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• W2892430151 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." @default.
• W2892430151 created "2018-10-05" @default.
• W2892430151 creator A5021015026 @default.
• W2892430151 creator A5051590473 @default.
• W2892430151 date "2018-09-25" @default.
• W2892430151 modified "2023-10-01" @default.
• W2892430151 title "A bootstrap recipe for post-model-selection inference under linear regression models" @default.
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• W2892430151 doi "https://doi.org/10.1093/biomet/asy046" @default.
• W2892430151 hasPublicationYear "2018" @default.
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