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VIF Regression: A Fast Regression Algorithm for Large Data
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DOI：10.1198/jasa.2011.tm10113.png)
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Abstract
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We propose a fast and accurate algorithm. VIF regression, for doing feature selection in large regression problems. VIP regression is extremely fast: it uses a one-pass search over the predictors and a computationally efficient method of testing each potential predictor for addition to the model. VIE regression provably avoids model overfitting, controlling the marginal false discovery rate. Numerical results show that it is much faster than any other published algorithm for regression with feature selection and is as accurate as the best of the slower algorithms.
Keywords:
Marginal False Discovery Rate
Model selection
Stepwise regression
Variable selection
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