New classes of tests for the Weibull distribution using Stein’s method in the presence of random right censoring

E. Bothma, J. S. Allison and I. J. H. Visagie ()
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E. Bothma: North-West University
J. S. Allison: North-West University
I. J. H. Visagie: North-West University

Computational Statistics, 2022, vol. 37, issue 4, No 8, 1770 pages

Abstract: Abstract We develop two new classes of tests for the Weibull distribution based on Stein’s method. The proposed tests are applied in the full sample case as well as in the presence of random right censoring. We investigate the finite sample performance of the new tests using a comprehensive Monte Carlo study. In both the absence and presence of censoring, it is found that the newly proposed classes of tests outperform competing tests against the majority of the distributions considered. In the cases where censoring is present we consider various censoring distributions. Some remarks on the asymptotic properties of the proposed tests are included. We present another result of independent interest; a test initially proposed for use with full samples is amended to allow for testing for the Weibull distribution in the presence of censoring. The techniques developed in the paper are illustrated using two practical examples.

Keywords: Goodness-of-fit testing; Hypothesis testing; Random right censoring; Warp-speed bootstrap; Weibull distribution (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s00180-021-01178-0

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