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

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  • Yeşim Güney
  • Yetkin Tuaç
  • Olcay Arslan

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.

Suggested Citation

  • Yeşim Güney & Yetkin Tuaç & Olcay Arslan, 2017. "Marshall–Olkin distribution: parameter estimation and application to cancer data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(12), pages 2238-2250, September.
    • Handle: RePEc:taf:japsta:v:44:y:2017:i:12:p:2238-2250
    DOI: 10.1080/02664763.2016.1252730
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    1. Duarte-López, Ariel & Pérez-Casany, Marta & Valero, Jordi, 2020. "The Zipf–Poisson-stopped-sum distribution with an application for modeling the degree sequence of social networks," Computational Statistics & Data Analysis, Elsevier, vol. 143(C).

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