title: Estimation and Accuracy after Model Selection.
abstract: Classical statistical theory ignores model selection in assessing estimation accuracy. Here we consider bootstrap methods for computing standard errors and confidence intervals that take model selection into account. The methodology involves bagging, also known as bootstrap smoothing, to tame the erratic discontinuities of selection-based estimators. A useful new formula for the accuracy of bagging then provides standard errors for the smoothed estimators. Two examples, nonparametric and parametric, are carried through in detail: a regression model where the choice of degree (linear, quadratic, cubic, …) is determined by theCpcriterion, and a Lasso-based estimation problem.
authors.affiliation: Stanford University.
authors.forename: Bradley
authors.initials: B
authors.lastname: Efron
keywords: ABC intervals; Cp; Lasso; bagging; bootstrap smoothing; importance sampling; model averaging