统计与管理学院2019年学术报告第1期

【主 题】 Variational Inference for Multiple Correlated Outcomes in Large Scale Data

【报告人】 史兴杰, 副教授

            南京财经大学

【时 间】 2019年1月7日（星期一）10:00-11:00

【地 点】 上海财经大学统计与管理学院大楼1208会议室

【摘 要】For large-scale inference, where multiple correlated outcomes have been measured on samples, a joint analysis strategy, whereby the outcomes are analyzed jointly, can improve statistical power over a single-outcome analysis strategy. There are two questions of interest to be addressed when conducting variable selection with multiple traits. The first question examines whether a feature is significantly associated with any of the outcomes being tested. The second question focuses on identifying the specific variable(s) that is associated with the outcome. Since existing methods primarily focus on the first question, this paper seeks to provide a complementary method that addresses the second question.

In this talk, I will discuss about a novel method, Variational Inference for Multiple Correlated Outcomes (VIMCO), that focuses on identifying the specific response that is associated with the variable, when performing a joint analysis of multiple outcomes, while accounting for correlation among the multiple outcomes. We performed extensive numerical studies and also applied VIMCO to analyze two GWAS datasets. The numerical studies and real data analysis demonstrate that VIMCO improves statistical power over single-trait analysis strategies when the multiple traits are correlated and has comparable performance when the traits are not correlated.