报告题目：Volatility Change Point Detection for Tensor Sequence Data

报告时间：2022年7月1日下午 15:00

会议链接：https://meeting.tencent.com/dm/teN9ODIMZHND

会议 ID：430-546-074

主办单位：科研处/437必赢会员中心网页版

主讲人：孔新兵

孔新兵简介：现为南京审计大学统计与数据科学学院教授、博士生导师、院长。主要研究兴趣为高频与髙维数据统计推断与机器学习；在统计学与计量经济学顶级期刊AoS,JASA,Biometrika, JoE, JBES等发表论文20篇；主持国家自然科学基金项目3项，教育部人文社会科学项目1项，参与国家自然科学基金重点项目1项；现为国际统计学会推选会员，国际数理统计学会会员，国家现场统计研究会数据科学与人工智能分会等5个分会常务理事；获第一届统计科学技术进步奖一等奖，江苏省应用统计学会优秀论文一等奖1项；在紫丁香国际应用统计会议、中国北区统计与优化研讨会、江苏省应用统计学会年会、江苏省工业与应用数学学会年会做大会报告；入选国家高层次青年人才计划、江苏省“双创博士”计划、江苏高校“青蓝工程”中青年学术带头人。

摘要：We consider the problem of detecting volatility change points in tensor sequence data. The majority of approaches to the problem focus only on the univariate or multivariate case. Tensor sequence data has not been considered so far. To address this, we propose a new method, which preserves the multi-dimensional data structure and overcomes the curse of dimensionality for covariance parameter estimation. Furthermore, we prove consistency under general conditions. More precisely, the consistency still holds even when the data has non-Gaussian distribution. Extensive numerical studies show that our proposed method improves the estimation accuracy substantially. The detected changes for two real data examples coincide well with both economic growth and recession periods.