modelselection.org - Model Selection

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Description: Model selection

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Introduction to Model Selection (.pdf)

'Model selection (variable selection in regression is a special case) is a bias versus variance trade-off and this is the statistical principle of parsimony. Inference under models with too few parameters (variables) can be biased, while with models having too many parameters (variables) there may be poor precision or identification of effects that are, in fact, spurious. These considerations call for a balance between under- and over-fitted models -- the so-called "model selection problem" (see Forster 200

Webmaster:  Martin Sewell

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