统计与决策2024，Vol.40Issue(8)：34-40,7.DOI:10.13546/j.cnki.tjyjc.2024.08.006
基于M估计强混合重尾序列结构变点的鲁棒检验
Robustness Test of M-Estimation-based Change Points of Strongly Mixed Heavy Tail Sequence Structures
摘要
Abstract
In view of the detection of change points of strongly mixed heavy tail sequence structures,and in order to avoid the ordinary least square deviation caused by a heavy-tailed sequence,this paper proposes an M-estimate-based ratio-type statistic to test the change point with a heavy-tailed sequence.Under general constraints,it is proved that the limit distribution of the sta-tistics under the null hypothesis is the functionality of Brownian motion,and the consistency under the alternative hypothesis is ob-tained.Aiming at the experience level distortion caused by sequence dependence,the Block Bootstrap sampling method is used to obtain a more accurate critical value,which effectively improves the inspection efficiency.The numerical simulation results show that the M-estimation-based ratio-type test under the Block Bootstrap sampling method can better control the experience level and reasonable experience potential in the change point detection of strongly mixed heavy-tailed sequence structure.Finally,the feasibility of the proposed test method is verified by a set of exchange rate data.关键词
结构变点/比值型检验/重尾/Block Bootstrap/M估计Key words
structural change point/ratio-type test/heavy tail/Block Bootstrap/M-estimate分类
数理科学引用本文复制引用
朱玲,金浩,乔宝明..基于M估计强混合重尾序列结构变点的鲁棒检验[J].统计与决策,2024,40(8):34-40,7.基金项目
国家自然科学基金资助项目(71473194) （71473194）
陕西省科技厅自然科学基金资助项目(2020JM513) （2020JM513）
