%0 Journal Article %1 ElAdlouni.etal2007 %A Adlouni, S. El %A Ouarda, T. B. M. J. %A Zhang, X. %A Roy, R. %A Bobée, B. %D 2007 %I AGU %J Water Resources Research %K Likelihood Likelihood:generalized precipitation %N 3 %P W03410 %R 10.1029/2005WR004545 %T Generalized maximum likelihood estimators for the nonstationary generalized extreme value model %U http://dx.doi.org/10.1029/2005WR004545 %V 43 %X The objective of the present study is to develop efficient estimation methods for the use of the GEV distribution for quantile estimation in the presence of nonstationarity. Parameter estimation in the nonstationary GEV model is generally done with the maximum likelihood estimation method (ML). In this work, we develop the generalized maximum likelihood estimation method (GML), in which covariates are incorporated into parameters. A simulation study is carried out to compare the performances of the GML and the ML methods in the case of the stationary GEV model (GEV0), the nonstationary case with a linear dependence of the location parameter on covariates (GEV1), the nonstationary case with a quadratic dependence on covariates (GEV2), and the nonstationary case with linear dependence in both location and scale parameters (GEV11). Simulation results show that the GLM method performs better than the ML method for all studied cases. The nonstationary GEV model is also applied to a case study to illustrate its potential. The case study deals with the annual maximum precipitation at the Randsburg station in California, and the covariate process is taken to be the Southern Index Oscillation.

@article{ElAdlouni.etal2007, abstract = {The objective of the present study is to develop efficient estimation methods for the use of the GEV distribution for quantile estimation in the presence of nonstationarity. Parameter estimation in the nonstationary GEV model is generally done with the maximum likelihood estimation method (ML). In this work, we develop the generalized maximum likelihood estimation method (GML), in which covariates are incorporated into parameters. A simulation study is carried out to compare the performances of the GML and the ML methods in the case of the stationary GEV model (GEV0), the nonstationary case with a linear dependence of the location parameter on covariates (GEV1), the nonstationary case with a quadratic dependence on covariates (GEV2), and the nonstationary case with linear dependence in both location and scale parameters (GEV11). Simulation results show that the GLM method performs better than the ML method for all studied cases. The nonstationary GEV model is also applied to a case study to illustrate its potential. The case study deals with the annual maximum precipitation at the Randsburg station in California, and the covariate process is taken to be the Southern Index Oscillation.}, added-at = {2011-07-21T15:35:45.000+0200}, author = {Adlouni, S. El and Ouarda, T. B. M. J. and Zhang, X. and Roy, R. and Bobée, B.}, biburl = {https://www.bibsonomy.org/bibtex/22925f9903239f22260a4587a9c9b8a11/marsianus}, doi = {10.1029/2005WR004545}, interhash = {4cf8da1b895c56302af18cd137cdddba}, intrahash = {2925f9903239f22260a4587a9c9b8a11}, issn = {00431397}, journal = {Water Resources Research}, keywords = {Likelihood Likelihood:generalized precipitation}, month = mar, number = 3, pages = {W03410}, publisher = {AGU}, timestamp = {2014-10-30T14:54:15.000+0100}, title = {Generalized maximum likelihood estimators for the nonstationary generalized extreme value model}, url = {http://dx.doi.org/10.1029/2005WR004545}, volume = 43, year = 2007 }