34: Using Sliced Inverse Regression for Dimensionality Reduction in Mental Health Outcome Analysis

Music City Center 

This study explores the utility of Sliced Inverse Regression (SIR) for dimension reduction in analyzing a multivariate endogenous variable related to mental health outcomes. Understanding the complex interdependencies between mental health and a wide array of covariates-spanning demographic, biological, genetic, and physiological domains-presents a significant statistical challenge. Traditional regression models often struggle with multicollinearity and high-dimensional data, limiting their ability to uncover meaningful relationships. By applying SIR, we reduce the dimensionality of the covariate space while preserving key directions that explain variation in the mental health outcome. Our analysis identifies the most influential covariate directions and reveals interpretable subspaces that capture underlying mental health dynamics. Results suggest that combinations of genetic markers, age, socioeconomic status, and physiological metrics play significant roles in mental health variability. The findings highlight SIR's potential to uncover complex nonlinear associations and its value in guiding further research on personalized interventions and targeted mental health treatments.

Sliced Inverse Regression

Mental Health

Dimensionality Reduction 

Mental Health Statistics Section