IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0229845.html
   My bibliography  Save this article

A new method for computing the projection median, its influence curve and techniques for the production of projected quantile plots

Author

Listed:
  • Fan Chen
  • Guy Nason

Abstract

This article introduces a new formulation of, and method of computation for, the projection median. Additionally, we explore its behaviour on a specific bivariate set up, providing the first theoretical result on form of the influence curve for the projection median, accompanied by numerical simulations. Via new simulations we comprehensively compare our performance with an established method for computing the projection median, as well as other existing multivariate medians. We focus on answering questions about accuracy and computational speed, whilst taking into account the underlying dimensionality. Such considerations are vitally important in situations where the data set is large, or where the operations have to be repeated many times and some well-known techniques are extremely computationally expensive. We briefly describe our associated R package that includes our new methods and novel functionality to produce animated multidimensional projection quantile plots, and also exhibit its use on some high-dimensional data examples.

Suggested Citation

  • Fan Chen & Guy Nason, 2020. "A new method for computing the projection median, its influence curve and techniques for the production of projected quantile plots," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-22, May.
    • Handle: RePEc:plo:pone00:0229845
    DOI: 10.1371/journal.pone.0229845
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0229845
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0229845&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0229845?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Struyf, Anja & Rousseeuw, Peter J., 2000. "High-dimensional computation of the deepest location," Computational Statistics & Data Analysis, Elsevier, vol. 34(4), pages 415-426, October.
    2. Oja, Hannu, 1983. "Descriptive statistics for multivariate distributions," Statistics & Probability Letters, Elsevier, vol. 1(6), pages 327-332, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Masse, Jean-Claude & Plante, Jean-Francois, 2003. "A Monte Carlo study of the accuracy and robustness of ten bivariate location estimators," Computational Statistics & Data Analysis, Elsevier, vol. 42(1-2), pages 1-26, February.
    2. Xiaohui Liu & Shihua Luo & Yijun Zuo, 2020. "Some results on the computing of Tukey’s halfspace median," Statistical Papers, Springer, vol. 61(1), pages 303-316, February.
    3. Mia Hubert & Peter Rousseeuw & Pieter Segaert, 2015. "Multivariate functional outlier detection," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(2), pages 177-202, July.
    4. G. Zioutas & C. Chatzinakos & T. D. Nguyen & L. Pitsoulis, 2017. "Optimization techniques for multivariate least trimmed absolute deviation estimation," Journal of Combinatorial Optimization, Springer, vol. 34(3), pages 781-797, October.
    5. Gangwei Cai & Baoping Zou & Xiaoting Chi & Xincheng He & Yuang Guo & Wen Jiang & Qian Wu & Yujin Zhang & Yanna Zhou, 2023. "Neighborhood Spatio-Temporal Impacts of SDG 8.9: The Case of Urban and Rural Exhibition-Driven Tourism by Multiple Methods," Land, MDPI, vol. 12(2), pages 1-37, January.
    6. Victor Chernozhukov & Alfred Galichon & Marc Hallin & Marc Henry, 2014. "Monge-Kantorovich Depth, Quantiles, Ranks, and Signs," Papers 1412.8434, arXiv.org, revised Sep 2015.
    7. Zhou, Xinyu & Ma, Yijia & Wu, Wei, 2023. "Statistical depth for point process via the isometric log-ratio transformation," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
    8. Hwang, Jinsoo & Jorn, Hongsuk & Kim, Jeankyung, 2004. "On the performance of bivariate robust location estimators under contamination," Computational Statistics & Data Analysis, Elsevier, vol. 44(4), pages 587-601, January.
    9. J. T. A. S. Ferreira & M. F. J. Steel, 2004. "On Describing Multivariate Skewness: A Directional Approach," Econometrics 0409010, University Library of Munich, Germany.
    10. Masato Okamoto, 2009. "Decomposition of gini and multivariate gini indices," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 7(2), pages 153-177, June.
    11. Victor Chernozhukov & Alfred Galichon & Marc Hallin & Marc Henry, 2014. "Monge-Kantorovich Depth, Quantiles, Ranks, and Signs," Papers 1412.8434, arXiv.org, revised Sep 2015.
    12. Averous, Jean & Meste, Michel, 1997. "Median Balls: An Extension of the Interquantile Intervals to Multivariate Distributions," Journal of Multivariate Analysis, Elsevier, vol. 63(2), pages 222-241, November.
    13. Chanont Banternghansa & Michael W. McCracken, 2009. "Forecast disagreement among FOMC members," Working Papers 2009-059, Federal Reserve Bank of St. Louis.
    14. Möttönen, J. & Hettmansperger, T. P. & Oja, H. & Tienari, J., 1998. "On the Efficiency of Affine Invariant Multivariate Rank Tests," Journal of Multivariate Analysis, Elsevier, vol. 66(1), pages 118-132, July.
    15. Taskinen, Sara & Kankainen, Annaliisa & Oja, Hannu, 2003. "Sign test of independence between two random vectors," Statistics & Probability Letters, Elsevier, vol. 62(1), pages 9-21, March.
    16. Eisenberg, Bennett, 2015. "The multivariate Gini ratio," Statistics & Probability Letters, Elsevier, vol. 96(C), pages 292-298.
    17. Aurora Torrente & Juan Romo, 2021. "Initializing k-means Clustering by Bootstrap and Data Depth," Journal of Classification, Springer;The Classification Society, vol. 38(2), pages 232-256, July.
    18. Fraiman, Ricardo & Gamboa, Fabrice & Moreno, Leonardo, 2019. "Connecting pairwise geodesic spheres by depth: DCOPS," Journal of Multivariate Analysis, Elsevier, vol. 169(C), pages 81-94.
    19. Kim, Jeankyung, 2000. "Rate of convergence of depth contours: with application to a multivariate metrically trimmed mean," Statistics & Probability Letters, Elsevier, vol. 49(4), pages 393-400, October.
    20. Wang, Jin & Zhou, Weihua, 2012. "A generalized multivariate kurtosis ordering and its applications," Journal of Multivariate Analysis, Elsevier, vol. 107(C), pages 169-180.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0229845. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.