Empirical Likelihood Ratio Based Goodness-of-Fit Test for Generalized Lambda Distribution

Wei Ning


In this paper, we propose a goodness-of-fit test based on the empirical likelihood method for the generalized lambda distribution (GLD) family. Such a nonparametric test approximates the optimal Neyman-Pearson likelihood ratio test under the unknown alternative distribution scenario. The p-value of the test is approximated through the simulations due to the dependency of the test statistic on the data. The test is applied to the roller
data set and the pollen data set to illustrate the testing procedure for the sufficiency of the GLD fittings.


Generalized lambda distribution; Empirical likelihood; Nonparametric; Goodness-of-fit test.

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