• The Korean Journal of Applied Statistics
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• v.23 no.5
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• pp.835-844
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• 2010

Tools for statistical analysis of extreme values include the classical annual maximum method, the modern threshold method and variants improving the second one. While the annual maximum method is to t th generalized extreme value distribution to the annual maxima of a time series, the threshold method is to the generalized Pareto distribution to the excesses over a high threshold from the series. In this paper we deal with the Poisson-GPD method, a variant of the threshold method with a further assumption that the total number of exceedances follows the Poisson distribution, and apply it to the daily percentage increases and decreases computed from the spot prices of West Texas Intermediate, which were collected from January 4th, 1988 until December 31st, 2009. According to this analysis, the distribution of daily percentage increases as well as decreases turns out to have a heavy tail, unlike the normal distribution, which coincides well with the general phenomenon appearing in the analysis of lots of nowaday nancial data.

• The Journal of the Convergence on Culture Technology
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• v.8 no.1
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• pp.531-536
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• 2022

This paper compared the performance of the parametric method and the nonparametric method when estimating the distribution for the tail of the distribution with heavy tails. For the parametric method, the generalized extreme value distribution and the generalized Pareto distribution were used, and for the nonparametric method, the kernel density estimation method was applied. For comparison of the two approaches, the results of function estimation by applying the block maximum value model and the threshold excess model using daily fine dust public data for each observatory in Seoul from 2014 to 2018 are shown together. In addition, the area where high concentrations of fine dust will occur was predicted through the return level.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2011.06a
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• pp.343-345
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• 2011

대형 여객선은, "a ship is its own best lifeboat"라는 개념을 바탕으로 여객선의 안전성(survivability) 향상을 위한 설계가 요구되고 있으며, 이를 위하여, 사고가 발생하더라도 선박의 자체 추진력으로 안전하게 항구까지 귀항하여야 한다는, 여객선의 안전귀항(SRtP) 이라는 개념을 IMO SOLAS에 적용시켰다. SOLAS의 여객선 안전귀항 관련 조항은, 길이 120m 이상인 선박 또는 3개 이상의 주 수직격벽을 가진 선박으로서 2010년 07월 01일 이후 건조되는 여객선에 적용된다. 여객선 안전귀항 관련 조항은 화재와 침수사고에 적용되며, 사고분계점을 넘지 아니하는 사고가 발생할 경우 자체 추진력으로 여객선의 안전한 귀항을 위하여 사용 가능한 상태로 유지되어야 하는 시스템들에 대한 설계 기준, 사고분계점을 초과하는 화재 사고가 발생하였을 경우 질서 정연한 탈출 및 퇴선을 지원하기 위하여 작동상태의 유지가 요구되는 시스템 설계 기준, 사고분계점에 대한 정의, 사고 발생 후에도 여객 및 승무원의 건강을 유지 확보하기 위한 안전구역에 대한 기준들을 요구하고 있다. 본 연구에서는, 여객선 안전귀항 관련 법규들을 검토하고, 여객선 안전귀항을 위한 시스템들의 능력 평가 방법과 안전귀항 관련 조항 만족을 위한 시스템들의 요구사항들을 검토하였다.

• The Korean Journal of Applied Statistics
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• v.27 no.4
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• pp.655-665
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• 2014

An adequate understanding and response to natural hazards such as heat wave, heavy rainfall and severe drought is required. We apply extreme value theory to analyze these abnormal weather phenomena. It is common for extremes in climatic data to be nonstationary in space and time. In this paper, we analyze summer rainfall data in South Korea using exceedance values over thresholds estimated by quantile regression with location information and time as covariates. We group weather stations in South Korea into 5 clusters and t extreme value models to threshold exceedances for each cluster under the assumption of independence in space and time as well as estimates of uncertainty for spatial dependence as proposed in Northrop and Jonathan (2011).

• Communications for Statistical Applications and Methods
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• v.18 no.6
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• pp.717-732
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• 2011

We consider bootstrap confidence intervals for high quantiles of heavy-tailed distribution. A semi-parametric method is compared with the non-parametric and the parametric method through simulation study.

• Communications for Statistical Applications and Methods
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• v.16 no.5
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• pp.803-812
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• 2009

The application of extreme value theory to financial data is a fairly recent innovation. The classical annual maximum method is to fit the generalized extreme value distribution to the annual maxima of a data series. An alterative modern method, the so-called threshold method, is to fit the generalized Pareto distribution to the excesses over a high threshold from the data series. A more substantial variant is to take the point-process viewpoint of high-level exceedances. That is, the exceedance times and excess values of a high threshold are viewed as a two-dimensional point process whose limiting form is a non-homogeneous Poisson process. In this paper, we apply the two-dimensional non-homogeneous Poisson process model to daily losses, daily negative log-returns, in the data series of KBW/USD exchange rate, collected from January 4th, 1982 until December 31 st, 2008. The main question is how to estimate extreme quantiles of losses such as the 10-year or 50-year return level.

• The Korean Journal of Applied Statistics
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• v.27 no.3
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• pp.461-473
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• 2014

We consider condence intervals for high quantiles of heavy-tailed distribution. The asymptotic condence intervals based on the limiting distribution of estimators are considered together with bootstrap condence intervals. We can also apply a non-parametric, parametric and semi-parametric approach to each of these two kinds of condence intervals. We considered 11 condence intervals and compared their performance in actual coverage probability and the length of condence intervals. Simulation study shows that two condence intervals (the semi-parametric asymptotic condence interval and the semi-parametric bootstrap condence interval using pivotal quantity) are relatively more stable under the criterion of actual coverage probability.