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ENTROPY-BASED AND NON-ENTROPY-BASED GOODNESS OF FIT TEST FOR THE INVERSE RAYLEIGH DISTRIBUTION WITH PROGRESSIVELY TYPE-II CENSORED DATA

Published online by Cambridge University Press:  11 March 2020

Yanbin Ma  and
Wenhao Gui Open the ORCID record for Wenhao Gui [Opens in a new window]
Yanbin Ma
Affiliation:
Department of Mathematics, Beijing Jiaotong University, Beijing 100044, China E-mail: whgui@bjtu.edu.cn
Wenhao Gui
Affiliation:
Department of Mathematics, Beijing Jiaotong University, Beijing 100044, China E-mail: whgui@bjtu.edu.cn
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Abstract

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.

Keywords

cumulative entropycumulative residual entropygoodness of fitinverse Rayleigh distributionpower studyprogressively type-II censoring scheme
Type
Research Article
Information
Probability in the Engineering and Informational Sciences , Volume 35 , Issue 3 , July 2021 , pp. 631 - 649
Copyright
© Cambridge University Press 2020

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