Estimation and Accuracy After Model Selection

Bradley Efron

Journal of the American Statistical Association, 2014, vol. 109, issue 507, 991-1007

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 the C p criterion and a Lasso-based estimation problem.

Date: 2014
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Citations: View citations in EconPapers (37)

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DOI: 10.1080/01621459.2013.823775

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