Dear all,
currently I am working on slightly complicated mixture models for my
data. My outcome variable is bounded between 0 and 20, and has mass at
either end of the interval. Whether or not I analyse the data on the
original [0,20] scale or a transformation to [0,1] (fractions) does
not make any difference to me.
My question concerns the goodness of fit. I would like to compare the
goodness fit of the complicated finite mixture model to much simpler
models, e.g. the tobit model, the glm model. and a hurdle
specification. Since the likelihood values of these models differ
substantially, likelihood based measures such as BIC appear to be
inadequate for the purpose. Also, measures that compare the model
likelihood of the fitted model to the null likelihood ("pseudo r2")
are difficult sine I can calculate them for the tobit and glm models,
but not for the mixture model, as it is unclear what the null model
would be.
So far I have been looking at crude measures like correlation between
predicted outcome and actual outcome, but I feel that this is
inadequate, especially since the outcome variable is bounded. I'd be
grateful for hints and comments. I am working with Stata 9.2.
Eva
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