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

被引：1

作者：

Coelho-Barros, Emilio A. ^{[1
]}

Mazucheli, Josmar ^{[2
]}

Achcar, Jorge A. ^{[3
]}

Parede Barco, Kelly Vanessa ^{[4
]}

Tovar Cuevas, Jose Rafael ^{[5
]}

机构：

[1] Fed Univ Technol, Dept Math, Curitiba, Parana, Brazil

[2] Univ Estadual Maringa, Dept Stat, Maringa, Parana, Brazil

[3] Univ Sao Paulo, Dept Social Med, Sao Paulo, Brazil

[4] Union Fac Campo Mourao, Campo Mourao, Brazil

[5] Univ Valle, Sch Stat, Santiago De Cali, Colombia

来源：

JOURNAL OF APPLIED STATISTICS | 2018年 / 45卷 / 11期

关键词：

Lindley distribution; likelihood; Bayesian analysis; survival analysis; GIBBS SAMPLER; INFERENCE;

D O I：

10.1080/02664763.2017.1410525

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

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.

引用

页码：2081 / 2094

页数：14

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