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Prior-free probabilistic interval estimation for binomial proportion

Author

Listed:
  • Hezhi Lu

    (South China Normal University)

  • Hua Jin

    (South China Normal University
    Stanford University)

  • Zhining Wang

    (South China Normal University)

  • Chao Chen

    (South China Normal University)

  • Ying Lu

    (Stanford University
    Stanford University
    Stanford University)

Abstract

The interval estimation of a binomial proportion has been one of the most important problems in statistical inference. The modified Wilson interval, Agresti–Coull interval, and modified Jeffreys interval have good coverage probabilities among the existing methods. However, as approximation approaches, they still behave poorly under some circumstances. In this paper, we propose an exact and efficient randomized plausible interval based on the inference model and suggest the practical use of its non-randomized approximation. The randomized plausible interval is proven to have the exact coverage probability. Moreover, our non-randomized approximation is competitive with the existing approaches confirmed by the simulation studies. Three examples including a real data analysis are illustrated to portray the usefulness of our method.

Suggested Citation

  • Hezhi Lu & Hua Jin & Zhining Wang & Chao Chen & Ying Lu, 2019. "Prior-free probabilistic interval estimation for binomial proportion," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 522-542, June.
    • Handle: RePEc:spr:testjl:v:28:y:2019:i:2:d:10.1007_s11749-018-0588-0
    DOI: 10.1007/s11749-018-0588-0
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    References listed on IDEAS

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    1. Jan Hannig & Hari Iyer & Randy C. S. Lai & Thomas C. M. Lee, 2016. "Generalized Fiducial Inference: A Review and New Results," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 1346-1361, July.
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