Bayesian inference for the log-cosine-power modified Lindley distribution: theory, simulation, and lifetime data application

In this paper, a new three-parameter lifetime distribution is derived from the log-cosinepower-generated family of distributions with the modified Lindley distribution as the baseline. It aims to modify the functionalities of the modified Lindley distribution by using a logarithmic-cosine transformation. Its key mathematical properties are established, and parameter estimation is performed using both maximum likelihood and Bayesian approaches under a variety of loss functions. A simulation study evaluates the performance of the classical and Bayesian estimators using mean squared errors and posterior risks, respectively, supplemented by a diagnostic study with three parallel chains to ensure Markov chain Monte Carlo convergence. In addition, three real data sets are analyzed to demonstrate the flexibility and practical applicability of the new distribution, featuring comprehensive Bayesian inference and prior sensitivity analysis.

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Publication Details

Journal
Hacettepe Journal of Mathematics and Statistics
Published
2026-10-08
DOI
https://doi.org/10.15672/hujms.1905675
Primary Topic
Statistical Distribution Estimation and Applications
Type
article
Field-Weighted Citation Impact
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article

Bayesian inference for the log-cosine-power modified Lindley distribution: theory, simulation, and lifetime data application

Walaa A. El-Sharkawy, Christophe Chesneau
Hacettepe Journal of Mathematics and Statistics
Statistical Distribution Estimation and Applications
article

Bayesian inference for the log-cosine-power modified Lindley distribution: theory, simulation, and lifetime data application

Walaa A. El-Sharkawy, Christophe Chesneau
article en

Abstract

In this paper, a new three-parameter lifetime distribution is derived from the log-cosinepower-generated family of distributions with the modified Lindley distribution as the baseline. It aims to modify the functionalities of the modified Lindley distribution by using a logarithmic-cosine transformation. Its key mathematical properties are established, and parameter estimation is performed using both maximum likelihood and Bayesian approaches under a variety of loss functions. A simulation study evaluates the performance of the classical and Bayesian estimators using mean squared errors and posterior risks, respectively, supplemented by a diagnostic study with three parallel chains to ensure Markov chain Monte Carlo convergence. In addition, three real data sets are analyzed to demonstrate the flexibility and practical applicability of the new distribution, featuring comprehensive Bayesian inference and prior sensitivity analysis.

Hacettepe Journal of Mathematics and Statistics(Advanced Online Publication)
Cairo University (EG), Université de Caen Normandie (FR)
Openalex Percentile: Top 11%
Statistical Distribution Estimation and Applications
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