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On consistency of redescending M-kernel smoothers

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Abstract

M-estimators and M-kernel estimators with a redescending ψ-function are not in general consistent. This is often handled by means of coupling the estimator to a consistent one. Coupling the estimator to the (inconsistent) starting point improves the jump preserving properties. However, the consistency depends heavily on the shape of the density of the residuals. This paper shows inconsistency under convenient conditions as well as consistency – even at jump points – under somewhat stronger conditions.

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References

  • Andrews DF, Bickel PJ, Hampel FR, Huber PJ, Rogers WH, Tukey JW (1972) Robust estimates of location. Survey and advances. Princeton University Press, Princeton

    MATH  Google Scholar 

  • Candès EJ, Donoho DL (1999) Ridgelets: a key to higher-dimensional intermittency. In: Wavelets, Silvermann B, Vassilicos J (eds) Oxford University Press, pp 111–127

  • Chu CK, Glad IK, Godtliebsen F, Marron JS (1998) Edge-preserving smoothers for image processing. J Am Stat Assoc 93:526–541

    Article  MATH  MathSciNet  Google Scholar 

  • Clarke BR (1983) Uniqueness and Frechét differentiability of functional solutions to maximum likelihood type equations. Ann Stat 4:1196–1205

    Google Scholar 

  • Clarke BR (1986) Asymptotic theory for description of regions in which Newton-Raphson iterations converge to location M-estimators. J Stat Plann Inference 15:71–85

    Article  MATH  Google Scholar 

  • Collins JR (1976) Robust estimation of a location parameter in the presence of asymmetry. Ann Stat 4:68–85

    Article  MATH  Google Scholar 

  • Copas JB (1975) On the unimodality of the likelihood for the Cauchy distribution. Biometrika 62:701–704

    Article  MATH  MathSciNet  Google Scholar 

  • Donoho DL (1999) Wedgelets: nearly minimax estimation of edges. Ann Stat 27:859–897

    Article  MATH  MathSciNet  Google Scholar 

  • Donoho DL, Johnstone IM, Kerkyacharian G, Picard D (1995) Wavelet shrinkage: asymptopia? J R Stat Soc B 57:301–369

    MATH  MathSciNet  Google Scholar 

  • Eubank RL (1988) Spline smoothing and nonparametric regression. Marcel Dekker, New York

    MATH  Google Scholar 

  • Freedman DA, Diaconis P (1982) On inconsistent M-estimators. Ann Stat 10:454–461

    Article  MATH  MathSciNet  Google Scholar 

  • Härdle W, Gasser T (1984) Robust nonparametric function fitting. J R Stat Soc B 46:42–51

    MATH  Google Scholar 

  • Hillebrand M (2003) On robust corner-preserving smoothing in image processing. PhD thesis, University of Oldenburg, Germany (http://docserver.bis.uni-oldenburg.de/publikationen/dissertation/2003/hilonr03/hilonr03.html)

  • Huber P (1964) Robust estimator of a location parameter. Ann Math Stat 36:73–101

    Article  MathSciNet  Google Scholar 

  • Huber P (1981) Robust statistics. Wiley, New York

    MATH  Google Scholar 

  • Jurečková J, Sen PK (1996) Robust statistical procedures Asymptotics and interrelations. Wiley, New York

    Google Scholar 

  • Kent JT, Tyler DE (1991) Redescending M-estimates of multivariate location and scatter. Ann Stat 19:2102–2119

    Article  MATH  MathSciNet  Google Scholar 

  • Koch I (1996) On the asymptotic performance of median smoothers in image analysis and nonparametric regression. Ann Stat 24:1648–1666

    Article  MATH  Google Scholar 

  • Mizera I (1994) On consistent M-estimators: Tuning constants, unimodality and breakdown. Kybernetika 30:289–300

    MATH  MathSciNet  Google Scholar 

  • Mizera I (1996) Weak continuity of redescending M-estimators of location with an unbounded objective function. Tatra Mountains Math Publ 7:343–347

    MATH  MathSciNet  Google Scholar 

  • Parzen E (1962) On estimation of a probability density function and mode. Ann Math Stat 33:1065–1076

    Article  MathSciNet  Google Scholar 

  • Polzehl J, Spokoiny VG (2000) Adaptive weights smoothing with applications to image restoration. J R Stat Soc B 62:335–354

    Article  MathSciNet  Google Scholar 

  • Polzehl J, Spokoiny V (2003) Image denoising: pointwise adaptive approach. Ann Stat 31:30–57

    Article  MATH  MathSciNet  Google Scholar 

  • Portnoy SL (1977) Robust estimation in dependent situations. Ann Stat 5:22–43

    Article  MATH  MathSciNet  Google Scholar 

  • Serfling R (1980) Approximation theorems of mathematical statistics. Wiley, New York

    MATH  Google Scholar 

  • Smith S, Brady J (1997) SUSAN – a new approach to low level image processing. Int J Comput Vis 23:45–78

    Article  Google Scholar 

  • Tsybakov AB (1986) Robust reconstruction of functions by the local-approximation method. Probl Inf Transm 22:133–146

    MATH  Google Scholar 

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Correspondence to Martin Hillebrand.

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Research supported by the Friedrich Ebert Foundation and by grant Mu 1031/4-1/2 of the Deutsche Forschungsgemeinschaft

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Hillebrand, M., Müller, C.H. On consistency of redescending M-kernel smoothers. Metrika 63, 71–90 (2006). https://doi.org/10.1007/s00184-005-0007-x

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  • DOI: https://doi.org/10.1007/s00184-005-0007-x

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