Rate My Professor Qin Wang

Qin Wang is Professor of Applied Statistics in the Department of Information Systems, Statistics, and Management Science at the Culverhouse College of Business, The University of Alabama. He earned a Ph.D. in Statistics from The University of Georgia, an M.S. in Management Science from the University of Science and Technology of China, and a B.S. in Management Science from the University of Science and Technology of China. His research specializes in dimension reduction and variable selection for big data. Methodological publications appear in Biometrika, Technometrics, Statistica Sinica, Journal of Multivariate Analysis, Computational Statistics and Data Analysis, and Journal of Statistical Planning and Inference. Dr. Wang collaborates with researchers in bioinformatics, neuroscience, and public health, with publications in PLOS ONE, Journal of Antimicrobial Chemotherapy, Drug and Alcohol Dependence, and Psychiatry Research: Neuroimaging.

Representative publications include "Hypothesis testing and power calculations for taxonomic-based human microbiome data" (PLoS ONE, 2012), "A nonlinear multi-dimensional variable selection method for high dimensional data: Sparse MAVE" (Computational Statistics & Data Analysis, 2008), "Medication risk factors associated with healthcare-associated Clostridium difficile infection: a multilevel model case–control study among 64 US academic medical centers" (Journal of Antimicrobial Chemotherapy, 2014), "Robust variable selection through MAVE" (Computational Statistics & Data Analysis, 2013), "Dimension reduction based on Hellinger integral" (Biometrika, 2015), "Kernel additive sliced inverse regression" (Statistica Sinica, 2016), "Robust estimation and variable selection in sufficient dimension reduction" (Computational Statistics & Data Analysis, 2017), "On aggregate dimension reduction" (Statistica Sinica, 2020), "New Parsimonious Multivariate Spatial Model: Spatial Envelope" (Statistica Sinica, 2020), "Aggregate inverse mean estimation for sufficient dimension reduction" (Technometrics, 2021), "An ensemble of inverse moment estimators for sufficient dimension reduction" (Computational Statistics and Data Analysis, 2021), and "Robust MAVE through nonconvex penalized regression" (Computational Statistics and Data Analysis, 2021). An elected member of the International Statistical Institute, Dr. Wang serves as Associate Editor for Statistics and Probability Letters.