An ‘apples to apples’ comparison of various tests for exponentiality

J. S. Allison, L. Santana (), N. Smit and I. J. H. Visagie ()
Additional contact information
J. S. Allison: North-West University
L. Santana: North-West University
N. Smit: North-West University
I. J. H. Visagie: University of Pretoria

Computational Statistics, 2017, vol. 32, issue 4, No 2, 1283 pages

Abstract: Abstract The exponential distribution is a popular model both in practice and in theoretical work. As a result, a multitude of tests based on varied characterisations have been developed for testing the hypothesis that observed data are realised from this distribution. Many of the recently developed tests contain a tuning parameter, usually appearing in a weight function. In this paper we compare the powers of 20 tests for exponentiality—some containing a tuning parameter and some that do not. To ensure a fair ‘apples to apples’ comparison between each of the tests, we employ a data-dependent choice of the tuning parameter for those tests that contain these parameters. The comparisons are conducted for various samples sizes and for a large number of alternative distributions. The results of the simulation study show that the test with the best overall power performance is the Baringhaus and Henze test, followed closely by the test by Henze and Meintanis; both tests contain a tuning parameter. The score test by Cox and Oakes performs the best among those tests that do not include a tuning parameter.

Keywords: Bootstrap; Exponential distribution; Goodness-of-fit testing; Tuning parameter (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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DOI: 10.1007/s00180-017-0733-3

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