Available at: https://digitalcommons.calpoly.edu/theses/3312
Date of Award
6-2026
Degree Name
MS in Statistics
Department/Program
Statistics
College
College of Science and Mathematics
Advisor
Kelly Bodwin
Advisor Department
Statistics
Advisor College
College of Science and Mathematics
Abstract
When comparing data sampled under several different conditions, we often want to study not only the overall treatment effect but also how treatments influence the relationships among variables. Differential Correlation Mining (DCM) is an existing method for identifying groups of variables whose average pairwise correlation differs significantly between exactly two sampling conditions. We extend this framework to the setting of three or more treatments, where the goal is to identify variable sets whose pairwise correlation structure varies across all conditions simultaneously. Our proposed method, VSAT for three or more treatments, generalizes the DCM search procedure using a multi-treatment hypothesis test to iteratively update the variable set of interest, producing sets whose members are more strongly correlated under some conditions than others. We demonstrate the effectiveness of this extension through simulation studies and a real data application, showing that it recovers known differential correlation structure and provides a single unified summary of community-level association patterns across all treatment groups.