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

Ya’acov Ritov

doi:10.1007/s11749-023-00898-3

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

Ya’acov Ritov^*

^*Corresponding author for this work

Research output: Contribution to journal › Comment/debate

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.

Original language	English
Pages (from-to)	1180-1183
Number of pages	4
Journal	Test
Volume	32
Issue number	4
DOIs	https://doi.org/10.1007/s11749-023-00898-3
State	Published - Dec 2023
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2023, The Author(s) under exclusive licence to Sociedad de Estadística e Investigación Operativa.

Keywords

Compatibility
Identifiability
Ultra high dimension
Verification

Access to Document

10.1007/s11749-023-00898-3

Cite this

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KW - Ultra high dimension

KW - Verification

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

Abstract

Bibliographical note

Keywords

Access to Document

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