Confidence Intervals of the Generalized Pareto Distribution Parameters Based on Upper Record Values

In this paper, we proposed a new efficient approach to construct confidence intervals for the location and scale parameters of the generalized Pareto distribution (GPD) when the shape parameter is known. The superiority of our method is that the distributions of pivots are exact, but not approximate distributions. The proposed interval estimation provides the shortest interval for the GPD parameter whether or not the confident distribution of the pivot is symmetric. We first estimate the location and scale parameters of the GPD using least squares and then, construct confidence intervals based on the equal probability density principle. The results of various simulation studies illustrate that our interval estimators show the better performance than competing method.