# Options for comparing logistic regression models

I want to choose variables for building a logistic regression model by comparing the GOF of different types. The problem is that some of the candidates have some missing values so I can´t use the likelihood ratio test for comparing models, given that when I want to compare models with those variables they are not nested anymore.

So after exploring some options, I found that AIC or even BIC might be an option for that purpose. Is either of those a valid option?

I am not familiar with the use of BIC, is it the same as AIC, where the smaller the value is, the better?

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I'm not sure I understand why missing data means your models are not nested. Is it because cases are getting deleted if they don't have values for predictors in one model but not getting deleted in the more minimal models? That would pose a problem for AIC/BIC too, because they only provide meaningful comparisons when used on data sets with the exact same set of observations/cases. –  Anne Z. Feb 8 '12 at 15:08

The BIC is justified from the Bayesian viewpoint but you din't need to be taking a Bayesian approach to use it. It is very similar to AIC. The difference is in the form of the penalty function on the number of parameterrs used. AIC was introduced by Akaike in the mid 1970s. BIC was introduced by Gideon Schwarz in 1978.

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How do you respond to the comment made by Anne Z.? –  whuber Aug 13 '12 at 1:19