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Generalised Smooth Tests of Goodness of Fit Utilising L‐moments |
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| Authors: | O Thas JCW Rayner J De Neve |
| Institution: | 1. Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Ghent, Belgium;2. National Institute for Applied Statistics Research Australia (NIASRA), School of Mathematics and Applied Statistics, University of Wollongong, Wollongong, NSW, Australia;3. School of Mathematical and Physical Sciences, University of Newcastle, Newcastle, NSW, Australia |
| Abstract: | In this paper we present a semiparametric test of goodness of fit which is based on the method of L‐moments for the estimation of the nuisance parameters. This test is particularly useful for any distribution that has a convenient expression for its quantile function. The test proceeds by investigating equality of the first few L‐moments of the true and the hypothesised distributions. We provide details and undertake simulation studies for the logistic and the generalised Pareto distributions. Although for some distributions the method of L‐moments estimator is less efficient than the maximum likelihood estimator, the former method has the advantage that it may be used in semiparametric settings and that it requires weaker existence conditions. The new test is often more powerful than competitor tests for goodness of fit of the logistic and generalised Pareto distributions. |
| Keywords: | generalised Pareto distribution logistic distribution order statistics quantile function |