Journal
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
Volume 28, Issue 2, Pages 147-165Publisher
SPRINGER
DOI: 10.1007/s00477-013-0705-2
Keywords
Flood; Extreme quantile; Bias reduction; Heavy tailed distribution; Order statistics; Weissman estimator
Categories
Funding
- Natural Sciences and Engineering Research Council (NSERC) of Canada
- Canada Research Chair Program
Ask authors/readers for more resources
Parametric models are commonly used in frequency analysis of extreme hydrological events. To estimate extreme quantiles associated to high return periods, these models are not always appropriate. Therefore, estimators based on extreme value theory (EVT) are proposed in the literature. The Weissman estimator is one of the popular EVT-based semi-parametric estimators of extreme quantiles. In the present paper we propose a new family of EVT-based semi-parametric estimators of extreme quantiles. To built this new family of estimators, the basic idea consists in assigning the weights to the k observations being used. Numerical experiments on simulated data are performed and a case study is presented. Results show that the proposed estimators are smooth, stable, less sensitive, and less biased than Weissman estimator.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available