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Accession Number:
ADP007224
Title:
Calculating Maximum Likelihood Estimators for the Generalized Pareto Distribution,
Corporate Author:
MARYLAND UNIV COLLEGE PARK COLL OF BUSINESS AND MANAGEMENT
Report Date:
1992-01-01
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.
Supplementary Note:
This article is from 'Computing Science and Statistics: Proceedings of the Symposium on Interface Critical Applications of Scientific Computing (23rd): Biology, Engineering, Medicine, Speech Held in Seattle, Washington on 21-24 April 1991,' AD-A252 938, p616-619.
Pages:
0004
File Size:
0.00MB
