Vector Generalized Additive Models for Extreme Rainfall Data Analysis: A case study in South Korea

• 영문명: Vector Generalized Additive Models for Extreme Rainfall Data Analysis: A case study in South Korea
• 발행기관: 한국자료분석학회
• 저자명: Hee-Young Kim Su-Hyeon Kim Ji-Yeon Oh
• 간행물 정보: 『Journal of The Korean Data Analysis Society (JKDAS)』Vol.25 No.5, 1595~1607쪽, 전체 13쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2023.10.31

영문 초록

Extreme value theory (EVT) is the branch of statistics concerned with inference about the tails of the distribution function. Accurate estimation of the year return levels of climate extremes is a critical step in the projection of future climate and in engineering design for disaster response. The study of non-stationary behavior in the extremes is important to analyze data in environmental sciences, climate, finance, or sports. The largest order statistics is an extension of the block maxima approach that is used in extreme value modeling. However, in Korea, the scientific literature usually has used the method based on fitting a distribution to an annual maximum. We focus on daily rainfall amounts data sets collected from the Korea Meteorological Administration for Seoul, Busan and Daegu, from 1961 to 2020. First, we fit the GEV df to block maxima data, i.e., annual maximum rainfall. Second, we use the top 10 annual precipitation events. We model, in which both mean and scale parameters of extreme value distribution are modeled as linear or smooth functions of covariate, YEAR, using the vector generalized linear models (VGLMs) and vector generalized additive models (VGAMs).

목차

1. Introduction

2. Extreme Value Models

3. Vector generalized linear and additive models

4. Data Analysis

5. Conclusion

References

키워드

Generalized extreme value distribution vector generalized linear models vector generalized additive models Precipitation