Bayesian estimation ofg-and-k distributions using MCMC
Copyright policy )Bayesian estimation ofg-and-k distributions using MCMC
The generalized \(g\)-and-\(k\) distribution can be characterized by it's quantile function \[ Q(u)=A+B\left( 1+c{1-e^{-gz_u}\over 1+e^{-gz_u}} \right)z_u(1+z_u^2)^k, \] where \(A\) and \(B\) are location and scale parameters, \(g\in R\) is a measure of skewness, \(k>-0.5\) is a measure of kurtosis, \(c=0.8\), and \(z_u\) is the \(u\)-th quantile of the standard normal distribution. The authors describe a Bayes technique for estimation of \((A,B,g,k)\). Markov chain Monte Carlo with a Metropolis-Hastings sampler is used for the posterior evaluation. Results of simulations and application to medical data are presented.
- Queensland University of Technology Australia
- University of Queensland Australia
- University of Newcastle Australia Australia
- University of Queensland Australia
- University of Queensland Australia
MCMC, Mcmc, Metropolis-Hastings sampler, Bayesian Estimation, Statistics & Probability, G-and-k Distributions, Bayesian inference, posterior distribution, Point estimation, 310, 230203 Statistical Theory, 780101 Mathematical sciences, Markov chain Monte Carlo, C1, Numerical analysis or methods applied to Markov chains, g-and-k Distributions, generalized \(g\)-and-\(k\) distributions, Generalised Distributions
MCMC, Mcmc, Metropolis-Hastings sampler, Bayesian Estimation, Statistics & Probability, G-and-k Distributions, Bayesian inference, posterior distribution, Point estimation, 310, 230203 Statistical Theory, 780101 Mathematical sciences, Markov chain Monte Carlo, C1, Numerical analysis or methods applied to Markov chains, g-and-k Distributions, generalized \(g\)-and-\(k\) distributions, Generalised Distributions
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