A GROUP RESTRICTED INFERENCE PROCEDURE FOR CORRELATION COEFFICIENTS

Music City Center 

In many scientific studies, researchers need to test whether correlations among pairs of variables show specific orders across different study groups. For instance, in biomedical research, it may be hypothesized that correlations among certain groups of commensal bacteria decrease monotonically from healthy individuals to those with progressive stages disease, reflecting systematic shifts in biological relationships with health status. We propose a constrained statistical inference procedure to test these order-based hypotheses about correlations to detect systematic changes in correlation patterns. This approach allows for more precise testing of ordered relationships and provides theoretical guarantees on its performance, including robustness and power under different scenarios. We apply this method to several simulated datasets and two real datasets, MACS Cohort HIV-1 data and Breast Cancer Cell Line data.

Correlation coefficient

Constrained Statistical Inference

PAVA algorithm

Group restricted ordering 

International Indian Statistical Association