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Smooth depth contours characterize the underlying distribution

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  • Kong, Linglong
  • Zuo, Yijun

Abstract

The Tukey depth is an innovative concept in multivariate data analysis. It can be utilized to extend the univariate order concept and advantages to a multivariate setting. While it is still an open question as to whether the depth contours uniquely determine the underlying distribution, some positive answers have been provided. We extend these results to distributions with smooth depth contours, with elliptically symmetric distributions as special cases. The key ingredient of our proofs is the well-known Cramér-Wold theorem.

Suggested Citation

  • Kong, Linglong & Zuo, Yijun, 2010. "Smooth depth contours characterize the underlying distribution," Journal of Multivariate Analysis, Elsevier, vol. 101(9), pages 2222-2226, October.
    • Handle: RePEc:eee:jmvana:v:101:y:2010:i:9:p:2222-2226
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    References listed on IDEAS

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    1. Struyf, Anja J. & Rousseeuw, Peter J., 1999. "Halfspace Depth and Regression Depth Characterize the Empirical Distribution," Journal of Multivariate Analysis, Elsevier, vol. 69(1), pages 135-153, April.
    2. Mizera, Ivan & Volauf, Milos, 2002. "Continuity of Halfspace Depth Contours and Maximum Depth Estimators: Diagnostics of Depth-Related Methods," Journal of Multivariate Analysis, Elsevier, vol. 83(2), pages 365-388, November.
    3. Masse, J. C. & Theodorescu, R., 1994. "Halfplane Trimming for Bivariate Distributions," Journal of Multivariate Analysis, Elsevier, vol. 48(2), pages 188-202, February.
    4. Koshevoy, Gleb A., 2002. "The Tukey Depth Characterizes the Atomic Measure," Journal of Multivariate Analysis, Elsevier, vol. 83(2), pages 360-364, November.
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    Citations

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    Cited by:

    1. Daniel Kosiorowski & Jerzy P. Rydlewski & Ma{l}gorzata Snarska, 2016. "Detecting a Structural Change in Functional Time Series Using Local Wilcoxon Statistic," Papers 1604.03776, arXiv.org, revised Oct 2019.
    2. Xiaohui Liu, 2017. "Fast implementation of the Tukey depth," Computational Statistics, Springer, vol. 32(4), pages 1395-1410, December.
    3. Stanislav Nagy, 2021. "Halfspace depth does not characterize probability distributions," Statistical Papers, Springer, vol. 62(3), pages 1135-1139, June.
    4. Wei, Bei & Lee, Stephen M.S., 2012. "Second-order accuracy of depth-based bootstrap confidence regions," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 112-123.
    5. Xiaohui Liu & Qihua Wang & Yi Liu, 2017. "A consistent jackknife empirical likelihood test for distribution functions," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 69(2), pages 249-269, April.
    6. Kotík, Lukáš & Hlubinka, Daniel, 2017. "A weighted localization of halfspace depth and its properties," Journal of Multivariate Analysis, Elsevier, vol. 157(C), pages 53-69.
    7. Daniel Kosiorowski & Jerzy P. Rydlewski & Małgorzata Snarska, 2019. "Detecting a structural change in functional time series using local Wilcoxon statistic," Statistical Papers, Springer, vol. 60(5), pages 1677-1698, October.
    8. Laketa, Petra & Nagy, Stanislav, 2021. "Reconstruction of atomic measures from their halfspace depth," Journal of Multivariate Analysis, Elsevier, vol. 183(C).
    9. Xiaohui Liu & Karl Mosler & Pavlo Mozharovskyi, 2017. "Fast computation of Tukey trimmed regions and median in dimension p > 2," Working Papers 2017-71, Center for Research in Economics and Statistics.

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