Robust estimation for nonparametric generalized regression

Ana M. Bianco, Graciela Boente and Susana Sombielle

Statistics & Probability Letters, 2011, vol. 81, issue 12, 1986-1994

Abstract: This paper focuses on nonparametric regression estimation for the parameters of a discrete or continuous distribution, such as the Poisson or Gamma distributions, when anomalous data are present. The proposal is a natural extension of robust methods developed in the setting of parametric generalized linear models. Robust estimators bounding either large values of the deviance or of the Pearson residuals are introduced and their asymptotic behaviour is derived. Through a Monte Carlo study, for the Poisson and Gamma distributions, the finite properties of the proposed procedures are investigated and their performance is compared with that of the classical ones. A resistant cross-validation method to choose the smoothing parameter is also considered.

Keywords: Asymptotic properties; Nonparametric generalized regression; Robust estimation; Smoothing techniques (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (3)

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DOI: 10.1016/j.spl.2011.08.007

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