New tests for exponentiality based on a characterization with random shift

We derive the efficiencies of two new tests for exponentiality which are based on a recent characterization that uses the idea of a random shift. The finite-sample performance of the newly proposed tests is evaluated and compared to other existing tests by means of Monte Carlo simulations. It is fou...

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Bibliographic Details
Published inJournal of statistical computation and simulation Vol. 90; no. 15; pp. 2840 - 2857
Main Authors Allison, J. S., Nikitin, Ya. Yu, Ragozin, I. A., Santana, L.
Format Journal Article
LanguageEnglish
Published Abingdon Taylor & Francis 12.10.2020
Taylor & Francis Ltd
Subjects
Bahadur efficiency
characterization
Computer simulation
Model testing
power
Probability distribution functions
Statistical models
Test of exponentiality
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Summary:We derive the efficiencies of two new tests for exponentiality which are based on a recent characterization that uses the idea of a random shift. The finite-sample performance of the newly proposed tests is evaluated and compared to other existing tests by means of Monte Carlo simulations. It is found that the new tests perform favourably when compared to the other tests. Overall the best performing tests seem to be our new Kolmogorov-Smirnov type test, the score function based test by Cox and Oakes, and the Kolmogorov-Smirnov type test based on the mean residual life. The tests are also applied to a real-world data set with i.i.d. data as well as to simulated data from a Cox-proportional hazards model, where we test whether the so-called Cox-Snell residuals follow a standard exponential distribution.
ISSN:0094-9655
1563-5163
DOI:10.1080/00949655.2020.1791865