Bootstrap prediction intervals for the birnbaum-saunders distribution

The Birnbaum-Saunders distribution has been recognized as a versatile failure time model. However, it is not widely used in process control as some of its important characteristics have not been obtained. In this paper, we utilize the bootstrap method to construct a prediction interval for future ob...

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Bibliographic Details
Published in:Microelectronics and reliability 1997, Vol.37 (8), p.1213-1216
Main Authors: Lu, Ming-Che, Shang Chang, Dong
Format: Article
Language:English
Subjects:
Applied sciences
Exact sciences and technology
Fracture mechanics (crack, fatigue, damage...)
Fundamental areas of phenomenology (including applications)
Operational research and scientific management
Operational research. Management science
Physics
Reliability theory. Replacement problems
Solid mechanics
Structural and continuum mechanics
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Summary:The Birnbaum-Saunders distribution has been recognized as a versatile failure time model. However, it is not widely used in process control as some of its important characteristics have not been obtained. In this paper, we utilize the bootstrap method to construct a prediction interval for future observations from a Birnbaum-Saunders distribution. Monte Carlo simulations are carried out to evaluate the performance of the proposed procedure. The results reveal that the bootstrap intervals are satisfied with desired coverage probabilities and average lengths as sample size n is at least 30.
ISSN:0026-2714
1872-941X
DOI:10.1016/S0026-2714(96)00296-X