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Robust estimation for partial linear single-index models

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  • Yang Zhao
  • Sanying Feng

Abstract

In this article, minimum average variance estimation (MAVE) based on local modal regression is proposed for partial linear single-index models, which can be robust to different error distributions or outliers. Asymptotic distributions of the proposed estimators are derived, which have the same convergence rate as the original MAVE based on least squares. A modal EM algorithm is provided to implement our robust estimation. Both simulation studies and a real data example are used to evaluate the finite sample performance of the proposed estimation procedure.

Suggested Citation

  • Yang Zhao & Sanying Feng, 2022. "Robust estimation for partial linear single-index models," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 34(1), pages 228-249, January.
    • Handle: RePEc:taf:gnstxx:v:34:y:2022:i:1:p:228-249
    DOI: 10.1080/10485252.2022.2027411
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