Selected publications by topics

Functional data analysis

• Distribution-on-scalar Single-index Quantile Regression Model for Handling Tumor Heterogeneity
  X.C. Zhou, S. Ding, J. Wang, R. Liu, L.L. Kong & C. Huang
  Techonometrics, 2025+, https://doi.org/10.1080/00401706.2024.2441686

• A General Framework for Quantile Estimation with Incomplete Data
  P.S. Han，L.L. Kong，J.W. Zhao & X.C. Zhou
  Journal of the Royal Statistical Society: Series B (Statistical Methodology), 81(2): 305-333, 2019 (Top)

• Shape Mediation Analysis in Alzheimer’s Disease Studies
  X.C. Zhou, M. Yeon, J. Wang, S. Ding, K. Lei, Y.Y. Zhao, R.J. Liu, C. Huang & ADNI
  Statistics in Medicine, 43:5698–5710, 2024.

• Confounder adjustment in single index function-on-scalar regression model
  S.X. Ding, X.C. Zhou, J.G Lin, R.J. Liu & C. Huang
  Electronic Journal of Statistics, 18(2) 5679 - 5714, 2024.

• Empirical likelihood M-estimation for the varying-coefficient model with functional response
  X.C. Zhou, D.H. Kong, M. Pietrosanu, L.L. Kong & R. Karunamuni
  Scandinavian Journal of Statistics, 51:1357–1387, 2024.

• Functional Response Quantile Regression Model
  X.C. Zhou, D.H. Kong, A.B. Kashlak, L.L. Kong, R. Karunamuni & H.T. Zhu
  Statistica Sinica, 33, 2643-2667, 2023.

• Reproducing kernel-based functional linear expectile regression
  M.C. Liu, P. Matthew, P. Liu, B. Jiang, X.C. Zhou & L.L. Kong
  The Canadian Journal of Statistics, 50(1):241-266, 2022.

• Wavelet-based LASSO in functional linear quantile regression
  Y.F. Wang, L.L. Kong, B. Jiang, X.C. Zhou, S.M. Yu, L. Zhang & G. Heo
  Journal of Statistical Computation and Simulation, 89(6): 1111-1130, 2019

Statistical machine learning

• Communication-efficient non-convex distributed learning with error feedback for uplink and downlink
  X.C. Zhou, Le Chang & Jinde Cao
  IEEE Transactions on Neural Networks and Learning Systems, 2025+, https://ieeexplore.ieee.org/document/10327766

• 广义线性模型分布式学习的无损Score检验----多站点心力衰竭EHRs数据分析
  周兴才, 陈慧莹 & 林金官
  数理统计与管理, 2025+ (accepted)

• Efficient distributed transfer learning for large-scale Gaussian graphic models
  X.C. Zhou, H.T. Zheng, H.R. Zhang & C. Huang
  Stat, 13: e70004, 2024.

• Communication-efficient Byzantine-robust distributed inference with error feedback: a trade-off between compression and adversary
  X.C. Zhou, G. Yang, L. Chang & S.G. Lv
  Information Sciences, 678: 121010, 2024.

• More Communication-efficient distributed sparse learning
  X.C. Zhou & G. Yang
  Information Sciences, 668: 120523, 2024.

• Federated Learning with Communication-Efficiency and Privacy-Preserving Counteracting Heterogeneity
  X.C. Zhou & G. Yang
  Information Sciences, 661: 120167, 2024.

• Distributed bootstrap simultaneous inference for high-dimensional quantile regression
  X.C. Zhou Z.Y. Jing & C. Huang
  Mathematics, 12(5), 735, 2024.

• Communication-efficient Byzantine-robust distributed learning with statistical guarantee
  X.C. Zhou, L. Chang, P.F. Xu & S.G. Lv
  Pattern Recognition, 137: 109312, 2023.

• ADMM-Based Differential Privacy Learning for Penalized Quantile Regression on Distributed Functional Data
  X.C. Zhou & Y. Xiang
  Mathematics, 10(16), 2954, 2022.

• Communication-efficient distributed learning for high-dimensional support vector machine
  X.C. Zhou & H. shen
  Mathematics, 10(7), 1029, 2022.

