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Some alternative bivariate Kumaraswamy models

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  • Barry C. Arnold
  • Indranil Ghosh

Abstract

In this article we discuss various strategies for constructing bivariate Kumaraswamy distributions. As alternatives to the Nadarajah et al. (2011) bivariate model, four different models are introduced utilizing a conditional specification approach, a conditional survival function approach, and an Arnold–Ng bivariate beta distribution construction approach. Distributional properties for such bivariate distributions are investigated. Parameter estimation strategies for the models are discussed, as are the consequences of fitting two of the models to a particular data set involving the proportion of foggy days at two different airports in Colombia.

Suggested Citation

  • Barry C. Arnold & Indranil Ghosh, 2017. "Some alternative bivariate Kumaraswamy models," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(18), pages 9335-9354, September.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:18:p:9335-9354
    DOI: 10.1080/03610926.2016.1208244
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    Cited by:

    1. Indranil Ghosh & Filipe J. Marques, 2021. "Tail Conditional Expectations Based on Kumaraswamy Dispersion Models," Mathematics, MDPI, vol. 9(13), pages 1-17, June.
    2. Ghosh Indranil, 2019. "On the Reliability for Some Bivariate Dependent Beta and Kumaraswamy Distributions: A Brief Survey," Stochastics and Quality Control, De Gruyter, vol. 34(2), pages 115-121, December.
    3. Indranil Ghosh, 2023. "A New Class of Alternative Bivariate Kumaraswamy-Type Models: Properties and Applications," Stats, MDPI, vol. 6(1), pages 1-21, January.

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