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Data depth based on spatial rank

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  • Gao, Yonghong

Abstract

A new notion of data depth based on spatial rank is introduced. We show the proposed depth function satisfies some properties one would expect from a notion of data depth. The large sample properties of sample data depth are studied. We look at the depth based L-estimator of the population mean and the simulation study shows that our DL-estimator has good finite sample efficiency. A numerical example is also given.

Suggested Citation

  • Gao, Yonghong, 2003. "Data depth based on spatial rank," Statistics & Probability Letters, Elsevier, vol. 65(3), pages 217-225, November.
    • Handle: RePEc:eee:stapro:v:65:y:2003:i:3:p:217-225
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    Citations

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    Cited by:

    1. Yonggang Hu & Qiang Li & Yong Wang & Yi Wu, 2012. "Rayleigh projection depth," Computational Statistics, Springer, vol. 27(3), pages 523-530, September.
    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. Yonggang Hu & Yong Wang & Yi Wu & Qiang Li & Chenping Hou, 2011. "Generalized Mahalanobis depth in the reproducing kernel Hilbert space," Statistical Papers, Springer, vol. 52(3), pages 511-522, August.
    4. Nagy, Stanislav, 2017. "Monotonicity properties of spatial depth," Statistics & Probability Letters, Elsevier, vol. 129(C), pages 373-378.

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