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Robust estimation in partially nonlinear models

Author

Listed:
  • Andrés Muñoz

    (Instituto Técnologico de Buenos Aires)

  • Daniela Rodriguez

    (Universidad de Buenos Aires and CONICET)

Abstract

This paper introduces a class of robust estimators for the parametric and nonparametric components of the partially nonlinear model. The robust estimators are based on a three-step procedure. We prove that the estimates of the parametric component are root–n consistent and asymptotically normally distributed. Through a Monte Carlo study, we compare the performance of our proposal to that of the classical estimators. We illustrate our procedure with examples.

Suggested Citation

  • Andrés Muñoz & Daniela Rodriguez, 2023. "Robust estimation in partially nonlinear models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(5), pages 1407-1437, December.
    • Handle: RePEc:spr:stmapp:v:32:y:2023:i:5:d:10.1007_s10260-023-00705-1
    DOI: 10.1007/s10260-023-00705-1
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    References listed on IDEAS

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    8. Boente, Graciela & Rodriguez, Daniela, 2008. "Robust bandwidth selection in semiparametric partly linear regression models: Monte Carlo study and influential analysis," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2808-2828, January.
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