Estimation and prediction in generalized half logistic lifetime model using hybrid censored data
International Journal of Quality & Reliability Management
ISSN: 0265-671X
Article publication date: 20 January 2023
Issue publication date: 24 October 2023
Abstract
Purpose
This article aims to develop procedures for estimation and prediction in case of Type-I hybrid censored samples drawn from a two-parameter generalized half-logistic distribution (GHLD).
Design/methodology/approach
The GHLD is a versatile model which is useful in lifetime modelling. Also, hybrid censoring is a time and cost-effective censoring scheme which is widely used in the literature. The authors derive the maximum likelihood estimates, the maximum product of spacing estimates and Bayes estimates with squared error loss function for the unknown parameters, reliability function and stress-strength reliability. The Bayesian estimation is performed under an informative prior set-up using the “importance sampling technique”. Afterwards, we discuss the Bayesian prediction problem under one and two-sample frameworks and obtain the predictive estimates and intervals with corresponding average interval lengths. Applications of the developed theory are illustrated with the help of two real data sets.
Findings
The performances of these estimates and prediction methods are examined under Type-I hybrid censoring scheme with different combinations of sample sizes and time points using Monte Carlo simulation techniques. The simulation results show that the developed estimates are quite satisfactory. Bayes estimates and predictive intervals estimate the reliability characteristics efficiently.
Originality/value
The proposed methodology may be used to estimate future observations when the available data are Type-I hybrid censored. This study would help in estimating and predicting the mission time as well as stress-strength reliability when the data are censored.
Keywords
Acknowledgements
The authors are very grateful to the anonymous reviewers and Editors for their critical comments and suggestions that have led to significant improvement in the previous version of the manuscript.
Funding: Ms. Sakshi Soni is grateful to the University Grant Commission (UGC) for their financial support in the form of a Junior Research Fellowship (1012/(CSIR-UGC NET JUNE 2018)).
Citation
Soni, S., Shukla, A.K. and Kumar, K. (2023), "Estimation and prediction in generalized half logistic lifetime model using hybrid censored data", International Journal of Quality & Reliability Management, Vol. 40 No. 9, pp. 2041-2063. https://doi.org/10.1108/IJQRM-05-2022-0149
Publisher
:Emerald Publishing Limited
Copyright © 2023, Emerald Publishing Limited