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Goodness-of-fit tests for the Cauchy distribution

Author

Listed:
  • Bora H. Onen

    (Air Force Institute of Technology, AFIT/EN)

  • Dennis C. Dietz

    (Air Force Institute of Technology, AFIT/EN)

  • Vincent C. Yen

    (Air Force Institute of Technology, AFIT/EN)

  • Albert H. Moore

    (Air Force Institute of Technology, AFIT/EN)

Abstract

Summary This article presents several modified goodness-of-fit tests for the Cauchy distribution with unknown location and scale parameters. Monte Carlo studies are performed to calculate critical values for several tests based on the empirical distribution function. Power studies suggest that the modified Kuiper (V) test is the most powerful standard test against most alternate distributions over a full range of sample sizes. A reflection technique is also employed which yields substantial improvement in the power of this test against symmetric distributions.

Suggested Citation

  • Bora H. Onen & Dennis C. Dietz & Vincent C. Yen & Albert H. Moore, 2001. "Goodness-of-fit tests for the Cauchy distribution," Computational Statistics, Springer, vol. 16(1), pages 97-107, March.
    • Handle: RePEc:spr:compst:v:16:y:2001:i:1:d:10.1007_s001800100053
    DOI: 10.1007/s001800100053
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    Cited by:

    1. Yuichi Akaoka & Kazuki Okamura & Yoshiki Otobe, 2022. "Bahadur efficiency of the maximum likelihood estimator and one-step estimator for quasi-arithmetic means of the Cauchy distribution," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(5), pages 895-923, October.

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