The University of Alabama

Qin Wang

Associate Professor of Applied Statistics

Information Systems, Statistics and Management Science

Phone: 205-384-6085
Email: qwang57@culverhouse.ua.edu

Focus and Current Research

Statistical Learning and Data Mining

Dimension Reduction and Variable Selection​​​​​​​

Statistical Applications​​​​​​​

Education

B.S., University of Science and Technology of China

M.S., University of Science and Technology of China​​​​​​​

Ph.D., The University of Georgia​​​​​​​

Honors Achievements & Affiliations

Dr.  Wang's main research interests focus on dimension reduction and variable selection for Big Data. His methodological research has been published in Biometrika, Statistica Sinica, Journal of Multivariate Analysis, Computational Statistics and Data Analysis, Journal of Statistical Planning and Inference, etc. Dr. Wang has also collaborated with researchers in neuroscience, public health, and other areas. His collaborative work has been published in PLOS ONE, Journal of Antimicrobial Chemotherapy, Drug and Alcohol Dependence, and Psychiatry Research: Neuroimaging. Dr. Wang currently serves as an Associate Editor for the Journal of Statistics and Probability Letters. Before joining UA, Dr. Wang was a faculty member at Virginia Commonwealth University. Dr. Wang's research was supported by funding from the National Institute of Health.

Selected Publications

Xue, Y., Wang, Q. and Yin, X., (2018). A unified approach to sufficient dimension reduction. Journal of Statistical Planning and Inference, 197, 168-179.

Lian, H. and Wang, Q. (2016). Kernel additive sliced inverse regression. Statistica Sinica, 26, 527-546.​​​​​​​

Wang, Q., Yin, X. and Critchley, F. (2015). Dimension reduction based on Hellinger integral. Biometrika, 102, 95-106.​​​​​​​

Wu, R. and Wang, Q. (2012). Shrinkage estimation for linear regression with ARMA errors. Journal of Statistical Planning and Inference, 142, 2136-2148.​​​​​​​

Wang, Q. and Yin, X. (2008). A nonlinear multi-dimensional variable selection method for high dimensional data: Sparse MAVE. Computational Statistics and Data Analysis, 52, 4512-4520.​​​​​​​