記述言語：英語  

担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（学術雑誌）  

担当区分：筆頭著者,　責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：Communications in Statistics - Theory and Methods  

Extreme value theory (EVT) provides an elegant mathematical tool for the statistical analysis of rare events. When data are collected from multiple population subgroups, because some subgroups may have less data available for extreme value analysis, a scientific interest of many researchers would be to improve the estimates obtained directly from each subgroup. To achieve this, we incorporate the mixed effects model (MEM) into the regression technique in EVT. In small area estimation, the MEM has attracted considerable attention as a primary tool for producing reliable estimates for subgroups with small sample sizes, i.e., “small areas.” The key idea of MEM is to incorporate information from all subgroups into a single model and to borrow strength from all subgroups to improve estimates for each subgroup. Using this property, in extreme value analysis, the MEM may contribute to reducing the bias and variance of the direct estimates from each subgroup. This prompts us to evaluate the effectiveness of the MEM for EVT through theoretical studies and numerical experiments, including its application to the risk assessment of a number of stocks in the cryptocurrency market.

DOI： 10.1080/03610926.2025.2450769

Scopus

担当区分：筆頭著者,　責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：Statistical Papers  

This study examines the varying coefficient model in tail index regression. The varying coefficient model is an efficient semiparametric model that avoids the curse of dimensionality when including large covariates in the model. In fact, the varying coefficient model is useful in mean, quantile, and other regressions. The tail index regression is not an exception. However, the varying coefficient model is flexible, but leaner and simpler models are preferred for applications. Therefore, it is important to evaluate whether the estimated coefficient function varies significantly with covariates. If the effect of the non-linearity of the model is weak, the varying coefficient structure is reduced to a simpler model, such as a constant or zero. Accordingly, the hypothesis test for model assessment in the varying coefficient model has been discussed in mean and quantile regression. However, there are no results in tail index regression. In this study, we investigate the asymptotic properties of an estimator and provide a hypothesis testing method for varying coefficient models for tail index regression.

DOI： 10.1007/s00362-024-01538-0

Scopus

開催年月日： 2023年8月

記述言語：英語   会議種別：口頭発表（一般）  

開催地：Waseda University, Tokyo, Japan  

開催年月日： 2022年2月

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：Doshisha University, Kyoto, Japan  

開催年月日： 2026年5月

記述言語：日本語   会議種別：口頭発表（一般）  

開催地：広島大学   国名：日本国  

開催年月日： 2026年2月

記述言語：日本語   会議種別：口頭発表（一般）  

開催年月日： 2025年10月

記述言語：日本語   会議種別：口頭発表（一般）  

種別：大会・シンポジウム等