A bootstrap recipe for post-model-selection inference under linear regression models

S M S Lee and Y Wu

Biometrika, 2018, vol. 105, issue 4, 873-890

Abstract: SUMMARYWe propose a general bootstrap recipe for estimating the distributions of post-model-selection least squares estimators under a linear regression model. The recipe constrains residual bootstrapping within the most parsimonious, approximately correct, models to yield a distribution estimator which is consistent provided any wrong candidate model is sufficiently separated from the approximately correct ones. Our theory applies to a broad class of model selection methods based on information criteria or sparse estimation. The empirical performance of our procedure is illustrated with simulated data.

Keywords: Bootstrap; Least squares estimator; Post-model-selection; Regression (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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