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On the validity of the pairs bootstrap for lasso estimators

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  • L. Camponovo

Abstract

We study the validity of the pairs bootstrap for lasso estimators in linear regression models with random covariates and heteroscedastic error terms. We show that the naive pairs bootstrap does not provide a valid method for approximating the distribution of the lasso estimator. To overcome this deficiency, we introduce a modified pairs bootstrap procedure and prove its consistency. Finally, we consider the adaptive lasso and show that the modified pairs bootstrap consistently estimates the distribution of the adaptive lasso estimator.

Suggested Citation

  • L. Camponovo, 2015. "On the validity of the pairs bootstrap for lasso estimators," Biometrika, Biometrika Trust, vol. 102(4), pages 981-987.
    • Handle: RePEc:oup:biomet:v:102:y:2015:i:4:p:981-987.
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    File URL: http://hdl.handle.net/10.1093/biomet/asv039
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    Cited by:

    1. Giuseppe Luca & Jan R. Magnus & Franco Peracchi, 2023. "Weighted-Average Least Squares (WALS): Confidence and Prediction Intervals," Computational Economics, Springer;Society for Computational Economics, vol. 61(4), pages 1637-1664, April.
    2. Lamarche, Carlos & Parker, Thomas, 2023. "Wild bootstrap inference for penalized quantile regression for longitudinal data," Journal of Econometrics, Elsevier, vol. 235(2), pages 1799-1826.

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