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Fiducial Inference on the Right Censored Birnbaum–Saunders Data via Gibbs Sampler

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  • Kalanka P. Jayalath

    (Department of Mathematics and Statistics, University of Houston—Clear Lake, Houston, TX 77058, USA)

Abstract

In this article, we implement a flexible Gibbs sampler to make inferences for two-parameter Birnbaum–Saunders (BS) distribution in the presence of right-censored data. The Gibbs sampler is applied on the fiducial distributions of the BS parameters derived using the maximum likelihood, methods of moments, and their bias-reduced estimates. A Monte-Carlo study is conducted to make comparisons between these estimates for Type-II right censoring with various parameter settings, sample sizes, and censoring percentages. It is concluded that the bias-reduced estimates outperform the rest with increasing precision. Higher sample sizes improve the overall accuracy of all the estimates while the amount of censoring shows a negative effect. Further comparisons are made with existing methods using two real-world examples.

Suggested Citation

  • Kalanka P. Jayalath, 2021. "Fiducial Inference on the Right Censored Birnbaum–Saunders Data via Gibbs Sampler," Stats, MDPI, vol. 4(2), pages 1-15, May.
    • Handle: RePEc:gam:jstats:v:4:y:2021:i:2:p:25-399:d:559503
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    References listed on IDEAS

    as
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