Extreme value theory in mixture distributions and a statistical method to control the possible bias
- 주제(키워드) Extreme behavior , Generalized Extreme distribution , Mixture distribution , Precipitation , Primary , Secondary
- 등재 SCIE, SCOPUS, KCI등재
- 발행기관 Korean Statistical Society
- 발행년도 2016
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000135495
- 본문언어 영어
- Published As http://dx.doi.org/10.1016/j.jkss.2016.04.003
초록/요약
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.
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