World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
50
Citations
15061
World Ranking
1057
National Ranking
50

Overview

Zhidong Bai is affiliated with Northeast Normal University in China and has made significant contributions primarily in the field of Mathematics. Their research spans multiple subfields including Statistics and Probability, Mathematical Physics, Discrete Mathematics and Combinatorics, Artificial Intelligence, and Molecular Biology.

The main topics of Bai's research include:

  • Random Matrices and Applications
  • Advanced Combinatorial Mathematics
  • Bayesian Methods and Mixture Models
  • Advanced Algebra and Geometry
  • Statistical Methods and Inference
  • Statistical Methods and Bayesian Inference
  • Stochastic processes and statistical mechanics

Bai has a substantial publication record with frequent appearances in several academic venues. These include:

  • arXiv (Cornell University)
  • Bernoulli
  • Random Matrices Theory and Application
  • Journal of Theoretical Probability
  • Statistics & Probability Letters

Some recent papers authored by or associated with Bai's research group are:

  • "RIS-Enhanced Spectrum Sensing: How Many Reflecting Elements are Required to Achieve a Detection Probability Close to 1?", 2022, IEEE Transactions on Wireless Communications
  • "Modified Pillai's trace statistics for two high-dimensional sample covariance matrices", 2020, Journal of Statistical Planning and Inference
  • "Generalized four moment theorem and an application to CLT for spiked eigenvalues of high-dimensional covariance matrices", 2020, Bernoulli
  • "DiffDomain enables identification of structurally reorganized topologically associating domains", 2024, Nature Communications
  • "Spectrally-Corrected Estimation for High-Dimensional Markowitz Mean-Variance Optimization", 2021, Econometrics and Statistics

Bai has collaborated frequently with several co-authors, including:

  • Jiang Hu
  • Huanchao Zhou
  • Jack W. Silverstein
  • Wenya Luo

Best Publications

  • Spectral Analysis of Large Dimensional Random Matrices

    Zhidong Bai;Jack W. Silverstein

  • CLT for linear spectral statistics of large-dimensional sample covariance matrices

    Z. D. Bai;Jack W. Silverstein

  • METHODOLOGIES IN SPECTRAL ANALYSIS OF LARGE DIMENSIONAL RANDOM MATRICES, A REVIEW

    Z. D. Bai

  • On the empirical distribution of eigenvalues of a class of large dimensional random matrices

    Jack W. Silverstein;Z. D. Bai

  • Limit of the smallest eigenvalue of a large dimensional sample covariance matrix

    Z. D. Bai;Y. Q. Yin

  • No eigenvalues outside the support of the limiting spectral distribution of large-dimensional sample covariance matrices

    Z. D. Bai;Jack W. Silverstein

  • On the limit of the largest eigenvalue of the large dimensional sample covariance matrix

    Y. Q. Yin;Z. D. Bai;P. R. Krishnaiah

  • On detection of the number of signals in presence of white noise

    L C Zhao;P R Krishnaiah;Z D Bai

  • Ranked set sampling

    Zehua Chen;Zhidong Bai;Bimal K. Sinha

  • Corrections to LRT on large-dimensional covariance matrix by RMT

    Zhidong Bai;Dandan Jiang;Jian-Feng Yao;Shurong Zheng

  • Central limit theorems for eigenvalues in a spiked population model

    Zhidong Bai;Jian-feng Yao

  • Large Sample Covariance Matrices and High-Dimensional Data Analysis

    Jianfeng Yao;Shurong Zheng;Zhidong Bai

  • Convergence Rate of Expected Spectral Distributions of Large Random Matrices. Part I. Wigner Matrices

    Z. D. Bai

  • ENHANCEMENT OF THE APPLICABILITY OF MARKOWITZ'S PORTFOLIO OPTIMIZATION BY UTILIZING RANDOM MATRIX THEORY

    Zhidong Bai;Huixia Liu;Wing Keung Wong

  • A note on the largest eigenvalue of a large dimensional sample covariance matrix

    Z. D. Bai;Jack W. Silverstein;Y. Q. Yin

  • LARGE SAMPLE COVARIANCE MATRICES WITHOUT INDEPENDENCE STRUCTURES IN COLUMNS

    Zhidong Bai;Wang Zhou

  • On asymptotics of eigenvectors of large sample covariance matrix

    Z. D. Bai;B. Q. Miao;G. M. Pan

  • EXACT SEPARATION OF EIGENVALUES OF LARGE DIMENSIONAL SAMPLE COVARIANCE MATRICES

    Z. D. Bai;Jack W. Silverstein

  • On sample eigenvalues in a generalized spiked population model

    Zhidong Bai;Jianfeng Yao

  • Convergence Rate of Expected Spectral Distributions of Large Random Matrices. Part II. Sample Covariance Matrices

    Z. D. Bai

  • On detection of the number of signals when the noise covariance matrix is arbitrary

    L C Zhao;P R Krishnaiah;Z D Bai

Frequent Co-Authors

Wing-Keung Wong
Wing-Keung Wong Asian University
Jack W. Silverstein
Jack W. Silverstein North Carolina State University
Feifang Hu
Feifang Hu George Washington University
Xuming He
Xuming He Washington University in St. Louis
Debasis Kundu
Debasis Kundu Indian Institute of Technology Kanpur
William F. Rosenberger
William F. Rosenberger George Mason University
Michael McAleer
Michael McAleer Erasmus University Rotterdam
Ričardas Zitikis
Ričardas Zitikis University of Western Ontario
Luc Devroye
Luc Devroye McGill University
Merouane Debbah
Merouane Debbah Khalifa University

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