• Discussion of: ‘A review of distributed statistical inference’
  S.G. Lv & X.C. Zhou
  Statistical Theory and Related Fields, 6(2): 105-107, 2022.

• Panel semiparametric quantile regression neural network for electricity consumption forecasting
  X.C. Zhou, J.Y. Wang, H.X. Wang & J.G. Lin
  Ecological Informatics, 67, 101489, 2022.

• Panel quantile regression neural network for electricity consumption forecasting in China: a new framework
  X.C. Zhou & J.Y. Wang
  Energy Sources, Part B: Economics, Planning, and Policy, 16(5): 420-442, 2021

Methodology and theory on Mathematical Statistics and Probability

• Asymptotic properties of wavelet estimators in semiparametric regression models under dependent errors
  X.C. Zhou & J.G. Lin
  Journal of Multivariate Analysis, 122: 251-270, 2013

• Uniform convergence rates for wavelet curve estimation in sup-norm loss
  X.C. Zhou
  Journal of Computational and Applied Mathematics, 400, 113752, 2022.

• Robust wavelet-based estimation for varying coefficient dynamic models under long-dependent structures
  X.C. Zhou & S.G. Lv
  Analysis and Applications, 19(6): 1033-1057, 2021

• The asymptotic properties of scad penalized generalized linear models with adaptive designs
  Q.B. Gao, C.H. Zhu, X.L. Du, X.C. Zhou & D.X. Yin
  Journal of Systems Science & Complexity, 34: 759–773, 2021

• Berry-Esseen bounds for wavelet estimator in time-varying model with censored dependent data
  X.C. Zhou, B.B. Ni, H.X. Wang & X.F. Huang
  Mathematica Slovaca, 69(5), 1213-1232, 2019

• Wavelet estimation in time-varying coefficient models
  X.C. Zhou, B.B. Ni & C.H. Zhu
  Lithuanian Mathematical Journal, 59(2): 276-293, 2019

• Asymptotics of a wavelet estimator in the nonparametric regression model with repeated measurements under a NA error process
  X.C. Zhou & J.G. Lin
  Revista de la Real Academia de Ciencias Exactas, Fisicas y Naturales. Serie A--Matematicas, 109:153-168, 2015

• Empirical likelihood inference in mixture of semiparametric varying coefficient models for longitudinal data with nonignorable dropout
  X.C. Zhou & J.G. Lin
  Statistics, 48: 668-684, 2014

• Empirical likelihood for varying-coefficient semiparametric mixed-effects error-in-variables models with longitudinal data
  X.C. Zhou & J.G. Lin
  Statistical Methods and Applications, 23: 51-69, 2014

• Empirical likelihood inference in mixtures of semiparametric varying coefficient EV models for longitudinal data with nonignorable Dropout
  X.C. Zhou & J.G. Lin
  Journal of the Korean Statistical Society, 42: 215-225, 2013

• Semiparametric regression estimation for longitudinal data in models with martingale difference error’s structure
  X.C. Zhou & J.G. Lin
  Statistics, 47(3): 521-534, 2013

• On complete convergence for strong mixing sequence
  X.C. Zhou & J.G. Lin
  Stochastics, 85(2): 262-271, 2013

• On moments of the maximum of partial sums of moving average processes under dependence assumptions
  X.C. Zhou & J.G. Lin
  Acta Mathematicae Applicatae Sinica, English Series, 27: 691-696, 2011

• Complete moment convergence of moving average processes under ρ-mixing assumption
  X.C. Zhou & J.G. Lin
  Mathematica Slovaca, 61: 979-992, 2011

• Moment consistency of estimators in partially linear models under NA samples
  X.C. Zhou, X.S. Liu & S.H. Hu
  Metrika, 72(2): 415-432, 2010

• The Monte Carlo EM method for estimating multivariate tobit latent variable models
  X.C. Zhou & X.S. Liu
  Jouranl of Statistical Computation & Simulation, 79(9): 1095-1107, 2009

• The EM algorithm for the extended finite mixture of factor analyzers model
  X.C. Zhou & X.S. Liu
  Computational Statistics and Data Analysis, 52(8): 3939-3953, 2008

• The Monte Carlo EM method for estimating multinomial probit latent variable models
  X.C. Zhou & X.S. Liu
  Computational Statistics, 23(2): 277-289, 2008