题目：Factor Augmented Inverse Regression and Its Application to Microbiome Data Analysis

报告人：王涛 教授(上海交通大学)

地点：致远楼108室

时间：2024年4月19日 10:00-11:00

摘要：We investigate the relationship between count data that inform the relative abundance of features of a composition, 

and factors that influence the composition. We introduce multinomial Factor Augmented Inverse Regression (FAIR) of the 

count vector onto response factors as a general framework for obtaining low-dimensional summaries of the count vector 

that preserve information relevant to the response. By augmenting known response factors with random latent factors, 

FAIR extends multinomial logistic regression to account for overdispersion and general correlations among counts. The 

method of maximum variational likelihood and a fast variational expectation-maximization algorithm are proposed for 

approximate inference based on variational approximation, and the asymptotic properties of the resulting estimator 

are derived. The effectiveness of FAIR is illustrated through application to a microbiome data set.

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