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The Kumaraswamy distribution: median-dispersion re-parameterizations for regression modeling and simulation-based estimation

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Abstract

The Kumaraswamy distribution is very similar to the Beta distribution, but has the important advantage of an invertible closed form cumulative distribution function. The parameterization of the distribution in terms of shape parameters and the lack of simple expressions for its mean and variance hinder, however, its utilization with modeling purposes. The paper presents two median-dispersion re-parameterizations of the Kumaraswamy distribution aimed at facilitating its use in regression models in which both the location and the dispersion parameters are functions of their own distinct sets of covariates, and in latent-variable and other models estimated through simulation-based methods. In both re-parameterizations the dispersion parameter establishes a quantile-spread order among Kumaraswamy distributions with the same median and support. The study also describes the behavior of the re-parameterized distributions, determines some of their limiting distributions, and discusses the potential comparative advantages of using them in the context of regression modeling and simulation-based estimation.

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  1. Center for the Study of Poverty and Inequality, Stanford University, 450 Serra Mall, Building 370, Room 212, Stanford, CA, 94305-2029, USA

    Pablo A. Mitnik & Sunyoung Baek

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  1. Pablo A. Mitnik
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  2. Sunyoung Baek
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Correspondence to Pablo A. Mitnik.

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Mitnik, P.A., Baek, S. The Kumaraswamy distribution: median-dispersion re-parameterizations for regression modeling and simulation-based estimation. Stat Papers 54, 177–192 (2013). https://doi.org/10.1007/s00362-011-0417-y

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  • DOI: https://doi.org/10.1007/s00362-011-0417-y

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