Information criteria are statistical tools used to assess and compare the fit of different models, particularly in nonparametric regression. They provide a balance between model complexity and goodness of fit, helping to identify models that effectively capture the underlying data patterns without overfitting. Common examples include Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), both of which are valuable when working with local polynomials and splines.
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