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- Title
A bootstrap recipe for post-model-selection inference under linear regression models.
- Authors
Lee, S M S; Wu, Y
- Abstract
We 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.
- Subjects
REGRESSION analysis; LEAST squares; APPROXIMATION error; ESTIMATION theory; PROBABILITY theory
- Publication
Biometrika, 2018, Vol 105, Issue 4, p873
- ISSN
0006-3444
- Publication type
Article
- DOI
10.1093/biomet/asy046