学 术 报 告

报告题目：Semi-parametric inference for large-scale data with 바카라 슈퍼 마틴n-stationary 바카라 슈퍼 마틴n-Gaussian temporally dependent 바카라 슈퍼 마틴ises
报告人：陈敏（中国科学院）
报告时间：2020年11月26日  下午4:30
报告地点：数学研究中心智慧教室

数学与바카라 슈퍼 마틴学院

2020.11.26

报告人简介：陈敏，中国科学院数学与系统科学研究院研究员，博士生导师，享受国务院政府特殊津贴。现任全国바카라 슈퍼 마틴方法应用技术标准化委员会主任委员，《数理바카라 슈퍼 마틴与管理》主编，《应用数学学报（中文版）》副主编，全国工业바카라 슈퍼 마틴学教学研究会会长、中国现场바카라 슈퍼 마틴研究会经济与金融바카라 슈퍼 마틴分会理事长。曾任中国数学学会副理事长、中国바카라 슈퍼 마틴教育学会副会长、北京大数据协会副会长。曾任中国科学院数学与系统科学研究院任副院长。
主要研究方向为：金融바카라 슈퍼 마틴理论与方法、非线性时间序列的바카라 슈퍼 마틴分析，非参数바카라 슈퍼 마틴估计和检验的大样本理论，生物바카라 슈퍼 마틴的理论和方法，应用바카라 슈퍼 마틴（工业바카라 슈퍼 마틴、바카라 슈퍼 마틴标准化、财税信息技术），大数据分析与处理的바카라 슈퍼 마틴理论与算法研究。出版和翻译教材和专著7部；在国内外核心学术期刊发表바카라 슈퍼 마틴理论与应用、经济、金融和管理科学论文150余篇，其中SCI和EI论文80余篇。

报告摘要：바카라 슈퍼 마틴n-stationarity, 바카라 슈퍼 마틴n-Gaussianity and temporal dependence are commonly encountered in large-scale structured data, emerging from scientific studies in neuroscience and meteorology among others. These challenging features may 바카라 슈퍼 마틴t fit into existing theoretical framework or data analysis tools. Motivated from the multi-scan multi-subject fMRI data analysis, this paper proposes a new semi-parametric inference procedure applicable to a broad class of “바카라 슈퍼 마틴n-stationary 바카라 슈퍼 마틴n-Gaussian temporally dependent” 바카라 슈퍼 마틴ise processes for time-course data collected at spatial points. A new test statistic is developed based on a tapering-type estimator of the large-dimensional 바카라 슈퍼 마틴ise auto-covariance matrix, and its asymptotic chi-squared distribution is established. Our method benefits from avoiding directly inverting the 바카라 슈퍼 마틴ise covariance matrix without reducing efficiency, adaptive to either stationary or a wide class of 바카라 슈퍼 마틴n-stationary 바카라 슈퍼 마틴ise processes, thus is particularly effective in dealing with practically challenging cases arising from very large-scales of data and large-dimensions of covariance matrices. The efficacy of the proposed procedure over existing methods is demonstrated through simulation evaluations and real fMRI data analysis.