Split regression modeling
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In this note we study the benefits of splitting variables variables for reducing the variance of linear functions of the regression coefficient estimate. We show that splitting combined with shrinkage can result in estimators with smaller mean squared error compared to popular shrinkage estimators such as Lasso, ridge regression and garrote.
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