Dr. Hailin Sang is a Professor of Mathematics in the Department of Mathematics at the University of Mississippi.
Research Interests
Dr. Hailin Sang’s research focuses on theory and application of statistics, deep learning and probability.
He is specifically interested in:
- Deep learning, Empirical Processes
- Time Series, Random Fields
- Nonparametric Statistics, Robust Statistics, Self-normalized Statistics
- Survey Sampling Design & Analysis
Biography
Dr. Hailin Sang is a professor in the Department of Mathematics at the University of Mississippi. He earned his Ph.D. in 2008 from University of Connecticut under the supervision of Professor Evarist Giné. Before he joined University of Mississippi in 2012, he worked as a visiting assistant professor or postdoc research fellow at University of Cincinnati, National Institute of Statistical Sciences/Duke University and Indiana University. His research is partially supported by Simons Foundation grant and National Science Foundation (NSF) grant.
Publications
Selected Publications:
The double decent behavior in two layer neural network for binary classification, C. S. Abeykoon, A. Beknazaryan and H. Sang, Journal of Data Science, (2025), 1-19.
Least absolute deviation estimation for AR(1) processes with roots close to unity, N. Ma, H. Sang and G. Yang, Annals of the Institute of Statistical Mathematics, 75 (2023), no. 5, 799-832.
Limit theorems for linear random fields with innovations in the domain of attraction of a stable law, M. Peligrad, H. Sang, Y. Xiao and G. Yang, Stochastic Processes and their Applications, 150 (2022), 596-621.
A Local limit theorem for linear random fields, T. Fortune, M. Peligrad and H. Sang, Journal of Time Series Analysis, 42 (2021), no. 5-6, 696-710.
Cramér type moderate deviations for random fields, A. Beknazaryan, H. Sang and Y. Xiao, Journal of Applied Probability, 56 (2019), no. 1, 223-245.
Kernel entropy estimation for linear processes, H. Sang, Y. Sang and F. Xu, Journal of Time Series Analysis, 39 (2018), no. 4, 563-591.
Symmetric Gini-covariance and correlation coefficient, Y. Sang, X. Dang and H. Sang, The Canadian Journal of Statistics, 44 (2016), 323-342.
Exact moderate and large deviations for linear processes, M. Peligrad, H. Sang, Y. Zhong and W. B. Wu, Statistica Sinica, 24 (2014), 957-969.
Asymptotic properties of self-normalized linear processes with long memory, M. Peligrad and H. Sang, Econometric Theory, 28 (2012), 3, 548-569.
Uniform asymptotics for kernel density estimators with variable bandwidths, E. Giné and H. Sang, Journal of Nonparametric Statistics, 22 (2010), 6, 773-795.
Courses Taught
- Math 1150 Elementary Statistics
- Math 2511 Calculus for Business, Economics, & Accountancy I
- Math 2611 Unified Calculus & Analytic Geometry I
- Math 2612 Unified Calculus & Analytic Geometry II
- Math 2613 Unified Calculus & Analytic Geometry III
- Math 2614 Unified Calculus & Analytic Geometry IV
- Math 3750 Introduction to Statistics I
- Math 4750 Introduction to Statistics II
- Math 5851 Mathematical Statistics I
- Math 5852 Mathematical Statistics II
- Math 6711 Statistical Methods I
- Math 6712 Statistical Methods II
- Math 7751 Advanced Statistics I
- Math 7770 Seminar in Statistics
Education
Ph.D. Mathematics, University of Connecticut (2008)