Relative Variability Estimation for the Power Lindley Distribution with Progressive Type-I Interval Censored Data
Abstract
The measures of relative variability, such as the coefficient of variation, are highly utilized tools of inference in several interdisciplinary fields such as reliability, finance, quality control, and biological sciences. Inferential studies of these measures are highly limited for lifetime models, such as the flexible power Lindley distribution, particularly under progressive type-I interval censoring. In this article, frequentist approaches including the midpoint approximation, maximum likelihood estimation, method of moments, bootstrap, and nonlinear least squares methods, and Bayesian inference using slice sampling are applied for estimation of these measures of relative variability for the power Lindley distribution under progressive type-I interval censoring. Confidence intervals are also constructed based on asymptotic theory, bootstrap and Bayesian paradigm. A discussion on choosing optimal monitoring intervals is also highlighted. A comprehensive simulation study is conducted to evaluate the performance of the proposed estimators across various censoring plans and sample sizes. A real data application illustrates the practical utility of the proposed methodologies. The results indicate that the Bayesian framework generally exhibits superior performance in both point and interval estimation.
Related
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BibTeX
@misc{nongrum2026relative,
title = {{Relative Variability Estimation for the Power Lindley Distribution with Progressive Type-I Interval Censored Data}},
author = {Bankitdor M. Nongrum and Adarsha Kumar Jena},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2608.09486},
eprint = {2608.09486},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2608.09486},
}
RIS
TY - UNPB
TI - Relative Variability Estimation for the Power Lindley Distribution with Progressive Type-I Interval Censored Data
AU - Bankitdor M. Nongrum
AU - Adarsha Kumar Jena
PY - 2026
DA - 2026/10/05
PB - arXiv
DO - 10.48550/arxiv.2608.09486
UR - https://arxiv.org/abs/2608.09486
AB - The measures of relative variability, such as the coefficient of variation, are highly utilized tools of inference in several interdisciplinary fields such as reliability, finance, quality control, and biological sciences. Inferential studies of these measures are highly limited for lifetime models, such as the flexible power Lindley distribution, particularly under progressive type-I interval censoring. In this article, frequentist approaches including the midpoint approximation, maximum likelihood estimation, method of moments, bootstrap, and nonlinear least squares methods, and Bayesian inference using slice sampling are applied for estimation of these measures of relative variability for the power Lindley distribution under progressive type-I interval censoring. Confidence intervals are also constructed based on asymptotic theory, bootstrap and Bayesian paradigm. A discussion on choosing optimal monitoring intervals is also highlighted. A comprehensive simulation study is conducted to evaluate the performance of the proposed estimators across various censoring plans and sample sizes. A real data application illustrates the practical utility of the proposed methodologies. The results indicate that the Bayesian framework generally exhibits superior performance in both point and interval estimation.
ER -