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  • 统计与管理学院2018年学术报告第23期

    【主 题】 Robust Graph Change-point Detection for Brain Evolvement Study

    【报告人】 汪洪浪 助教授

    印第安纳大学-普渡大学,印第安纳波利斯分校 (IUPUI)

    【时 间】 2018年06月13日(星期三)15:00-16:00

    【地 点】 上海财经大学统计与管理学院大楼1208会议室

    摘 要】This paper studies brain structural evolvement from resting-state functional magnetic resonance imaging. The brain structure is characterized by a series of Gaussian graphical models, and we propose a robust data-driven method for inferring the structural changes of multiple graphs. The graphs correspond to different subjects, are aligned by, e.g., the ages of the subjects, and need to be estimated from the subject level data. We propose to estimate the structural changes of these graphs through a three-step procedure. First, we employ a kernel-smoothing approach to estimate multiple graphs at different ages simultaneously. Secondly, we summarize graphical information, such as the number of edges, global and local efficiency, for each estimated graph, and align them as a curve. Lastly, we propose a robust least-absolute-deviation (LAD) type penalization procedure with the fused Lasso (FL) penalty, named LAD-FL, to infer the change-points in those graph summary metrics. Our method is theoretically well understood, and results show that it could effectively capture the brain evolvement pattern.

    嘉宾简介】印第安纳大学-普渡大学,印第安纳波利斯分校 (IUPUI)统计助理教授,研究领域: 纵向数据和函数型数据的统计分析、高维数据的统计推断与应用、非参与半参统计分析、经验似然统计推断方法及应用、统计遗传与基因组学