報告題目：Semi-parametric inference for large-scale data with non-stationary non-Gaussian temporally dependent noises
報 告 人：陳敏 研究員 中科院
報告時間：2020年11月29日 14:30-15:30
報告地點：第一報告廳
校内聯系人：朱複康 fzhu@jlu.edu.cn
報告摘要：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.
報告人簡介：陳敏，中國科學院數學與系統科學研究院二級研究員，博士生導師。現任中國科學院政府行政管理系統分析研究中心主任。全國統計方法應用技術标準化委員會主任委員，《數理統計與管理》主編，《應用數學學報（中文版）》副主編，《中醫藥現代化》編委。中國數學學會副理事長、中國統計教育學會副會長、北京大數據協會副會長。曾任中國科學院數學與系統科學研究院任副院長，享受國務院政府特殊津貼。主要研究方向為：金融統計理論與方法、非線性時間序列的統計分析，非參數統計估計和檢驗的大樣本理論，生物統計的理論和方法，應用統計(工業統計、統計标準化、财稅信息技術)，大數據分析與處理的統計理論與算法研究。出版和翻譯教材和專著7部；在國内外核心學術期刊發表統計理論與應用、經濟、金融和管理科學論文130餘篇，其中SCI和EI論文90餘篇。
