報告題目：Monitoring mean and variance change-points in long-memory time series
報 告 人：陳占壽 教授 青海師範大學
報告時間：2021年6月18日 14:30-15:30
報告地點：騰訊會議 ID：217 884 771會議密碼：0618
校内聯系人：朱複康 fzhu@jlu.edu.cn
報告摘要：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.
報告人簡介：陳占壽，男，漢族，1982年出生，青海師範大學數學與統計學院副院長，教授，博士，南京信息工程大學兼職博導，中組部“西部之光”訪問學者，加拿大英屬哥倫比亞大學訪問學者，青海省“高端創新人才千人計劃”拔尖人才，青海省高校“135高層次人才培養工程”拔尖學科帶頭人，青海省自然科學與工程技術學科帶頭人，省級骨幹教師，校學術委員會委員；主要從事時間序列變點分析，小區域估計，Bootstrap等方面的研究工作，主持完成國家自然科學基金2項，青海省自然科學基金4項；發表科研論文50餘篇，出版學術專著一部，獲青海省自然科學優秀論文三等獎2項。
