The generalized Pareto distribution (GPD) is a flexible parametric model commonly used in financial modeling. Maximum likelihood estimation (MLE) of the GPD was proposed by Grimshaw (1993). Maximum likelihood estimation of the GPD for censored data is developed, and a goodness-of-fit test is constructed to verify an MLE algorithm in R and to support the model-validation step. The algorithms were composed in R. Grimshaw’s algorithm outperforms functions available in the R package ‘gPdtest’. A simulation study showed the MLE method for censored data and the goodness-of-fit test are both reliable.
In the original published version of this article, the affiliation for the third author was incorrectly given as "University of North Carolina at Chapel Hill” instead of "North Dakota State University". This has been corrected.
Pham, M. H., Tsokos, C., & Choi, B.-J. (2018). Maximum likelihood estimation for the generalized Pareto distribution and goodness-of-fit test with censored data. Journal of Modern Applied Statistical Methods, 17(2), eP2608. doi: 10.22237/jmasm/1553261471