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CompPD: A MATLAB Package for Computing Projection Depth

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  • Liu, Xiaohui
  • Zuo, Yijun

Abstract

Since the seminal work of Tukey (1975), depth functions have proved extremely useful in robust data analysis and inference for multivariate data. Many notions of depth have been developed in the last decades. Among others, projection depth appears to be very favorable. It turns out that (Zuo 2003 ; Zuo, Cui, and He 2004; Zuo 2006), with appropriate choices of univariate location and scale estimators, the projection depth induced estimators usually possess very high breakdown point robustness and infinite sample relative efficiency. However, the computation of the projection depth seems hopeless and intimidating if not impossible. This hinders the further inference procedures development in practice. Sporadically algorithms exist in individual papers, though an unified computation package for projection depth has not been documented. To fill the gap, a MATLAB package entitled CompPD is presented in this paper, which is in fact an implementation of the latest developments (Liu, Zuo, and Wang 2013; Liu and Zuo 2014). Illustrative examples are also provided to guide readers through step-by-step usage of package CompPD to demonstrate its utility.

Suggested Citation

  • Liu, Xiaohui & Zuo, Yijun, 2015. "CompPD: A MATLAB Package for Computing Projection Depth," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 65(i02).
    • Handle: RePEc:jss:jstsof:v:065:i02
    DOI: http://hdl.handle.net/10.18637/jss.v065.i02
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    References listed on IDEAS

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    1. Bazovkin, Pavel & Mosler, Karl, 2012. "An Exact Algorithm for Weighted-Mean Trimmed Regions in Any Dimension," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 47(i13).
    2. Marc Hallin & Davy Paindaveine & Miroslav Siman, 2008. "Multivariate quantiles and multiple-output regression quantiles: from L1 optimization to halfspace depth," Working Papers ECARES 2008_042, ULB -- Universite Libre de Bruxelles.
    3. Jun Li & Juan A. Cuesta-Albertos & Regina Y. Liu, 2012. "DD -Classifier: Nonparametric Classification Procedure Based on DD -Plot," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 737-753, June.
    4. Mosler, Karl & Lange, Tatjana & Bazovkin, Pavel, 2009. "Computing zonoid trimmed regions of dimension d>2," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2500-2510, May.
    5. Mia Hubert & Stephan Van der Veeken, 2010. "Robust classification for skewed data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 4(4), pages 239-254, December.
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    Cited by:

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    2. Daniel Kosiorowski & Jerzy P. Rydlewski, 2020. "Centrality-oriented causality. A study of EU agricultural subsidies and digital developement in Poland," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 30(3), pages 47-63.
    3. Kosiorowski Daniel & Jerzy P. Rydlewski, 2019. "Centrality-oriented Causality -- A Study of EU Agricultural Subsidies and Digital Developement in Poland," Papers 1908.11099, arXiv.org, revised Sep 2019.
    4. Gloria Gonzalez-Rivera & Yun Luo & Esther Ruiz, 2018. "Prediction Regions for Interval-valued Time Series," Working Papers 201817, University of California at Riverside, Department of Economics.
    5. Wang, Shanshan & Serfling, Robert, 2018. "On masking and swamping robustness of leading nonparametric outlier identifiers for multivariate data," Journal of Multivariate Analysis, Elsevier, vol. 166(C), pages 32-49.
    6. Liu, Xiaohui & Rahman, Jafer & Luo, Shihua, 2019. "Generalized and robustified empirical depths for multivariate data," Statistics & Probability Letters, Elsevier, vol. 146(C), pages 70-79.

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