Zobrazeno 1 - 10
of 31
pro vyhledávání: '"Minkah, Richard"'
Publikováno v:
In Heliyon August 2023 9(8)
Autor:
Minkah, Richard, de Wet, Tertius
The Peaks-Over Threshold is a fundamental method in the estimation of rare events such as small exceedance probabilities, extreme quantiles and return periods. The main problem with the Peaks-Over Threshold method relates to the selection of threshol
Externí odkaz:
http://arxiv.org/abs/1812.03432
Extreme value analysis in the presence of censoring is receiving much attention as it has applications in many disciplines, including survival and reliability studies. Estimation of extreme value index (EVI) is of primary importance as it is a critic
Externí odkaz:
http://arxiv.org/abs/1709.08720
In extreme value analysis, the extreme value index plays a vital role as it determines the tail heaviness of the underlying distribution and is the primary parameter required for the estimation of other extreme events. In this paper, we review the es
Externí odkaz:
http://arxiv.org/abs/1709.08723
Autor:
Minkah, Richard, de Wet, Tertius
In many statistical problems, several estimators are usually available for interval estimation of a parameter of interest, and hence, the selection of an appropriate estimator is important. The criterion for a good estimator is to have a high coverag
Externí odkaz:
http://arxiv.org/abs/1702.08572
Publikováno v:
Journal of Applied Mathematics; 11/13/2023, p1-11, 11p
Publikováno v:
Communications for Statistical Applications & Methods; Nov2023, Vol. 30 Issue 6, p531-550, 20p
The estimation of extreme quantiles is one of the main objectives of statistics of extremes ( which deals with the estimation of rare events). In this paper, a robust estimator of extreme quantile of a heavy-tailed distribution is considered. The est
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9ec3dc6bddb76fa98c0599de087133fb
https://doi.org/10.31730/osf.io/hf7vk
https://doi.org/10.31730/osf.io/hf7vk
Publikováno v:
Communications in Statistics: Theory & Methods. 2023, Vol. 52 Issue 2, p479-498. 20p.