A density-based empirical likelihood ratio goodness-of-fit test for the Rayleigh distribution and power comparison

Safavinejad, M.; Jomhoori, Sara; Noughabi, Hadi Alizadeh

doi:10.1080/00949655.2014.970753

Cited by 11 publications

(8 citation statements)

References 23 publications

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“…A test based on the empirical likelihood ratio by Safavinejad, Jomhoori, and Alizadeh Noughabi (2015):…”

Section: Other Tests For the Rayleigh Distributionmentioning

confidence: 99%

See 1 more Smart Citation

A Review of Goodness-of-Fit Tests for the Rayleigh Distribution

Liebenberg

Allison²

2023

AJS

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The Rayleigh distribution has recently become popular as a model for a range of phenomena. As a result, a number of goodness-of-fit tests have been developed for this distribution. In this paper, we provide the first overview of goodness-of-fit tests for the Rayleigh distribution and compare these tests in a Monte-Carlo study to identify the tests that provide the highest powers against a wide range of alternatives. Our findings suggest that two recently developed tests as well as a test based on the Laplace transform and a test based on the Hellinger distance are the better performing tests.

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“…A test based on the empirical likelihood ratio by Safavinejad, Jomhoori, and Alizadeh Noughabi (2015):…”

Section: Other Tests For the Rayleigh Distributionmentioning

confidence: 99%

“…where f H 1 is the density under the alternative hypothesis and f H 0 is the density under H 0 . A density-based empirical likelihood technique is employed by Safavinejad et al (2015) to estimate n i=1 f H 1 (X i ). Given X 1 , X 2 , .…”

Section: Other Tests For the Rayleigh Distributionmentioning

confidence: 99%

A Review of Goodness-of-Fit Tests for the Rayleigh Distribution

Liebenberg

Allison²

2023

AJS

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“…This data is widely used in the problems of testing the Rayleigh distribution. Safavinejad et al [28] investigated the data while exploring a density-based empirical likelihood ratio goodness of fit test for the Rayleigh distribution. Jahanshahi et al [13] applied their method to this data while doing research about the goodness of fit test for the Rayleigh distribution based on the Hellinger distance.…”

Section: Real Data Analysismentioning

confidence: 99%

“…Meintanis and Iliopoulos [20] explored tests of fit for the Rayleigh distribution based on the empirical Laplace transform. Safavinejad et al [28] developed a density-based empirical likelihood ratio goodness of fit test for the Rayleigh distribution and did work about power comparison. Jahanshahi et al [13] proposed a goodness of fit test for the Rayleigh distribution based on the Hellinger distance.…”

Section: Introductionmentioning

confidence: 99%

Entropy-Based and Non-Entropy-Based Goodness of Fit Test for the Inverse Rayleigh Distribution With Progressively Type-Ii Censored Data

Gui

2020

Prob. Eng. Inf. Sci.

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In this paper, the problem of goodness of fit test for the inverse Rayleigh distribution based on progressively Type-II censored samples is studied. We develop two test statistics via entropy and propose one new non-entropy test statistic via a pivotal method. We also study the properties of these test statistics. Critical values are obtained by simulations. Then, we do power analysis of these test statistics against various alternatives under different censoring schemes. We conclude that the tests we proposed perform well against various alternatives, especially for non-monotone hazard alternatives. Finally, one real data set is analyzed.

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“…In addition, Safavinejad et al [22] presented a goodness-of-fit test for the Rayleigh distribution by using sample entropy. Then, the problem of developing an EL ratio-based test for the logistic distribution was investigated by Alizadeh Noughabi [1].…”

Section: Introductionmentioning

confidence: 99%

A Density-Based Empirical Likelihood Ratio Approach for Goodness-of-fit Tests in Decreasing Densities

Fakoor

Ajami

Jahanshahi

et al. 2020

Stat., optim. inf. comput.

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In this paper, we propose a test for the null hypothesis that a decreasing density function belongs to a givenparametric family of distribution functions against the non-parametric alternative. This method, which is based on an empirical likelihood (EL) ratio statistic, is similar to the test introduced by Vexler and Gurevich [23]. The consistency of the test statistic proposed is derived under the null and alternative hypotheses. A simulation study is conducted to inspect the power of the proposed test under various decreasing alternatives. In each scenario, the critical region of the test is obtained using a Monte Carlo technique. The applicability of the proposed test in practice is demonstrated through a few real data examples.

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A density-based empirical likelihood ratio goodness-of-fit test for the Rayleigh distribution and power comparison

Cited by 11 publications

References 23 publications

A Review of Goodness-of-Fit Tests for the Rayleigh Distribution

A Review of Goodness-of-Fit Tests for the Rayleigh Distribution

Entropy-Based and Non-Entropy-Based Goodness of Fit Test for the Inverse Rayleigh Distribution With Progressively Type-Ii Censored Data

A Density-Based Empirical Likelihood Ratio Approach for Goodness-of-fit Tests in Decreasing Densities

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