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The impact of contamination and correlated design on the Lasso: An average case analysis

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  • Minsker, Stanislav
  • Shen, Yiqiu

Abstract

We study the prediction problem in the context of the high-dimensional linear regression model. We focus on the practically relevant framework where a fraction of the linear measurements is corrupted while the columns of the design matrix can be moderately correlated. Our findings suggest that for most sparse signals, the Lasso estimator admits strong performance guarantees under more easily verifiable and less stringent assumptions on the design matrix compared to much of the existing literature.

Suggested Citation

  • Minsker, Stanislav & Shen, Yiqiu, 2025. "The impact of contamination and correlated design on the Lasso: An average case analysis," Statistics & Probability Letters, Elsevier, vol. 223(C).
    • Handle: RePEc:eee:stapro:v:223:y:2025:i:c:s0167715225000628
    DOI: 10.1016/j.spl.2025.110417
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