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WLAD-LASSO method for robust estimation and variable selection in partially linear models

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  • Hu Yang
  • Ning Li

Abstract

This paper focuses on robust estimation and variable selection for partially linear models. We combine the weighted least absolute deviation (WLAD) regression with the adaptive least absolute shrinkage and selection operator (LASSO) to achieve simultaneous robust estimation and variable selection for partially linear models. Compared with the LAD-LASSO method, the WLAD-LASSO method will resist to the heavy-tailed errors and outliers in the parametric components. In addition, we estimate the unknown smooth function by a robust local linear regression. Under some regular conditions, the theoretical properties of the proposed estimators are established. We further examine finite-sample performance of the proposed procedure by simulation studies and a real data example.

Suggested Citation

  • Hu Yang & Ning Li, 2018. "WLAD-LASSO method for robust estimation and variable selection in partially linear models," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 47(20), pages 4958-4976, October.
    • Handle: RePEc:taf:lstaxx:v:47:y:2018:i:20:p:4958-4976
    DOI: 10.1080/03610926.2017.1383427
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