学 术 报 告

报告题目：Semi-parametric inference for large-scale data with non-stationary non-Gaussian temporally dependent noises
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

红世一足666814

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。现任全国统计方法应用技术标准化委员会主任委员，《数理统计与管理》主编，《应用数学学报（中文版）》副主编，全国工业统计学教学研究会会长、中国现场统计研究会经济与金融统计分会理事长。曾任中国数学学会副理事长、中国统计教育学会副会长、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：金融统计理论与方法、非线性时间序列的统计分析，非参数统计估计和检验的大样本理论，生物统计的理论和方法，应用统计（工业统计、统计标准化、财税信息技术），大数据分析与处理的统计理论与算法研究。出版和翻译教材和专著7部；在国内外核心学术期刊发表统计理论与应用、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：Non-stationarity, non-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 not 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 “non-stationary non-Gaussian temporally dependent” noise 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 noise auto-covariance matrix, and its asymptotic chi-squared distribution is established. Our method benefits from avoiding directly inverting the noise covariance matrix without reducing efficiency, adaptive to either stationary or a wide class of non-stationary noise 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.