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Extreme value theory in mixture distributions and a statistical method to control the possible bias

Title
Extreme value theory in mixture distributions and a statistical method to control the possible bias
Authors
Gwak W.Goo H.Choi Y.H.Ahn J.Y.
Ewha Authors
안재윤
SCOPUS Author ID
안재윤scopus
Issue Date
2016
Journal Title
Journal of the Korean Statistical Society
ISSN
1226-3192JCR Link
Citation
vol. 45, no. 4, pp. 581 - 594
Keywords
Extreme behaviorGeneralized Extreme distributionMixture distributionPrecipitation
Publisher
Korean Statistical Society
Indexed
SCIE; SCOPUS; KCI WOS scopus
Abstract
In this paper, extreme behaviors of a mixture distribution are analyzed. We investigate some cases where the mixture distributions are in the proper domain of attraction so that the extreme value of mixture distributions converges to the proper Generalized Extreme Value distribution (GEV). However, in general, there is no guarantee that the distribution of the data is in the proper maximum domain of attraction. Furthermore, since the convergence rate can be slow even with guaranteed asymptotic convergence, GEV estimation method might provide a biased estimation, as shown in Choi et al. (2014). The paper provides a safe method to control the quality of the quantile estimator in extreme values. © 2016 The Korean Statistical Society
DOI
10.1016/j.jkss.2016.04.003
Appears in Collections:
자연과학대학 > 통계학전공 > Journal papers
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