Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.13/807708

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Title
Bayesian estimation of quantile distributions
Author/Creator
Allingham, D.;  King, R. A. R.;  Mengersen, K. L.
Institution
The University of Newcastle. Faculty of Science & Information Technology, School of Mathematical and Physical Sciences
Description
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.
Relation
Statistics and Computing Vol. 19, Issue 2, p. 189-201
Publisher Link
http://dx.doi.org/10.1007/s11222-008-9083-x
Date
2009
Publisher
Springer Verlag
Keyword(s)
approximate Bayesian computation;  posterior distribution;  quantile distribution;  response time data
Resource Type
journal article
Identifier
http://hdl.handle.net/1959.13/807708
Identifier
ISSN:0960-3174
Reviewed
Reviewed
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Subject
"Statistics and Computing"
 
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