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

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  • Ya’acov Ritov

    (University of Michigan)

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.

Suggested Citation

  • Ya’acov Ritov, 2023. "Comments on: Statistical inference and large-scale multiple testing for high-dimensional regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(4), pages 1180-1183, December.
    • Handle: RePEc:spr:testjl:v:32:y:2023:i:4:d:10.1007_s11749-023-00898-3
    DOI: 10.1007/s11749-023-00898-3
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    References listed on IDEAS

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    1. Robinson, Peter M, 1988. "Root- N-Consistent Semiparametric Regression," Econometrica, Econometric Society, vol. 56(4), pages 931-954, July.
    2. T. Tony Cai & Zijian Guo & Yin Xia, 2023. "Statistical inference and large-scale multiple testing for high-dimensional regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(4), pages 1135-1171, December.
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