文章基本信息

标题：Estimating Extreme Losses for the Florida Public Hurricane Model
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作者：Sneh Gulati ; Florence George ; Fan Yang 等
期刊名称：Sri Lankan Journal of Applied Statistics
印刷版ISSN：1391-4987
电子版ISSN：2424-6271
出版年度：2014
卷号：5
期号：4
页码：247-271
DOI：10.4038/sljastats.v5i4.7793
语种：English
出版社：The Institute of Applied Statistics, Sri Lanka
摘要：While the world thinks of coastal Florida as a paradise and retirement haven, residents in these areas don’t always agree with that depiction. Living under the threat of hurricanes for six months of the year and paying enormous sums of money for wind storm insurance is not exactly paradise. However this has not deterred people from wanting a piece of paradise and migration to Florida has continued unabated. Exposure has increased significantly along coastal regions causing insurance companies to reevaluate their risks. They still focus on estimation of annual insured loss, but increasingly they want to be prepared for extreme losses. This paper attempts to look at various methods of estimating extreme quantiles of the loss distribution in the Public Hurricane Loss Model. Both nonparametric and parametric models are used to estimate the catastrophic quantiles and then compared for accuracy. We found that the Weibull distribution fitted the data very well compare to simple exponential and GDP distributions.DOI: http://dx.doi.org/10.4038/sljastats.v5i4.7793
关键词：Applied Statistics; Statistics;Catastrophic Events; Insured Loss; Mathematical Models; Probable Maximum Loss