%0 Journal Article %1 Zhang.Stephans2009 %A Zhang, Jin %A Stephens, Michael A. %D 2009 %J Technometrics %K %N 3 %T A New and Efficient Estimation Method for the Generalized Pareto Distribution %U http://cat.inist.fr/?aModele=afficheN&cpsidt=21859146 %V 51 %X The generalized Pareto distribution (GPD) is widely used to model extreme values, for example, exceedences over thresholds, in modeling floods. Existing methods for estimating parameters have theoretical or computational defects. An efficient new estimator is proposed, which is computationally easy, free from the problems observed in traditional approaches, and performs well compared with existing estimators. A numerical example involving heights of waves is used to illustrate the various methods and tests of fit are performed to compare them.

@article{Zhang.Stephans2009, abstract = {The generalized Pareto distribution (GPD) is widely used to model extreme values, for example, exceedences over thresholds, in modeling floods. Existing methods for estimating parameters have theoretical or computational defects. An efficient new estimator is proposed, which is computationally easy, free from the problems observed in traditional approaches, and performs well compared with existing estimators. A numerical example involving heights of waves is used to illustrate the various methods and tests of fit are performed to compare them.}, added-at = {2012-03-21T14:54:32.000+0100}, author = {Zhang, Jin and Stephens, Michael A.}, biburl = {https://www.bibsonomy.org/bibtex/2f2247e2c4e5b0646c5ca8cd5f891f2f6/marsianus}, interhash = {6c235df05675b88c60dd7409b1374f5b}, intrahash = {f2247e2c4e5b0646c5ca8cd5f891f2f6}, journal = {Technometrics }, keywords = {}, number = 3, timestamp = {2012-03-21T14:54:32.000+0100}, title = {A New and Efficient Estimation Method for the Generalized Pareto Distribution}, url = {http://cat.inist.fr/?aModele=afficheN&cpsidt=21859146}, volume = 51, year = 2009 }