Report title: Semi-parametric inference for large-scale data with non-stationary non-Gaussian temporally dependent noises
Reporter: Chen Min, researcher Chinese Academy of Sciences
Reporting time: 14:30-15:30, November 29, 2020
Report location: the first lecture hall
School contact: Zhu Fukang fzhu@jlu.edu.cn
Report Abstract: 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 practi cally 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.
Speaker profile: Chen Min, a second-level researcher and doctoral supervisor of the Academy of Mathematics and Systems Science, Chinese Academy of Sciences. The current director of the Chinese Academy of Sciences Government Administration System Analysis and Research Center. Chairman of the National Committee for Standardization of Applied Technology of Statistical Methods, Chief Editor of "Mathematical Statistics and Management", Deputy Editor of "Journal of Applied Mathematics (Chinese Edition)", editorial board member of "Modernization of Traditional Chinese Medicine". Vice President of the Chinese Mathematical Society, Vice President of the China Association for Statistics Education, Vice President of the Beijing Big Data Association. Served as the vice president of the Academy of Mathematics and Systems Science, Chinese Academy of Sciences, and enjoyed the special government allowance of the State Council. The main research directions are: financial statistics theory and methods, statistical analysis of nonlinear time series, large sample theory of non-parametric statistical estimation and testing, theories and methods of biostatistics, applied statistics (industrial statistics, statistical standardization, fiscal and taxation information technology) , Statistical theory and algorithm research of big data analysis and processing. Published and translated 7 textbooks and monographs; published more than 130 papers on statistical theory and application, economics, finance and management science in core academic journals at home and abroad, including more than 90 SCI and EI papers.
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