The Journal of the Convergence on Culture Technology (문화기술의 융합)

The International Promotion Agency of Culture Technology (국제문화기술진흥원)
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Parametric nonparametric methods for estimating extreme value distribution

극단값 분포 추정을 위한 모수적 비모수적 방법

  • 우승현 (한국외국어대학교 통계학과) ;
  • 강기훈 (한국외국어대학교 통계학과)
  • Received : 2021.12.29
  • Accepted : 2022.01.08
  • Published : 2022.01.31
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Abstract

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.

본 논문은 꼬리가 두꺼운 분포의 꼬리부분에 대한 분포를 추정할 경우 모수적 방법과 비모수적 방법의 성능에 대해 비교하였다. 모수적 방법으로는 일반화 극단값 분포와 일반화 파레토 분포를 이용하였고, 비모수적 방법은 커널형 확률밀도함수 추정방법을 적용하였다. 두 접근법의 비교를 위해 2014년부터 2018년까지 서울시 관측소별 일일 미세먼지 공공데이터를 이용하여 블록 최댓값 모형과 분계점 초과치 모형을 적용하여 함수 추정한 결과를 함께 보이고 2년, 5년, 10년의 재현수준을 통해 고농도의 미세먼지가 일어날 지역을 예측하였다.

Keywords

Acknowledgement

이 연구는 2021학년도 한국외국어대학교 교내학술연구비 지원에 의하여 이루어진 것임

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