Goodness-of-fit test based on information criterion for interval censored data

被引：0

作者：

Omidi, Fatemeh ^{[1
]}

Fakoor, Vahid ^{[1
]}

Habibirad, Arezou ^{[1
]}

机构：

[1] Ferdowsi Univ Mashhad, Dept Stat, Mashhad, Razavi Khorasan, Iran

来源：

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 2023年 / 52卷 / 03期

关键词：

Cumulative residual entropy; interval censored data; Kullback-Leibler divergence; leveraged bootstrap; nonparametric maximum likelihood estimator; KULLBACK-LEIBLER INFORMATION; FAILURE TIME DATA; LOG-RANK TEST; MAXIMUM-LIKELIHOOD; NONPARAMETRIC-ESTIMATION; BOOTSTRAP METHODS; COMPUTATION; CONSISTENCY; ALGORITHM; ENTROPY;

D O I：

10.1080/03610926.2021.1931331

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：

Some goodness-of-fit tests for interval censored data are proposed. For this aim, we introduce a divergence measure based on Kullback-Leibler divergence and use a resampling method called the leveraged bootstrap. We show that these tests are asymptotically consistent. Some results are determined using Monte Carlo simulation, and an example from AIDS research is considered.

引用

页码：830 / 850

页数：21

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