Chapter 3: Simplicity and unification in model selection
| Abstract | This chapter examines four solutions to the problem of many models, and finds some fault or limitation with all of them except the last. The first is the naïve empiricist view that best model is the one that best fits the data. The second is based on Popper’s falsificationism. The third approach is to compare models on the basis of some kind of trade off between fit and simplicity. The fourth is the most powerful: Cross validation testing. | |||||||||
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Mark L. Taper, David F. Staples & Bradley B. Shepard (2008). Model Structure Adequacy Analysis: Selecting Models on the Basis of Their Ability to Answer Scientific Questions. Synthese 163 (3):357 - 370.
I. A. Kieseppä (2001). Statistical Model Selection Criteria and Bayesianism. Proceedings of the Philosophy of Science Association 2001 (3):S141 - S152.
Jay N. Eacker (2001). Selection and the Unification of Science. Behavioral and Brain Sciences 24 (3):535-536.
Malcolm R. Forster (1995). Bayes and Bust: Simplicity as a Problem for a Probabilist's Approach to Confirmation. [REVIEW] British Journal for the Philosophy of Science 46 (3):399-424.
Arnold Zellner, Hugo A. Keuzenkamp & Michael McAleer (eds.) (2001). Simplicity, Inference and Modeling: Keeping It Sophisticatedly Simple. Cambridge University Press.
MR Forster (1999). Model Selection in Science: The Problem of Language Variance. British Journal for the Philosophy of Science 50 (1):83-102.
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