報告題目：Matrix variate distributions regression models and statistical diagnostics
報 告 人：劉雙喆 教授
所在單位：University of Canberra，Australia
報告時間：2024年10月10日 星期四 14:00-16:00
報告地點：吉林大學中心校區數學樓第二報告廳
校内聯系人：程建華 chengjh@jlu.edu.cn
報告摘要：Matrix variate distributions and matrix regression models are powerful tools for analyzing multivariate data with inherent matrix structure. These methods extend traditional univariate and multivariate techniques to handle more complex data structures, such as those found in genomics, neuroscience, and image analysis. In this talk, we introduce a framework for regression models under matrix variate distributions. We begin by discussing several matrix variate distributions and then explore the general linear model under the matrix variate normal distribution, along with its relevant alternative distributions. Additionally, we touch upon important sensitivity analysis and statistical diagnostics for these models.
報告人簡介：Prof. Shuangzhe Liu is currently the group lead of data science, Faculty of Science and Technology at the University of Canberra in Australia. He obtained his PhD in Econometrics from the Tinbergen Institute, University of Amsterdam, the Netherlands, specializing in matrix differential calculus, multivariate analysis, and statistical learning. His extensive expertise is evidenced by his various publications in prestigious journals in the fields of mathematics, statistics, and related areas. Additionally, he has co-authored a comprehensive book on time series analysis using SAS Enterprise Guide. Demonstrating a strong commitment to advancing statistical and data science knowledge, Prof. Liu actively contributes to the field as an Associate Editor for multiple statistical journals and holds an Editor position at Statistical Papers.
