Inferences for multiple interval type-I censoring scheme

Shubham Agnihotri, Sanjay Kumar Singh and Umesh Singh

Journal of Applied Statistics, 2023, vol. 50, issue 1, 86-105

Abstract: In this paper, we have introduced a new type of censoring scheme named the multiple interval type-I censoring scheme. Further, We have assumed that the test units are drawn from the Weibull population. We have also proposed the maximum product of spacing estimators for unknown parameters under the multiple interval type-I censoring scheme and compare them with the existing maximum likelihood estimators. In addition to this, the Bayes estimators for shape and scale parameters are also obtained under the squared error loss function. Their corresponding asymptotic confidence/credible intervals are also discussed. A real data set containing the breakdown time of insulating fluids are used to demonstrate the appropriateness of the proposed methodology.

Date: 2023
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DOI: 10.1080/02664763.2021.1981832

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