Accession Number : ADP007224
Title : Calculating Maximum Likelihood Estimators for the Generalized Pareto Distribution,
Corporate Author : MARYLAND UNIV COLLEGE PARK COLL OF BUSINESS AND MANAGEMENT
Personal Author(s) : Grimshaw, Scott D.
Report Date : 1992
Pagination or Media Count : 4
Abstract : The Generalized Pareto Distribution (GPD) is a two-parameter family of distributions which can be used to model exceedences over a threshold. Maximum likelihood parameter estimates are preferred since they are asymptotically normal and asymptotically efficient. Numerical methods are required for maximizing the loglikelihood since the minimal sufficient statistics are the order statistics and there is no obvious simplification of the nonlinear likelihood equation. An algorithm is given to compute GPD maximum likelihood estimates by reducing the two-dimensional numerical search for the zeros of the gradient vector to a one-dimensional numerical search.
Descriptors : *STATISTICAL ANALYSIS, ALGORITHMS, EQUATIONS, ESTIMATES, GRADIENTS, MODELS, ONE DIMENSIONAL, ORDER STATISTICS, PARAMETERS, SIMPLIFICATION, STATISTICS, TWO DIMENSIONAL.
Subject Categories : Statistics and Probability
Distribution Statement : APPROVED FOR PUBLIC RELEASE