报告人：陈敏
报告地点：数学与统计学院四楼报告厅
报告时间：2019年10月24日星期四15:00-16:00
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.
主讲人简介：
陈敏研究员现担任中国科学院政府行政管理系统分析研究中心主任、全国统计方法应用技术标准化委员会主任委员、《数学与统计管理》主编、中国数学学会副理事长、中国统计教育学会副会长等职，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、大数据分析与处理的统计理论和算法研究。
