The inverse power Lindley distribution in the presence of left-censored data

In this study, classical and Bayesian inference methods are introduced to analyze lifetime data sets in the presence of left censoring considering two generalizations of the Lindley distribution: a first generalization proposed by Ghitany et al. [Power Lindley distribution and associated inference,...

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Bibliographic Details
Published inJournal of applied statistics Vol. 45; no. 11; pp. 2081 - 2094
Main Authors Coelho-Barros, Emílio A., Mazucheli, Josmar, Achcar, Jorge A., Barco, Kelly Vanessa Parede, Tovar Cuevas, José Rafael
Format Journal Article
LanguageEnglish
Published Abingdon Taylor & Francis 18.08.2018
Taylor & Francis Ltd
Subjects
Bayesian analysis
Cancer
Electric power distribution
likelihood
Lindley distribution
Mathematical models
Reliability analysis
Statistical inference
Statistical methods
Stress concentration
survival analysis
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Summary:In this study, classical and Bayesian inference methods are introduced to analyze lifetime data sets in the presence of left censoring considering two generalizations of the Lindley distribution: a first generalization proposed by Ghitany et al. [Power Lindley distribution and associated inference, Comput. Statist. Data Anal. 64 (2013), pp. 20-33], denoted as a power Lindley distribution and a second generalization proposed by Sharma et al. [The inverse Lindley distribution: A stress-strength reliability model with application to head and neck cancer data, J. Ind. Prod. Eng. 32 (2015), pp. 162-173], denoted as an inverse Lindley distribution. In our approach, we have used a distribution obtained from these two generalizations denoted as an inverse power Lindley distribution. A numerical illustration is presented considering a dataset of thyroglobulin levels present in a group of individuals with differentiated cancer of thyroid.
ISSN:0266-4763
1360-0532
DOI:10.1080/02664763.2017.1410525