时 间：2019年9月5日(周四)上午10:00-11:00
地 点：中山北路校区理科大楼A1714会议室
题 目：Semi-parametric inference for large-scale data with non-stationary non-Gaussian temporally dependent noises
主讲人：Min Chen Academy of Mathematics and Systems Science, CAS
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.
报告人简介：
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。现任中国科学院政府行政管理系统分析研究中心主任。《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，中国现场统计研究会经济与金融分会理事长，全国统计方法应用技术标准化委员会主任。曾任中国科学院数学与系统科学研究院任副院长。
主要研究方向包括金融统计理论与方法，非平稳、非线性与高频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，函数数据统计分析的理论和方法，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SCI、EI和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160余篇。
