Hailin Sang

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Magda PeligradTaft Professor University of CincinnatiVerified email at ucmail.uc.edu
Xin DangUniversity of MississippiVerified email at olemiss.edu
Yongli SangUniversity of Louisiana at LafayetteVerified email at louisiana.edu
Yimin XiaoDepartment of Statistics and Probability, Michigan State UniversityVerified email at stt.msu.edu
Evarist GineProfessor of Mathematics, University of ConnecticutVerified email at math.uconn.edu
Kenneth LopianoDirector of Research and Development, Roundtable AnalyticsVerified email at roundtableanalytics.com
Linda J. YoungChief Mathematical Statistician; USDA National Agricultural Statistics Service and Professor Emeritus, University of FloridaVerified email at ufl.edu
Xiequan FanNortheastern University at QinhuangdaoVerified email at univ-ubs.fr
Martial LonglaUniversity of MississippiVerified email at olemiss.edu

Hailin Sang

Associate Professor, The University of Mississippi

Verified email at olemiss.edu - Homepage

Time series/random fields nonparametric statistics empirical processes theory on machine learning


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Exact moderate and large deviations for linear processes M Peligrad, H Sang, Y Zhong, WB Wu Statistica Sinica, 957-969, 2014	22	2014
Uniform asymptotics for kernel density estimators with variable bandwidths E Giné, H Sang Journal of Nonparametric Statistics 22 (6), 773-795, 2010	19	2010
Asymptotic properties of self-normalized linear processes with long memory M Peligrad, H Sang Econometric Theory 28 (3), 548-569, 2012	17	2012
Symmetric Gini covariance and correlation Y Sang, X Dang, H Sang Canadian Journal of Statistics 44 (3), 323-342, 2016	16	2016
On mutual information estimation for mixed-pair random variables A Beknazaryan, X Dang, H Sang Statistics & Probability Letters 148, 9-16, 2019	13	2019
Simultaneous sparse model selection and coefficient estimation for heavy-tailed autoregressive processes H Sang, Y Sun Statistics 49 (1), 187-208, 2015	13	2015
Central limit theorem for linear processes with infinite variance M Peligrad, H Sang Journal of Theoretical Probability 26, 222-239, 2013	13	2013
A rank-based Cramér–von-Mises-type test for two samples J Curry, X Dang, H Sang Brazilian Journal of Probability and Statistics 33 (3), 425-454, 2019	11	2019
Cramér type moderate deviations for random fields A Beknazaryan, H Sang, Y Xiao Journal of Applied Probability 56 (1), 223-245, 2019	10	2019
Exact moderate and large deviations for linear random fields H Sang, Y Xiao Journal of Applied Probability 55 (2), 431-449, 2018	9	2018
A local limit theorem for linear random fields T Fortune, M Peligrad, H Sang Journal of Time Series Analysis 42 (5-6), 696-710, 2021	8	2021
Gini covariance matrix and its affine equivariant version X Dang, H Sang, L Weatherall Statistical Papers 60, 641-666, 2019	8	2019
Kernel entropy estimation for linear processes H Sang, Y Sang, F Xu Journal of Time Series Analysis 39 (4), 563-591, 2018	7	2018
On the estimation of smooth densities by strict probability densities at optimal rates in sup-norm E Giné, H Sang From Probability to Statistics and Back: High-Dimensional Models and …, 2013	7	2013
A statistician teaches deep learning GJ Babu, D Banks, H Cho, D Han, H Sang, S Wang Journal of Statistical Theory and Practice 15, 1-23, 2021	5	2021
Limit theorems for linear random fields with innovations in the domain of attraction of a stable law M Peligrad, H Sang, Y Xiao, G Yang Stochastic Processes and their Applications 150, 596-621, 2022	4	2022
Central limit theorem for the variable bandwidth kernel density estimators J Nakarmi, H Sang Journal of the Korean Statistical Society 47 (2), 201-215, 2018	4	2018
Memory properties of transformations of linear processes H Sang, Y Sang Statistical Inference for Stochastic Processes 20, 79-103, 2017	4	2017
A Berry–Esseen bound of order for martingales S Wu, X Ma, H Sang, X Fan Comptes Rendus. Mathématique 358 (6), 701-712, 2020	3	2020
Shannon entropy estimation for linear processes T Fortune, H Sang Journal of Risk and Financial Management 13 (9), 205, 2020	2	2020

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