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Robust estimators for additive models using backfitting

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  • Graciela Boente
  • Alejandra Martínez
  • Matías Salibián-Barrera

Abstract

Additive models provide an attractive setup to estimate regression functions in a nonparametric context. They provide a flexible and interpretable model, where each regression function depends only on a single explanatory variable and can be estimated at an optimal univariate rate. Most estimation procedures for these models are highly sensitive to the presence of even a small proportion of outliers in the data. In this paper, we show that a relatively simple robust version of the backfitting algorithm (consisting of using robust local polynomial smoothers) corresponds to the solution of a well-defined optimisation problem. This formulation allows us to find mild conditions to show Fisher consistency and to study the convergence of the algorithm. Our numerical experiments show that the resulting estimators have good robustness and efficiency properties. We illustrate the use of these estimators on a real data set where the robust fit reveals the presence of influential outliers.

Suggested Citation

  • Graciela Boente & Alejandra Martínez & Matías Salibián-Barrera, 2017. "Robust estimators for additive models using backfitting," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 29(4), pages 744-767, October.
    • Handle: RePEc:taf:gnstxx:v:29:y:2017:i:4:p:744-767
    DOI: 10.1080/10485252.2017.1369077
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    References listed on IDEAS

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    1. Ana Bianco & Graciela Boente, 2007. "Robust estimators under semi‐parametric partly linear autoregression: Asymptotic behaviour and bandwidth selection," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(2), pages 274-306, March.
    2. Alimadad, Azadeh & Salibian-Barrera, Matias, 2011. "An Outlier-Robust Fit for Generalized Additive Models With Applications to Disease Outbreak Detection," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 719-731.
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    5. Boente, Graciela & Fraiman, Ricardo, 1989. "Robust nonparametric regression estimation," Journal of Multivariate Analysis, Elsevier, vol. 29(2), pages 180-198, May.
    6. Baek, Jangsun & Wehrly, Thomas E., 1993. "Kernel estimation for additive models under dependence," Stochastic Processes and their Applications, Elsevier, vol. 47(1), pages 95-112, August.
    7. Stefan Sperlich & Oliver Linton & Wolfgang Härdle, 1999. "Integration and backfitting methods in additive models-finite sample properties and comparison," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 8(2), pages 419-458, December.
    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.
    9. Hee-Seok Oh & Douglas W. Nychka & Thomas C. M. Lee, 2007. "The Role of Pseudo Data for Robust Smoothing with Application to Wavelet Regression," Biometrika, Biometrika Trust, vol. 94(4), pages 893-904.
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    Cited by:

    1. Ju, Xiaomeng & Salibián-Barrera, Matías, 2021. "Robust boosting for regression problems," Computational Statistics & Data Analysis, Elsevier, vol. 153(C).

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