报告题目：Monitoring mean and variance change-points in long-memory time series
报 告 人：陈占寿 教授 青海师范大学
报告时间：2021年6月18日 14:30-15:30
报告地点：腾讯会议 ID：217 884 771会议密码：0618
校内联系人：朱复康 fzhu@jlu.edu.cn
报告摘要：In this paper, we propose 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, we illustrate our 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. We find a new variance change-point in the latter data.
报告人简介：陈占寿，男，汉族，1982年出生，青海师范大学数学与统计学院副院长，教授，博士，南京信息工程大学兼职博导，中组部“西部之光”访问学者，加拿大英属哥伦比亚大学访问学者，青海省“高端创新人才千人计划”拔尖人才，青海省高校“135高层次人才培养工程”拔尖学科带头人，青海省自然科学与工程技术学科带头人，省级骨干教师，校学术委员会委员；主要从事时间序列变点分析，小区域估计，Bootstrap等方面的研究工作，主持完成国家自然科学基金2项，青海省自然科学基金4项；发表科研论文50余篇，出版学术专著一部，获青海省自然科学优秀论文三等奖2项。
