Monitoring Mean and Variance Change-Points in Long-Memory Time Series  

作　　者：CHEN Zhanshou LI Fuxiao ZHU Li XING Yuhong 

机构地区：[1]School of Mathematics and Statistics,Qinghai Normal University,Xining 810008,China [2]Academy of Plateau Science and Sustainability,Xining 810008,China [3]Department of Applied Mathematics,Xi'an University of Technology,Xi'an 710048,China [4]College of Finance,Xingjiang University of Finance and Economics,Urumqi 830012,China

出　　处：《Journal of Systems Science & Complexity》2022年第3期1009-1029,共21页系统科学与复杂性学报（英文版）

基　　金：supported by the National Natural Science Foundation of China under Grant Nos.11661067,11801438,71661028,61966030;the Natural Science Foundation of Qinghai Province under Grant No.2019-ZJ-920。

摘　　要：This paper proposes two ratio-type statistics to sequentially detect mean and variance change-points in the long-memory time series.The limiting distributions of monitoring statistics under the no change-point null hypothesis,alternative hypothesis as well as change-point misspecified hypothesis are proved.In particular,a sieve bootstrap approximation method is proposed to determine the critical values.Simulations indicate that the new monitoring procedures have better finite sample performance than the available off-line tests when the change-point nears to the beginning time of monitoring,and can discriminate between mean and variance change-point.Finally,the authors illustrate their procedures via two real data sets:A set of annual volume of discharge data of the Nile river,and a set of monthly temperature data of northern hemisphere.The authors find a new variance change-point in the latter data.

关 键 词：Change-point monitoring long-memory time series ratio-type statistic sieve bootstrap 

分 类 号：P333[天文地球—水文科学] P413[水利工程—水文学及水资源] O212.1[天文地球—地球物理学]