Open josef-pkt opened 8 years ago
Interesting discussion on information criteria and "all models are false" http://warnercnr.colostate.edu/~anderson/PDF_files/AIC%20Myths%20and%20Misunderstandings.pdf linked to from this stats.stackexchange discussion http://stats.stackexchange.com/questions/86664/can-the-burnham-anderson-book-on-multimodel-inference-be-recommended?rq=1
another reference for information criteria in general settings, including penalized maximum likelihood and robust regression, if the model is misspecified Konishi & Kitagawa (1996), "Generalized information criteria in model selection", Biometrika, 83 http://bemlar.ism.ac.jp/zhuang/Refs/Refs/kitagawa1996biometrika.pdf found in the discussion in http://stats.stackexchange.com/questions/88398/model-selection-with-firth-logistic-regression
Suppose we only have "wrong" models, what's the "best" model and our "best" estimates?
And, how do you define "best"?
Motivation: Gamma regression: Greene compares MLE versus GMM, where GMM uses overidentified moment conditions. Gamma regression is in GLM/LEF and uses mean and variance assumptions.
preliminaries
So, What's the point?
... ???