Goodness-of-Fit Test Based on Kullback-Leibler Information for Progressively Type-II Censored Data

被引：44

作者：

Rad, Arezou Habibi ^{[1
]}

Yousefzadeh, Fatemeh ^{[2
]}

Balakrishnan, Narayanaswamy ^{[3
,4
]}

机构：

[1] Ferdowsi Univ Mashhad, Dept Stat, Sch Math Sci, Mashhad, Iran

[2] Birjand Univ, Dept Stat, Sch Sci, Birjand, Iran

[3] McMaster Univ, Dept Math & Stat, Hamilton, ON L8S 4K1, Canada

[4] King Saud Univ, Fac Sci, Riyadh, Saudi Arabia

来源：

IEEE TRANSACTIONS ON RELIABILITY | 2011年 / 60卷 / 03期

基金：

加拿大自然科学与工程研究理事会;

关键词：

Approximate maximum likelihood estimate; entropy; goodness-of-fit test; hazard function; Kullback-Leibler information; log-normal distribution; maximum likelihood estimate; Monte Carlo simulation; Pareto distribution; progressively type-II censored data; Weibull distribution; INTERVAL ESTIMATION; ENTROPY;

D O I：

10.1109/TR.2011.2162470

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

We express the joint entropy of progressively Type-II censored order statistics in terms of an incomplete integral of the hazard function, and use it to develop a simple estimate of the joint entropy of progressively Type-II censored data, considered earlier by Balakrishnan et al., IEEE Trans. Reliability, vol. 56, pp. 349-356. We then construct a goodness-of-fit test statistic based on the Kullback-Leibler information for Pareto, log-normal, and Weibull distributions by using maximum likelihood estimates and approximate maximum likelihood estimates of the model parameters. Finally, we use Monte Carlo simulations to evaluate the power of the proposed test for several alternatives under different sample sizes and progressive censoring schemes.

引用

页码：570 / 579

页数：10

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