在线学术报告 | 李悫风副教授：多模态数据分析的综合因子回归及其推论

摘要

Multimodal data, where different types of data are collected from the same subjects, are fast emerging in a large variety of scientific applications. Factor analysis is commonly used in integrative analysis of multimodal data, and is particularly useful to overcome the curse of high dimensionality and high correlations. However, there is little work on statistical inference for factor analysis based supervised modeling of multimodal data. In this talk, I will discuss a new integrative linear regression model that is built upon the latent factors extracted from multimodal data. I will address three important questions: how to infer the significance of one data modality given the other modalities in the model; how to infer the significance of a combination of variables from one modality or across different modalities; and how to quantify the contribution, measured by the goodness-of-fit, of one data modality given the others. When answering each question, I will explicitly characterize both the benefit and the extra cost of factor analysis. Those questions, to my knowledge, have not yet been addressed despite wide use of factor analysis in integrative multimodal analysis, and our work bridges an important gap.

嘉宾介绍

李悫风，北卡罗来纳大学教堂山分校生物统计系副教授。李教授博士毕业去美国威斯康星大学统计系。2013年至2015年在普林斯顿大学从事博后研究。2015年至今就职于北卡生物统计系。李教授的研究方向包括高维统计分析，数据融合，神经影像分析，以及稳健统计量。他的论文多次发表在JRSSB, JASA, Biometrika, Biometrics等统计学顶级期刊上。他现担任Journal of Nonparametric Statistics的副主编。

狗熊会线上学术报告厅向数据科学及相关领域的学者及从业者开放，非常期待各位熊粉报名或推荐报告人。相关事宜，请联系：常莹，ying.chang@clubear.org。