首页> 中文期刊> 《系统科学与复杂性:英文版》 >Monitoring Mean and Variance Change-Points in Long-Memory Time Series

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

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.

著录项

相似文献

  • 中文文献
  • 外文文献
  • 专利

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号