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A fast algorithm for two-dimensional Kolmogorov–Smirnov two sample tests

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  • Xiao, Yuanhui

Abstract

By using the brute force algorithm, the application of the two-dimensional two-sample Kolmogorov–Smirnov test can be prohibitively computationally expensive. Thus a fast algorithm for computing the two-sample Kolmogorov–Smirnov test statistic is proposed to alleviate this problem. The newly proposed algorithm is O(n) times more efficient than the brute force algorithm, where n is the sum of the two sample sizes. The proposed algorithm is parallel and can be generalized to higher dimensional spaces.

Suggested Citation

  • Xiao, Yuanhui, 2017. "A fast algorithm for two-dimensional Kolmogorov–Smirnov two sample tests," Computational Statistics & Data Analysis, Elsevier, vol. 105(C), pages 53-58.
    • Handle: RePEc:eee:csdana:v:105:y:2017:i:c:p:53-58
    DOI: 10.1016/j.csda.2016.07.014
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    Cited by:

    1. M. D. Jiménez-Gamero & J. L. Moreno-Rebollo & J. A. Mayor-Gallego, 2018. "On the estimation of the characteristic function in finite populations with applications," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(1), pages 95-121, March.
    2. Wei Pei & Cuizhu Tian & Qiang Fu & Yongtai Ren & Tianxiao Li, 2022. "Risk analysis and influencing factors of drought and flood disasters in China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 110(3), pages 1599-1620, February.
    3. Boris Buchmann & Kevin W. Lu & Dilip B. Madan, 2018. "Calibration for Weak Variance-Alpha-Gamma Processes," Papers 1801.08852, arXiv.org, revised Jul 2018.
    4. Boris Buchmann & Kevin W. Lu & Dilip B. Madan, 2019. "Calibration for Weak Variance-Alpha-Gamma Processes," Methodology and Computing in Applied Probability, Springer, vol. 21(4), pages 1151-1164, December.

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