to explain because the
deviance formulas is giving the lowest BIC for null model as the deviance
of negative binomial regression is small after GLM.

I am still in double minds which BIC formula to use. R software is
probably using likelihhod formula.

highly appreciated.

Best regards

Memon

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