Statistical inference is, essentially, about identifying the best model for a set of data. However, “best” entails a tradeoff between model fit and model flexibility: A more flexible model will generally fit better, but at the potential cost of fitting random noise. Consequently, measuring model flexibility is a key problem. For linear models, model flexibility can be measured by counting model parameters, but what about nonlinear models? In the present talk, I describe a technique for measuring the flexibility of nonlinear (and constrained linear) models by finding the “effective” number of model parameters, providing a common index of flexibility across different model types.

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