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Learning Interpretable Mixture of Weibull Distributions—Exploratory Analysis of How Economic Development Influences the Incidence of COVID-19 Deaths

Author

Listed:
  • Róbert Csalódi

    (MTA-PE “Lendület” Complex Systems Monitoring Research Group, Department of Process Engineering, University of Pannonia, Egyetem Street 10, H-8200 Veszprém, Hungary)

  • Zoltán Birkner

    (University Center for Circular Economy Nagykanizsa, University of Pannonia, H-8800 Nagykanizsa, Hungary)

  • János Abonyi

    (MTA-PE “Lendület” Complex Systems Monitoring Research Group, Department of Process Engineering, University of Pannonia, Egyetem Street 10, H-8200 Veszprém, Hungary)

Abstract

This paper presents an algorithm for learning local Weibull models, whose operating regions are represented by fuzzy rules. The applicability of the proposed method is demonstrated in estimating the mortality rate of the COVID-19 pandemic. The reproducible results show that there is a significant difference between mortality rates of countries due to their economic situation, urbanization, and the state of the health sector. The proposed method is compared with the semi-parametric Cox proportional hazard regression method. The distribution functions of these two methods are close to each other, so the proposed method can estimate efficiently.

Suggested Citation

  • Róbert Csalódi & Zoltán Birkner & János Abonyi, 2021. "Learning Interpretable Mixture of Weibull Distributions—Exploratory Analysis of How Economic Development Influences the Incidence of COVID-19 Deaths," Data, MDPI, vol. 6(12), pages 1-11, November.
    • Handle: RePEc:gam:jdataj:v:6:y:2021:i:12:p:125-:d:688926
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    References listed on IDEAS

    as
    1. Castet, Jean-Francois & Saleh, Joseph H., 2010. "Single versus mixture Weibull distributions for nonparametric satellite reliability," Reliability Engineering and System Safety, Elsevier, vol. 95(3), pages 295-300.
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