Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Gillariose, Jiju; Abdelwahab, Mahmoud M.; Elbatal, Ibrahim; Oommen, Ninan P.; Joseph, Joshin; Hasaballah, Mustafa M. Title A name given to the resource A comparative study of bayesian and classical methods for the weighted Lindley distribution under unified hybrid censoring with survival data applications Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived AIMS Mathematics;Volume;10;Issue;9;pp.22180-22205 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.3934/math.2025987" target="_blank" rel="noreferrer noopener">https://doi.org/10.3934/math.2025987</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105017389407?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105017389407?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Gillariose J., Department of Statistics and Data Science, Christ University, Hosur Road, Karnataka, Bangalore, 560029, India; Abdelwahab M.M., Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia; Elbatal I., Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia; Oommen N.P., Department of Statistics and Data Science, Christ University, Hosur Road, Karnataka, Bangalore, 560029, India; Joseph J., SCAPS, Marian College Kuttikkanam, Kuttikkanam P.O, Peermade, Idukki District, Kerala, 685531, India; Hasaballah M.M., Department of Basic Sciences, Marg Higher Institute of Engineering and Modern Technology, Cairo, 11721, Egypt Description An account of the resource In survival analysis and reliability engineering, censoring schemes play a crucial role in efficient data collection and analysis. This study investigated the unified hybrid censoring scheme (UHCS), a versatile framework that integrates multiple censoring strategies, to evaluate the suitability of the Weighted Lindley (WL) distribution for modeling lifetime data. Maximum likelihood estimates (MLEs) and their corresponding asymptotic confidence intervals are derived for the parameters of the WL distribution. In the Bayesian framework, parameter estimation was performed under a squared error loss function. A detailed Monte Carlo simulation study was conducted to compare the performance of classical and Bayesian estimators across various sample sizes and censoring schemes. The simulation results revealed that Bayesian estimators consistently yielded lower mean squared errors (MSEs) than their classical counterparts, and the associated credible intervals were generally narrower than the frequentist confidence intervals. To demonstrate the practical applicability of the proposed methods, the analysis was applied to real-world survival datasets. The results highlighted the effectiveness of the WL distribution under UHCS, offering valuable insights for researchers and practitioners in reliability and survival analysis. 2025 the Author(s), licensee AIMS Press. Subject The topic of the resource Bayesian estimation; censored data; Markov Chain Monte Carlo; maximum likelihood estimation; reliability modeling; survival analysis; unified hybrid censoring scheme; weighted Lindley distribution Publisher An entity responsible for making the resource available American Institute of Mathematical Sciences Relation A related resource ISSN: 24736988; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online