Comments on: High-dimensional simultaneous inference with the bootstrap

Richard A. Lockhart () and Richard J. Samworth ()
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Richard A. Lockhart: Simon Fraser University
Richard J. Samworth: University of Cambridge

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2017, vol. 26, issue 4, No 5, 734-739

Abstract: Abstract We congratulate the authors on their stimulating contribution to the burgeoning high-dimensional inference literature. The bootstrap offers such an attractive methodology in these settings, but it is well-known that its naive application in the context of shrinkage/superefficiency is fraught with danger (e.g. Samworth in Biometrika 90:985–990, 2003; Chatterjee and Lahiri in J Am Stat Assoc 106:608–625, 2011). The authors show how these perils can be elegantly sidestepped by working with de-biased, or de-sparsified, versions of estimators. In this discussion, we consider alternative approaches to individual and simultaneous inference in high-dimensional linear models, and retain the notation of the paper.

Keywords: Confidence intervals; De-biased estimator; High-dimensional inference; 62E20 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11749-017-0555-1

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