Stepwise Model Selection

Stepwise model selection refers broadly to the use of an iterative procedure for the identification of predictors for inclusion in a model, whereby the utility of predictors is evaluated at each step and the results of this evaluation are used to inform the inclusion of predictors at subsequent steps of the process. Predictive modeling can encompass a number of research goals, such as maximizing accuracy, whittling down predictors to minimize redundancy, testing specific predictive hypotheses, or comparing competing predictive models. In well-researched areas with strong theoretical guidance, the choice of predictors to test in a model may be relatively clear. The focus of such research may be on testing the incremental value of specific predictors or testing competing predictive models. However, in domains lacking strong ...

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