Comments on: Statistical inference and large-scale multiple testing for high-dimensional regression models

Ya’acov Ritov ()
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Ya’acov Ritov: University of Michigan

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2023, vol. 32, issue 4, No 4, 1180-1183

Abstract: Abstract We consider the estimation of a one-dimensional parameter in a linear model with an ultra-high number of independent variables. We argue that the standard assumptions on the design matrix are essentially technical and can be relaxed. Conversely, the assumptions on the sparsity of the nuisance parameters are unverifiable, too strong, and unavoidable.

Keywords: Ultra high dimension; Compatibility; Identifiability; Verification (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11749-023-00898-3

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