Rate My Professor Nedret Billor

Billor's research interests center on robust multivariate data analysis, robust functional data analysis, and outlier detection, encompassing diagnostic methods, regression modeling, multicollinearity, and applications in genomewide association studies and interdisciplinary fields. Her selected publications include "Vegetation Communities in the Mobile-Tensaw’s Tidal Fresh-water Forested Wetlands" (Balder, Anderson, and Billor, 2024, Forests), "Enhancing Electrocardiography Data Classification Confidence: A Robust Gaussian Process Approach" (Nnyaba et al., 2024), "Understanding Post-Surgical Decline in Left Ventricular Function in Primary Mitral Regurgitation" (Zheng et al., 2023, Frontiers in Cardiovascular Medicine), "CW-ICA: An Efficient Dimensionality Determination Method for Independent Component Analysis" (Yi, Billor, Ekstrom, and Zheng, 2023, Scientific Reports), and "Emerging Statistical Methodologies in Twenty-First Century" (Billor and Turkmen, 2019, Springer). Notable earlier work is the BACON algorithm for outlier detection (Billor, Hadi, and Velleman, 2000, Computational Statistics and Data Analysis). Billor has received the Jack B. Brown Endowed Faculty Award (2023), COSAM Service/Outreach Award (2023), Fulbright Specialist Scholar (2016), Dr. Marie Kraska Award for Excellence in Teaching (2014), and election as member of the International Statistical Institute (2012). She serves on editorial boards for Communications in Statistics journals, as country representative for the ISI Committee on Women in Statistics, referee for multiple journals, and has advised numerous Ph.D. and M.S. students.