Ph.D., The University of Georgia
M.S., University of Science and Technology of China
B.S., University of Science and Technology of China
Dr. Wang’s main research interests focus on dimension reduction and variable selection for Big Data. His methodological research has been published in journals including Biometrika, Technometrics, Statistica Sinica, Journal of Multivariate Analysis, Computational Statistics and Data Analysis, and Journal of Statistical Planning and Inference. Dr. Wang has also collaborated with researchers in bioinformatics, neuroscience, and public health. His collaborative work has been published in PLOS ONE, Journal of Antimicrobial Chemotherapy, Drug and Alcohol Dependence, and Psychiatry Research: Neuroimaging. Dr. Wang is an elected member of the International Statistical Institute and currently serves as an Associate Editor for the Journal of Statistics and Probability Letters.
Wang, Q. and Yin, X. (2021). Aggregate inverse mean estimation for sufficient dimension reduction. Technometrics, 456-465.
Zhang, J., Wang, Q. and Mays, D. (2021). Robust MAVE through nonconvex penalized regression. Computational Statistics and Data Analysis, 107247.
Wang, Q. and Xue, Y. (2021). An ensemble of inverse moment estimators for sufficient dimension reduction. Computational Statistics and Data Analysis, 107241.
Rekabdarkolaee, H., Wang, Q., Naji, Z. and Fuentus, M. (2020). New Parsimonious Multivariate Spatial Model: Spatial Envelope. Statistica Sinica, 30, 1583-1604.
Wang, Q., Yin, X., Li, B. and Tang, Z. (2020). On aggregate dimension reduction. Statistica Sinica, 30, 1027-1048.
Rekabdarkolaee, H., Boone, E. and Wang, Q. (2017). Robust estimation and variable selection in sufficient dimension reduction. Computational Statistics and Data Analysis, 108, 146-157.
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.