|
|
|
 |
Search published articles |
 |
|
Showing 2 results for Generalized Extreme Value Distribution
Behzad Mahmoudian, Mousa Golalizadeh,
Volume 3, Issue 1 (9-2009)
Abstract
Modeling of extreme responses in presence nonlinear, temporal, spatial and interaction effects can be accomplished with mixed models. In addition, smoothing spline through mixed model and Bayesian approach together provide convenient framework for inference of extreme values. In this article, by representing as a mixed model, smoothing spline is used to assess nonlinear covariate effect on extreme values. For this reason, we assume that extreme responses given covariates and random effects are independent with generalized extreme value distribution. Then by using MCMC techniques in Bayesian framework, location parameter of distribution is estimated as a smooth function of covariates. Finally, the proposed model is employed to model the extreme values of ozone data.
Farnoosh Ashoori, Malihe Ebrahimpour, Abolghasem Bozorgnia,
Volume 9, Issue 2 (2-2016)
Abstract
Distribution of extreme values of a data set is especially used in natural phenomena including flow discharge, wind speeds, precipitation and it is also used in many other applied sciences such as reliability studies and analysis of environmental extreme events. So if one can model the extremal behavior, then the manner of their future behavior can be predicted. This article is devoted to study extreme wind speeds in Zahedan city using maximal generalized extreme value distribution. In this article, we apply four methods to estimate distribution parameters including maximum likelihood estimation, probability weighted moments, elemental percentile and quantile least squares then compare estimates by average scaled absolute error criterion. We also obtain quantiles estimation and confidence intervals. As a part of result, return period of maximum wind speeds are computed.
|
|
