Estimation in Generalized Lindley distribution with randomly censored data

Authors

  • Shahdie Marganpoor Department of Statistics, Golsetan University, Iran
  • Vahid Ranjbar Department of Statistics, Golsetan University, Iran

DOI:

https://doi.org/10.19139/soic-2310-5070-1183

Keywords:

Generalized Lindley distribution, Random censoring, maximum likelihood estimation, Bayes estimation, MCMC method, HPD credible interval

Abstract

 In this article, we obtained the point and interval estimations for a generalized Lindley distribution (GLD) based on randomly censored data. The maximum likelihood (ML) and Bayes estimation method are used to estimate the unknown parameters of the GLD. Furthermore, approximate confidence intervals (ACIs) for the unknown parameters were constructed. Markov chain Monte Carlo (MCMC) method applied to find the Bayes estimation. Also, highest posterior density (HPD) credible intervals (CRIs) were obtained for the parameters. Gibbs within Metropolis-Hasting samplers used to generate samples from the posterior density functions. A real data set is discussed to illustrate the proposed methods. we performed a Monte Carlo simulation study.

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Published

2026-06-08

How to Cite

Marganpoor, S., & Ranjbar, V. (2026). Estimation in Generalized Lindley distribution with randomly censored data. Statistics, Optimization & Information Computing, 16(2), 1618–1639. https://doi.org/10.19139/soic-2310-5070-1183

Issue

Vol. 16 No. 2 (2026)

Section

Research Articles

License

Copyright (c) 2026 Shahdie Marganpoor, Vahid Ranjbar

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