ASQ - Statistics Division

Model Selection (Part I: Model Selection Criteria) – Mini Paper Appearing in Winter 2011 Newsletter

Abstract: Model selection is the process of choosing terms for a statistical model that adequately describes or accurately predicts the system under observation. This article (Part 1) and a forthcoming article (Part 2) will discuss model selection in the context of linear statistical models, where the response variable is a continuous variable. Unifying Parts 1&2 is the awareness of a tradeoff between over- and under- fitting the model. Too few terms, and the model is under-fit and thus biased: it misses predictable parts of the data (the signal). Too many terms, and the model is over-fit: unpredictable noise in the data gets modeled as well as the desired signal.

Keywords: model selection - R-squared - Mallow's Cp - AIC - BIC

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A concise explanation and overview of model selection criteria with suggestions for more in-depth reading. The article answered many key questions for me in a straightforward, practical manner.
--Steve Robbins, 09-02-2011
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