An approximate method for generating asymmetric random variables
Communications of the ACM
Numerical maximum likelihood estimation for the g-and-k and generalized g-and-h distributions
Statistics and Computing
Weighted quantile-based estimation for a class of transformation distributions
Computational Statistics & Data Analysis
Numerical maximum log likelihood estimation for generalized lambda distributions
Computational Statistics & Data Analysis
Likelihood-free Bayesian estimation of multivariate quantile distributions
Computational Statistics & Data Analysis
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Use of Bayesian modelling and analysis has become commonplace in many disciplines (finance, genetics and image analysis, for example). Many complex data sets are collected which do not readily admit standard distributions, and often comprise skew and kurtotic data. Such data is well-modelled by the very flexibly-shaped distributions of the quantile distribution family, whose members are defined by the inverse of their cumulative distribution functions and rarely have analytical likelihood functions defined. Without explicit likelihood functions, Bayesian methodologies such as Gibbs sampling cannot be applied to parameter estimation for this valuable class of distributions without resorting to numerical inversion. Approximate Bayesian computation provides an alternative approach requiring only a sampling scheme for the distribution of interest, enabling easier use of quantile distributions under the Bayesian framework. Parameter estimates for simulated and experimental data are presented.