Exponentiality Test Based on the Progressive Type II Censoring via Cumulative Entropy

Baratpour, S.; Rad, A. Habibi

doi:10.1080/03610918.2014.917673

Cited by 26 publications

(9 citation statements)

References 30 publications

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“…Vasicek's contribution has greatly contributed to the development of entropy-based distribution fitting test topics. Afterwards, many scholars have studied this topic; refer to [12][13][14].…”

Section: Entropy and Kullback-leibler Informationmentioning

confidence: 99%

“…Baratpour [14] proposed a new CRE-based method for comparing the distance between two distributions, named cumulative residual Kullback-Leibler (CRKL) divergence. They also constructed fitting tests under exponential distribution.…”

Section: Lemmamentioning

confidence: 99%

“…Because the study of the exponential distribution [14] indicated that the hazard function had a great influence on the study of the power of statistics, we present alternatives in Table 1 classified by the type of hazard function: Table 1. Alternative hypotheses for power study.…”

Section: Monte Carlo Simulationsmentioning

confidence: 99%

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Goodness of Fit Tests for the Log-Logistic Distribution Based on Cumulative Entropy under Progressive Type II Censoring

Gui

2019

Mathematics

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In this paper, we propose two new methods to perform goodness-of-fit tests on the log-logistic distribution under progressive Type II censoring based on the cumulative residual Kullback-Leibler information and cumulative Kullback-Leibler information. Maximum likelihood estimation and the EM algorithm are used for statistical inference of the unknown parameter. The Monte Carlo simulation is conducted to study the power analysis on the alternative distributions of the hazard function monotonically increasing and decreasing. Finally, we present illustrative examples to show the applicability of the proposed methods.

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Section: Entropy and Kullback-leibler Informationmentioning

confidence: 99%

Section: Lemmamentioning

confidence: 99%

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Goodness of Fit Tests for the Log-Logistic Distribution Based on Cumulative Entropy under Progressive Type II Censoring

Gui

2019

Mathematics

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“…Since HX,YRfalse(f,g;t1,t2false) has simple relationships with many of these generalized divergence measures , HX,YRfalse(f,g;t1,t2false) can be equally useful as a goodness of fit test to compare two probability distributions. One can also refer to Baratpour and Rad [31,[32] for using cumulative KL divergence as deriving a consistent test statistic for testing the hypothesis of exponentiality against some alternatives .…”

Section: Residual R-norm Entropy For Conditionally Specified Modelsmentioning

confidence: 99%

Some Properties of Residual R-norm Entropy and Divergence Measures

Linu

Sunoj

2017

Calcutta Statistical Association Bulletin

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Shannon entropy plays an important role in measuring the expected uncertainty contained in the probability density function about the predictability of an outcome of a random variable. However, in certain systems, Shannon entropy may not be appropriate, where some generalized versions of it are only suitable. One such generalization is due to Boekee and Lubee [1] , called R-norm entropy. Recently, Nanda and Das [2] studied the R-norm entropy and its divergence measure in the context of used items, useful in reliability modelling. In the present article, we further study R-norm entropy and divergence in the context of weighted models. We also extend these measures to the conditionally specified and conditional survival models, and studied their properties.

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“…It is easy to see that ε(X, t) = ε(X t ), where X t = {X − t | X > t} is the residual lifetime of X at t (in particular, when F (0) = 0, ε(X, 0) = ε(X)). Applications and properties of these measures can be found in Navarro et al (2010), Baratpour (2010), Kapodistria and Psarrakos (2012), Baratpour and Habibi Rad (2016), and Chamany and Baratpour (2014). We provide conditions in Section 3 under which ε(X, t) is a measure consistent with the dispersive order.…”

Section: Introductionmentioning

confidence: 99%

Stochastic comparisons of interfailure times under a relevation replacement policy

Sordo

Psarrakos

2017

J. Appl. Probab.

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We provide some results for the comparison of the failure times and interfailure times of two systems based on a replacement policy proposed by Kapodistria and Psarrakos (2012). In particular, we show that when the first failure times are ordered in terms of the dispersive order (or, the excess wealth order), then the successive interfailure times are ordered in terms of the usual stochastic order (respectively, the increasing convex order). As a consequence, we provide comparison results for the cumulative residual entropies of the systems and their dynamic versions.

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Exponentiality Test Based on the Progressive Type II Censoring via Cumulative Entropy

Cited by 26 publications

References 30 publications

Goodness of Fit Tests for the Log-Logistic Distribution Based on Cumulative Entropy under Progressive Type II Censoring

Goodness of Fit Tests for the Log-Logistic Distribution Based on Cumulative Entropy under Progressive Type II Censoring

Some Properties of Residual R-norm Entropy and Divergence Measures

Stochastic comparisons of interfailure times under a relevation replacement policy

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