Marshall–Olkin distribution: parameter estimation and application to cancer data

Yeşim Güney, Yetkin Tuaç and Olcay Arslan

Journal of Applied Statistics, 2017, vol. 44, issue 12, 2238-2250

Abstract: In this study, as alternatives to the maximum likelihood (ML) and the frequency estimators, we propose robust estimators for the parameters of Zipf and Marshall–Olkin Zipf distributions. A small simulation study is given to illustrate the performance of the proposed estimators. We apply the proposed estimators to a real data set from cancer research to illustrate the performance of the proposed estimators over the ML, moments and frequency estimators. We observe that the robust estimators have superiority over the frequency estimators based on classical sample mean.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:44:y:2017:i:12:p:2238-2250

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DOI: 10.1080/02664763.2016.1252730

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