Department of Mathematics

University of Mississippi

Dr. Sang

Hailin Sang

Assistant Professor
Ph.D., University of Connecticut 2008
Hume Hall 325
(662) 915-7398 | sang@olemiss.edu
Personal Website

TEACHING, Fall 2017
Office Hours: M.W.F. 10:00-11:00 AM.
Math 263-05: T.Th. 2:30-3:45 PM, Hume 201.
Math 671: T.Th. 1:00-2:15 PM, Hume 331.

RESEARCH INTERESTS
Long Memory Time Series/Linear Processes, Time Series Model Selection
Nonparametric Statistics, Asymptotic Statistics, Empirical Processes
Survey Sampling Design and Analysis, Data Mining

SELECTED PUBLICATIONS

  • H. Sang and L. Ge, Further refinement of self-normalized Cramér-type moderate deviations, to appear in ESAIM: P&S
  • X. Dang, H. Sang and L. Weatherall, Gini covariance matrix and its affine equivariant version, to appear in Statistical Papers.
  • 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, to appear in Journal of Official Statistics.
  • Y. Sang, X. Dang, H. Sang, Symmetric Gini covariance and correlation, The Canadian Journal of Statistics 44 (2016), 323-342.
  • H. Sang, Y. Sang, Memory properties of transformations of linear processes, Statistical Inference for Stochastic Processes, 20 (2017), no. 1, 79-103.
  • M. Longla, M. Peligrad and H. Sang, On kernel estimators of density for reversible Markov chains, Statistics and Probability Letters 100 (2015), 149-157.
  • H. Sang, Y. Sun, Simultaneous sparse model selection and coefficient estimation for heavy-tailed autoregressive processes, Statistics 49 (2015), 187-208.
  • M. Peligrad, H. Sang, The self-normalized asymptotic results for linear processes, Asymptotic Laws and Methods in Stochastics, Fields Institute Communications Series 76 (2015), 43-51, Springer.
  • M. Peligrad, H. Sang, Y. Zhong, W. B. Wu, Exact moderate and large deviations for linear processes, Statistica Sinica 24 (2014), 957-969.
  • E. Giné, H. Sang, On the estimation of smooth probability densities by probability densities at the optimal rate in sup-norm loss, IMS Collections, From Probability to Statistics and Back: High-Dimensional Models and Processes 9 (2013) 128-149.
  • M. Peligrad, H. Sang, Central limit theorem for linear processes with infinite variance, J. Theoret. Probab. 26 (2013), no. 1, 222-239.
  • 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.