, Volume 13, Issue 3, pp 291–314 | Cite as

Polynomial power-Pareto quantile function models



In this paper we propose a polynomial power-Pareto quantile function model and a Bayesian method for parameters estimation. We also carried out simulation studies and applied our methodology to real data sets empirically. The results show that a quantile function approach to statistical modelling is very flexible due to the properties of quantile functions, and that the combination of a power and a Pareto distribution enables us to model both the main body and the tails of a distribution, even though the mathematical form of the distribution does not exist. Our research also suggests a new approach to studying extreme values based on a whole data set rather than group maximum/minimum or exceedances above/below a proper threshold value.


Bayesian methods Quantile functions Power-Pareto distribution Simulation Speed and stopping distance data Wave and surge data 

AMS 2000 Subject Classifications

62XX 62Fxx 62F15 


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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.School of Mathematics and StatisticsUniversity of PlymouthPlymouthUK

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