Hi all;
I am looking for help. I am reading this paper from Thomas Lumley & Alastair Scott.
https://www.stat.colostate.edu//graybillconference2013/Presentations/Scott.pdf
They mentioned about AIC, BIC and new AIC and BIC value that scaled to sample size.
Output from the SAS SURVEYLOGISTIC Procedure
Model Fit Statistics
Criterion Intercept Intercept
Only and Covariates
AIC 201153424 159489290
SC 201153431 159489396
-2 Log L 201153422 159489262
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 41664159.2 13 <.0001
Score 38579687.9 13 <.0001
Wald 1344.6 13 <.0001
Notice that the output contains values for quantities labelled AIC,
SC (aka BIC) and Likelihood Ratio.
These mean very little as they stand. However, we can adapt them
to produce something useful.
Part of the problem is that we have used the published weights,
summing to the population size N = 246750000.
We get more reasonable values if we re-scale to the sample size
n = 13,957:
Output from PROC SURVEYLOGISTIC
Model Fit Statistics
Criterion Intercept Intercept
Only and Covariates
AIC 12800.7 10173.8
SC 12807.7 10281.8
-2 Log L 12798.7 10147.8
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 2356.7 13 <.0001
Score 2182.2 13 <.0001
Wald 1344.6 13 <.0001
I am not sure how they got the AIC value scale to sample size. It will be helpful if someone send me a code to calculate new aic value based on sample size.
Thank you,
Bikash