Model selection: a bootstrap approach

The problem of model selection is addressed (in a signal processing framework). Bootstrap methods based on residuals are used to select the best model according to a prediction criterion. Both the linear and the nonlinear models are treated. It is shown that bootstrap methods are consistent and in simulations that in most cases they outperform classical techniques such as Akaike's (1974) information criterion and Rissanen's (1983) minimum description length. We also show how the methods apply to dependent data models such as autoregressive models.

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