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

Kalanka P. Jayalath
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Kalanka P. Jayalath: Department of Mathematics and Statistics, University of Houston—Clear Lake, Houston, TX 77058, USA

Stats, 2021, vol. 4, issue 2, 1-15

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.

Keywords: censoring; Gibbs sampler; maximum likelihood; methods of moments (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2021
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