World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
32
Citations
10956
World Ranking
3097
National Ranking
1238

Overview

Jack W. Silverstein is affiliated with North Carolina State University in the United States. Their academic work primarily spans the fields of mathematics and computer science, with significant contributions to subfields such as statistics and probability, mathematical physics, signal processing, discrete mathematics and combinatorics, and artificial intelligence.

Their research focuses on topics including:

  • Random Matrices and Applications
  • Advanced Algebra and Geometry
  • Advanced Combinatorial Mathematics
  • Spectral Theory in Mathematical Physics
  • Blind Source Separation Techniques
  • Bayesian Methods and Mixture Models
  • Mathematical Dynamics and Fractals

Jack W. Silverstein has contributed articles to several academic venues. Frequent publication venues include:

  • arXiv (Cornell University)
  • Random Matrices Theory and Application
  • Journal of Theoretical Probability
  • Bernoulli
  • International Encyclopedia of Statistical Science

Their recent papers cover a range of topics related to random matrix theory and statistical methodologies. Selected works include:

  • "Analysis of the Limiting Spectral Distribution of Large-dimensional General Information-Plus-Noise-Type Matrices," 2023, Journal of Theoretical Probability
  • "No eigenvalues outside the support of the limiting spectral distribution of large dimensional noncentral sample covariance matrices," 2024, Bernoulli
  • "Weak convergence of a collection of random functions defined by the eigenvectors of large dimensional random matrices," 2022, Random Matrices Theory and Application
  • "Random Matrix Theory," 2025, International Encyclopedia of Statistical Science
  • "Local Convergence of an AMP Variant to the LASSO Solution in Finite Dimensions," 2020, arXiv (Cornell University)

The scientist frequently collaborates with other researchers, with coauthor relationships most commonly involving:

  • Zhidong Bai
  • Huanchao Zhou
  • Jiang Hu
  • Yanting Ma

Best Publications

  • Spectral Analysis of Large Dimensional Random Matrices

    Zhidong Bai;Jack W. Silverstein

  • Distinctive features, categorical perception, and probability learning: some applications of a neural model

    James A. Anderson;Jack W. Silverstein;Stephen A. Ritz;Randall S. Jones

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

    Z. D. Bai;Jack W. Silverstein

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

    Jack W. Silverstein;Z. D. Bai

  • Eigenvalues of large sample covariance matrices of spiked population models

    Jinho Baik;Jack W. Silverstein

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

    Z. D. Bai;Jack W. Silverstein

  • Strong convergence of the empirical distribution of eigenvalues of large dimensional random matrices

    Jack W. Silverstein

  • Analysis of the limiting spectral distribution of large dimensional random matrices

    Jack W. Silverstein;Sang-Il Choi

  • The Smallest Eigenvalue of a Large Dimensional Wishart Matrix

    Jack W. Silverstein

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

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

  • A Deterministic Equivalent for the Analysis of Correlated MIMO Multiple Access Channels

    R Couillet;M Debbah;J W Silverstein

  • On the empirical distribution of eigenvalues of large dimensional information-plus-noise-type matrices

    R. Brent Dozier;Jack W. Silverstein

  • Fundamental Limit of Sample Generalized Eigenvalue Based Detection of Signals in Noise Using Relatively Few Signal-Bearing and Noise-Only Samples

    Raj Rao Nadakuditi;Jack W Silverstein

  • EXACT SEPARATION OF EIGENVALUES OF LARGE DIMENSIONAL SAMPLE COVARIANCE MATRICES

    Z. D. Bai;Jack W. Silverstein

  • Spectral Analysis of Networks with Random Topologies

    Ulf Grenander;Jack W. Silverstein

  • Signal detection via spectral theory of large dimensional random matrices

    J.W. Silverstein;P.L. Combettes

  • Analysis of the limiting spectral distribution of large dimensional information-plus-noise type matrices

    R. Brent Dozier;Jack W. Silverstein

  • No eigenvalues outside the support of the limiting empirical spectral distribution of a separable covariance matrix

    Debashis Paul;Jack W. Silverstein

  • Robust Estimates of Covariance Matrices in the Large Dimensional Regime

    Romain Couillet;Frédéric Pascal;Jack W. Silverstein

  • On the eigenvectors of large dimensional sample covariance matrices

    Jack W. Silverstein

  • The random matrix regime of Maronna's M-estimator with elliptically distributed samples

    Romain Couillet;Frédéric Pascal;Jack W. Silverstein

Frequent Co-Authors

Zhidong Bai
Zhidong Bai Northeast Normal University
Merouane Debbah
Merouane Debbah Khalifa University
James A. Anderson
James A. Anderson University of Minnesota
Patrick L. Combettes
Patrick L. Combettes North Carolina State University
Jinho Baik
Jinho Baik University of Michigan–Ann Arbor
Antonia M. Tulino
Antonia M. Tulino University of Naples Federico II
Stephen L. Campbell
Stephen L. Campbell North Carolina State University
Ulf Grenander
Ulf Grenander Brown University

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