I am putting together a negative bin. regression model. (And some mlogit models, too, actually)
Example from nbreg below.
(nbreg) I thought to report an indicator of goodness-of-model fit. AIC seemed to do the job.
Fitstat reports 3 different types of AIC.
I read often that a difference of +/- 2 in AIC is not important when comparing models.
QUESTION 1) But which version of AIC does that +/-2 rule pertain to? The largest one in the fitstat printout?
QUESTION 2) Same query about different versions of BIC reported in fitstat, which one does +/-2 rule apply to?
ALSO, I read about different ways to report AIC (see http://www.stanfordphd.com/AIC.html).
QUESTION 3) Is there a correct usual standard way to say I am using the version of AIC that divides by N (6.417 in below example), and not one of the other versions of AIC? Does this smallest value version of AIC have a special name?
QUESTION 4) I read that BIC is preferable when using 'large' datasets. My dataset has ~27,000 observations. Is ~27,000 = 'large'?
Here's hoping for simple answers...
Thanks in advance for any help! -Julii
Example from nbreg below.
(nbreg) I thought to report an indicator of goodness-of-model fit. AIC seemed to do the job.
Fitstat reports 3 different types of AIC.
I read often that a difference of +/- 2 in AIC is not important when comparing models.
QUESTION 1) But which version of AIC does that +/-2 rule pertain to? The largest one in the fitstat printout?
QUESTION 2) Same query about different versions of BIC reported in fitstat, which one does +/-2 rule apply to?
ALSO, I read about different ways to report AIC (see http://www.stanfordphd.com/AIC.html).
QUESTION 3) Is there a correct usual standard way to say I am using the version of AIC that divides by N (6.417 in below example), and not one of the other versions of AIC? Does this smallest value version of AIC have a special name?
QUESTION 4) I read that BIC is preferable when using 'large' datasets. My dataset has ~27,000 observations. Is ~27,000 = 'large'?
Here's hoping for simple answers...
Thanks in advance for any help! -Julii
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