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

By Pablo A. Mitnik, Su-Gi Baek. Statistical Papers, 2012

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New Properties of the Kumaraswamy Distribution (2013, unknown): “However, in spite of the fact that the Kumaraswamy distribution was introduced in 1980, further theoretical research on the distribution was not developed until very recently (Garg, 2008; Jones, 2009; Mitnik, 2009; Nadarajah, 2008).”
Kumaraswamy autoregressive moving average models for double bounded environmental data (2017, background): “In fact, the lack of tractable expressions for the mean and variance, given by (1) and (2), respectively, has hindered its utilization for modeling purposes (Lemonte et al., 2013; Mitnik and Baek, 2013).”
A quantile parametric mixed regression model for bounded response variables (2017, methodology): “As pointed out by [30], the expressions for E(Y ) and V ar(Y ) make a mean-variance based reparameterization unfeasible.”
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model (2018, methodology): “In the experiments presented in this paper (Section 5) the system uses categorical and continuous uniform distributions in the prior, for which we use, respectively, categorical and mixture of Kumaraswamy [68, 69] distributions as proposals parameterized by the neural network.”