TY - JOUR
T1 - An improved method of extreme value analysis
AU - Makkonen, Lasse
AU - Tikanmäki, Maria
N1 - Funding Information:
We thank Matti Pajari for inspiration and many fruitful comments. This work was financially supported by Ministry of Environment , Finland and the Academy of Finland, grant 268925.
Funding Information:
We thank Matti Pajari for inspiration and many fruitful comments. This work was financially supported by Ministry of Environment, Finland and the Academy of Finland, grant 268925.
Publisher Copyright:
© 2018 The Authors
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2019/1
Y1 - 2019/1
N2 - An improved method to estimate the probability of extreme events from independent observations is presented. The method, called VWLS, is based on minimizing the variance of order-ranked observations plotted according to their true probability and applying the least squares fitting. We show by Monte-Carlo simulations that this method provides estimates for the extremes that are considerably better than obtained by presently available EVA methods, particularly for small data sets. An additional benefit of VWLS is that its application requires no subjective methodological decisions by the user.
AB - An improved method to estimate the probability of extreme events from independent observations is presented. The method, called VWLS, is based on minimizing the variance of order-ranked observations plotted according to their true probability and applying the least squares fitting. We show by Monte-Carlo simulations that this method provides estimates for the extremes that are considerably better than obtained by presently available EVA methods, particularly for small data sets. An additional benefit of VWLS is that its application requires no subjective methodological decisions by the user.
KW - extremes
KW - extreme value analysis
KW - statistical inference
KW - flood frequency analysis
UR - http://www.scopus.com/inward/record.url?scp=85064404944&partnerID=8YFLogxK
U2 - 10.1016/j.hydroa.2018.100012
DO - 10.1016/j.hydroa.2018.100012
M3 - Article
SN - 2589-9155
VL - 2
JO - Journal of Hydrology X
JF - Journal of Hydrology X
M1 - 100012
ER -