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bio website stat.cmu.edu/~nmv
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Jul
22
comment Does there exist a model fit statistic (like AIC or BIC) that can be used for absolute instead of just relative comparisons?
@whuber Apparently It only lets me notify one person, so please see my response above about my specific case. Your point is well taken about $R^2$ and the context of the data.
Jul
22
comment Does there exist a model fit statistic (like AIC or BIC) that can be used for absolute instead of just relative comparisons?
@drknexus I have 3 different exams that were given to 3 different groups of people and scored by 3 different sets of raters. I fit a few models (e.g. linear, Rasch, hierarchical Bayesian) on each set of exams separately. Within the same exam data-set I can compare among the models using AIC/BIC, but I can't compare across the data-sets. For example, on exam 1 Bayesian beats Rasch, but on exam 2 Rasch beats Bayesian. Is that because of poor fit (and high AIC/BIC variance) or because of good fit and there is something different about the two exams.
Jul
22
awarded  Student
Jul
22
comment Does there exist a model fit statistic (like AIC or BIC) that can be used for absolute instead of just relative comparisons?
@whuber Wow, that's an awesome response to the $R^2$ question! But, its inadequacies aside, $R^2$ is used to say that their model is "good" in an "absolute" sense (e.g. "My $R^2$ is such-and-such which is better than what one normally sees..."). I'm looking for a more justified (and general) statistic than $R^2$ to accomplish the same purpose (e.g. "My MagicStatistic is such-and-such which is better...). My first naive thought was to do something like normalizing a k-fold cross validation score, but it doesn't seem like anyone has done such a thing (so its probably not a good idea).
Jul
22
asked Does there exist a model fit statistic (like AIC or BIC) that can be used for absolute instead of just relative comparisons?