Extreme eigenvalue statistics of m-dependent heavy-tailed matrices

Article Properties
  • DOI (url)
    10.1214/21-aihp1152
  • Publication Date
    2021/11/01
  • Journal
    Annales de l'Institut Henri Poincaré, Probabilités et Statistiques
  • Indian UGC (journal)
  • Refrences
    31
  • Citations
    1
  • Bojan Basrak Department of Mathematics, University of Zagreb, Bijenička 30, Zagreb, Croatia
  • Yeonok Cho Department of Mathematical Sciences, KAIST, Daejeon, South Korea
  • Johannes Heiny Department of Mathematics, Ruhr-University Bochum, Universitätsstraße 150, Bochum, Germany
  • Paul Jung Department of Mathematical Sciences, KAIST, Daejeon, South Korea
Cite
Basrak, Bojan, et al. “Extreme Eigenvalue Statistics of M-Dependent Heavy-Tailed Matrices”. Annales De l’Institut Henri Poincaré, Probabilités Et Statistiques, vol. 57, no. 4, 2021, https://doi.org/10.1214/21-aihp1152.
Basrak, B., Cho, Y., Heiny, J., & Jung, P. (2021). Extreme eigenvalue statistics of m-dependent heavy-tailed matrices. Annales De l’Institut Henri Poincaré, Probabilités Et Statistiques, 57(4). https://doi.org/10.1214/21-aihp1152
Basrak, Bojan, Yeonok Cho, Johannes Heiny, and Paul Jung. “Extreme Eigenvalue Statistics of M-Dependent Heavy-Tailed Matrices”. Annales De l’Institut Henri Poincaré, Probabilités Et Statistiques 57, no. 4 (2021). https://doi.org/10.1214/21-aihp1152.
Basrak B, Cho Y, Heiny J, Jung P. Extreme eigenvalue statistics of m-dependent heavy-tailed matrices. Annales de l’Institut Henri Poincaré, Probabilités et Statistiques. 2021;57(4).
Journal Categories
Science
Mathematics
Science
Mathematics
Probabilities
Mathematical statistics
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Citations
Title Journal Journal Categories Citations Publication Date
Limiting distributions for eigenvalues of sample correlation matrices from heavy-tailed populations The Annals of Statistics
  • Science: Mathematics: Probabilities. Mathematical statistics
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 2022
Citations Analysis
Category Category Repetition
Science: Mathematics: Probabilities. Mathematical statistics1
Science: Mathematics1
The category Science: Mathematics: Probabilities. Mathematical statistics 1 is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Limiting distributions for eigenvalues of sample correlation matrices from heavy-tailed populations and was published in 2022. The most recent citation comes from a 2022 study titled Limiting distributions for eigenvalues of sample correlation matrices from heavy-tailed populations. This article reached its peak citation in 2022, with 1 citations. It has been cited in 1 different journals. Among related journals, the The Annals of Statistics cited this research the most, with 1 citations. The chart below illustrates the annual citation trends for this article.
Citations used this article by year