Marshall–Olkin Extended Inverse Weibull Distribution: Different Methods of Estimations

Hassan M. Okasha, Abdulkareem M. Basheer and A. H. El-Baz ()
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Hassan M. Okasha: King AbdulAziz University
Abdulkareem M. Basheer: Damietta University
A. H. El-Baz: Damietta University

Annals of Data Science, 2021, vol. 8, issue 4, No 6, 769-784

Abstract: Abstract The inverse Weibull distribution is successfully applied in many different disciplines e.g., reliability engineering, bioengineering and modeling of survival data. There are lots of statistical and computer science techniques e.g., particle swarm optimization, employed to estimate the parameters of this distribution and its generalization. The suitable methods for estimating the parameters of Marshall–Olkin extended inverse Weibull distribution are specified in this paper. So, the performance of different estimation methods called maximum likelihood, Percentiles, Least squares, Weighted least squares, Cramér–van Mises and Anderson–Darling methods, is compared in terms of the bias and mean squared error through extensive numerical simulations. Also, empirical illustration on real life dataset supported the obtained conclusion.

Keywords: Simulation; Least and weighted least squares estimators; Anderson–Darling estimators; Percentiles estimators; Cramér–van Mises estimators (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s40745-020-00299-5

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