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Department of Mathematics
University of Mississippi

Dr. Sang

Hailin Sang

Associate Professor
Ph.D., University of Connecticut 2008
Hume Hall 325
(662) 915-7398 |
Personal Website

Time series, random fields, nonparametric statistics, robust statistics, self-normalized statistics, empirical processes, moderate or large deviations, survey sampling design and analysis.


  • Simons Foundation Collaboration Grant for Mathematicians (2018-2023), project entitled “On Linear Processes and Linear Random Fields with Long Memory/Heavy Tails”.


  • H. Sang, Y. Sang and F. Xu, Kernel entropy estimation for linear processes, Journal of Time Series Analysis 39 (2018), no. 4, 563-591.
  • H. Sang and Y. Xiao, Exact moderate and large deviations for linear random fields, Journal of Applied Probability 55 (2018), no. 2, 431–449.
  • H. Sang, K. K. Lopiano, D. A. Abreu, A. C. Lamas, P. Arroway and L. J. Young, Adjusting for misclassification: A three-phase sampling approach, Journal of Official Statistics 33 (2017), no. 1, 207-222.
  • Y. Sang, X. Dang, H. Sang, Symmetric Gini covariance and correlation, The Canadian Journal of Statistics 44 (2016), 323-342.
  • M. Peligrad, H. Sang, Y. Zhong, W. B. Wu, Exact moderate and large deviations for linear processes, Statistica Sinica 24 (2014), 957-969.
  • M. Peligrad, H. Sang, Asymptotic properties of self-normalized linear processes with long memory, Econometric Theory 28 (2012), no. 3, 548-569.
  • E. Giné, H. Sang, Uniform asymptotics for kernel density estimators with variable bandwidths, J. Nonparametr. Statist. 22 (2010), no.6, 773-795.