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Multivariate L-estimation

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Abstract

In one dimension, order statistics and ranks are widely used because they form a basis for distribution free tests and some robust estimation procedures. In more than one dimension, the concept of order statistics and ranks is not clear and several definitions have been proposed in the last years. The proposed definitions are based on different concepts of depth. In this paper, we define a new notion of order statistics and ranks for multivariate data based on density estimation. The resulting ranks are invariant under affinc transformations and asymptotically distribution free. We use the corresponding order statistics to define a class of multivariate estimators of location that can be regarded as multivariate L-estimators. Under mild assumptions on the underlying distribution, we show the asymptotic normality of the estimators. A modification of the proposed estimates results in a high breakdown point procedure that can deal with patches of outliers. The main idea is to order the observations according to their likelihoodf(X 1),...,f(X n ). If the densityf happens to be cllipsoidal, the above ranking is similar to the rankings that are derived from the various notions of depth. We propose to define a ranking based on a kernel estimate of the densityf. One advantage of estimating the likelihoods is that the underlying distribution does not need to have a density. In addition, because the approximate likelihoods are only used to rank the observations, they can be derived from a density estimate using a fixed bandwidth. This fixed bandwidth overcomes the curse of dimensionality that typically plagues density estimation in high dimension.

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Author information

Authors and Affiliations

  1. Department of Statistics, The University of British, Columbia, Canada

    Jean Meloche

  2. Departamento de Matemática, Universidad de San Andrés, Victoria, Argentina

    Ricardo Fraiman

  3. University of Valladolid, Spain

    Luis A. García-Escudero & Alfonso Gordaliza

  4. University of Illinois, USA

    Xuming He

  5. Universidad de La Plata and Comision de Investigaciones Científicas Provincia de Buenos Aires, Argentina

    Ricardo Maronna

  6. Universidad de Buenos Aires and CONICET, Argentina

    Víctor J. Yohai

  7. University of New South Wales, Australia

    Simon J. Sheather

  8. Western Michigan University, USA

    Joseph W. McKean

  9. University of Waterloo, Canada

    Christopher G. Small

  10. University of Nottingham, UK

    Andrew Wood

Authors
  1. Ricardo Fraiman
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  2. Jean Meloche
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  3. Luis A. García-Escudero
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  4. Alfonso Gordaliza
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  5. Xuming He
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  9. Joseph W. McKean
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  10. Christopher G. Small
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  11. Andrew Wood
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  12. R. Fraiman
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  13. Jean Meloche
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Corresponding author

Correspondence to Ricardo Fraiman.

Additional information

The research was partially supported by grant #37 from the CONICYT and by grant 5-81089 from NSERC.

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Fraiman, R., Meloche, J., García-Escudero, L.A. et al. Multivariate L-estimation. Test 8, 255–317 (1999). https://doi.org/10.1007/BF02595872

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