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线上学术报告预告——陈占寿--Monitoring mean and variance change-points in long-memory time series

一、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。

二、报告信息

报告题目:Monitoring mean and variance change-points in long-memory time series

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

注:报告信息来自吉林大学数学学院官网

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