Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.13/807708
- Bayesian estimation of quantile distributions
King, R. A. R.;
Mengersen, K. L.
- The University of Newcastle. Faculty of Science & Information Technology, School of Mathematical and Physical Sciences
- 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.
- Statistics and Computing Vol. 19, Issue 2, p. 189-201
- Publisher Link
- Springer Verlag
approximate Bayesian computation;
response time data
- Resource Type
- journal article