Classical and Bayesian inference approaches for the exponentiated discrete Weibull model with censored data and a cure fraction

被引：3

作者：

Lopes de Freitas, Bruno Caparroz ^{[1
]}

de Oliveira Peres, Marcos Vinicius ^{[2
]}

Achcar, Jorge Alberto ^{[2
]}

Martinez, Edson Zangiacomi ^{[2
]}

机构：

[1] Univ Estadual Maringa, Master Program Biostat, Maringa, Parana, Brazil

[2] Univ Sao Paulo, Ribeirao Preto Med Sch, Ribeirao Preto, Brazil

来源：

PAKISTAN JOURNAL OF STATISTICS AND OPERATION RESEARCH | 2021年 / 17卷 / 02期

关键词：

Survival analysis; Maximum likelihood estimation; Cure fraction; Bayesian inference; Discrete distributions; Censored data; SURVIVAL-DATA; MIXTURE; DISTRIBUTIONS; CONVERGENCE; TERM;

D O I：

10.18187/pjsor.v17i2.3693

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 paper, we introduce maximum likelihood and Bayesian parameter estimation for the exponentiated discrete Weibull (EDW) distribution in the presence of randomly right-censored data. We also consider the inclusion of a cure fraction in the model. The performance of the maximum likelihood estimation approach is assessed by conducting an extensive simulation study with different sample sizes and different values for the parameters of the EDW distribution. The uselfuness of the proposed model is illustrated with two examples considering real data sets.

引用

页码：467 / 481

页数：15

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