Hailin Sang

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Magda PeligradTaft Professor University of CincinnatiPatvirtintas el. paštas ucmail.uc.edu
Xin DangUniversity of MississippiPatvirtintas el. paštas olemiss.edu
Yongli SangUniversity of Louisiana at LafayettePatvirtintas el. paštas louisiana.edu
Yimin XiaoDepartment of Statistics and Probability, Michigan State UniversityPatvirtintas el. paštas stt.msu.edu
Evarist GineProfessor of Mathematics, University of ConnecticutPatvirtintas el. paštas math.uconn.edu
Kenneth LopianoDirector of Research and Development, Roundtable AnalyticsPatvirtintas el. paštas roundtableanalytics.com
Linda J. YoungChief Mathematical Statistician; USDA National Agricultural Statistics Service and Professor Emeritus, University of FloridaPatvirtintas el. paštas ufl.edu
Xiequan FANNortheastern University at QinhuangdaoPatvirtintas el. paštas univ-ubs.fr
Martial LonglaUniversity of MississippiPatvirtintas el. paštas olemiss.edu

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Hailin Sang

Professor, The University of Mississippi

Patvirtintas el. paštas olemiss.edu - Pagrindinis puslapis

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


Pavadinimas Rūšiuoti pagal šaltinius Rūšiuoti pagal metus Rūšiuoti pagal pavadinimą	Cituota Cituota	Metai
Exact moderate and large deviations for linear processes M Peligrad, H Sang, Y Zhong, WB Wu Statistica Sinica, 957-969, 2014	24	2014
Uniform asymptotics for kernel density estimators with variable bandwidths E Giné, H Sang Journal of Nonparametric Statistics 22 (6), 773-795, 2010	20	2010
Symmetric Gini covariance and correlation Y Sang, X Dang, H Sang Canadian Journal of Statistics 44 (3), 323-342, 2016	19	2016
Asymptotic properties of self-normalized linear processes with long memory M Peligrad, H Sang Econometric Theory 28 (3), 548-569, 2012	18	2012
On mutual information estimation for mixed-pair random variables A Beknazaryan, X Dang, H Sang Statistics & Probability Letters 148, 9-16, 2019	15	2019
Central limit theorem for linear processes with infinite variance M Peligrad, H Sang Journal of Theoretical Probability 26, 222-239, 2013	15	2013
Simultaneous sparse model selection and coefficient estimation for heavy-tailed autoregressive processes H Sang, Y Sun Statistics 49 (1), 187-208, 2015	13	2015
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	12	2019
Gini covariance matrix and its affine equivariant version X Dang, H Sang, L Weatherall Statistical Papers 60, 641-666, 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	11	2019
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	10	2021
Exact moderate and large deviations for linear random fields H Sang, Y Xiao Journal of Applied Probability 55 (2), 431-449, 2018	9	2018
Kernel entropy estimation for linear processes H Sang, Y Sang, F Xu Journal of Time Series Analysis 39 (4), 563-591, 2018	8	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	6	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	5	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
Error analysis of generative adversarial network M Hasan, H Sang arXiv preprint arXiv:2310.15387, 2023	2	2023

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