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Influence functions for a general class of depth-based generalized quantile functions

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  • Wang, Jin
  • Serfling, Robert

Abstract

Given a multivariate probability distribution F, a corresponding depth function orders points according to their "centrality" in the distribution F. One useful role of depth functions is to generate two-dimensional curves for convenient and practical description of particular features of a multivariate distribution, such as dispersion and kurtosis. Here the robustness of sample versions of such curves is explored via the influence function approach applied to the relevant functionals, using structural representations of the curves as generalized quantile functions. In particular, for a general class of so-called Type D depth functions including the well-known Tukey or halfspace depth, we obtain influence functions for the depth function itself, the depth distribution function, the depth quantile function, and corresponding depth-based generalized quantile functions. Robustness behavior similar to the usual univariate quantiles is found and quantified: the influence functions are of step function form with finite gross error sensitivity but infinite local shift sensitivity. Applications to a "scale" curve, a Lorenz curve for "tailweight", and a "kurtosis" curve are treated. Graphical illustrations are provided for the influence functions of the scale and kurtosis curves in the case of the bivariate standard normal distribution and the halfspace depth function.

Suggested Citation

  • Wang, Jin & Serfling, Robert, 2006. "Influence functions for a general class of depth-based generalized quantile functions," Journal of Multivariate Analysis, Elsevier, vol. 97(4), pages 810-826, April.
  • Handle: RePEc:eee:jmvana:v:97:y:2006:i:4:p:810-826
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    References listed on IDEAS

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    1. Einmahl, J. H.J. & Mason, D.M., 1992. "Generalized quantile processes," Other publications TiSEM b2a76bac-045d-457f-869f-d, Tilburg University, School of Economics and Management.
    2. Serfling, Robert, 2002. "Generalized Quantile Processes Based on Multivariate Depth Functions, with Applications in Nonparametric Multivariate Analysis," Journal of Multivariate Analysis, Elsevier, vol. 83(1), pages 232-247, October.
    3. Romanazzi, Mario, 2001. "Influence Function of Halfspace Depth," Journal of Multivariate Analysis, Elsevier, vol. 77(1), pages 138-161, April.
    4. Gastwirth, Joseph L, 1971. "A General Definition of the Lorenz Curve," Econometrica, Econometric Society, vol. 39(6), pages 1037-1039, November.
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    Cited by:

    1. Wang, Jin, 2019. "Asymptotics of generalized depth-based spread processes and applications," Journal of Multivariate Analysis, Elsevier, vol. 169(C), pages 363-380.
    2. Xin Dang & Robert Serfling & Weihua Zhou, 2009. "Influence functions of some depth functions, and application to depth-weighted L-statistics," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 21(1), pages 49-66.
    3. Daniel Kosiorowski, 2008. "Scale curve – a robust and nonparametric approach to study a dispersion and interdependence of multivariate distributions," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 18(4), pages 47-60.

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