Nonlinear variable selection with continuous outcome: a nonparametric incremental forward stagewise approach
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We present a method of variable selection for the situation where some predictors are nonlinearly associated with a continuous outcome variable. The method doesn't assume any specific functional form, and can select from a large number of candidates. It takes the form of incremental forward stagewise regression, in which very small steps are taken to select the variables. Given no functional form is assumed, we devised an approach termed roughening to adjust the residuals in the iterations. In simulations, we show the new method is competitive against popular machine learning approaches. We also demonstrate its performance using some real datasets.
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