Approximation of Beta-Jacobi Ensembles by Beta-Laguerre Ensembles

被引:0
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作者
Yutao Ma
Xinmei Shen
机构
[1] Beijing Normal University,School of Mathematical Sciences & Laboratory of Mathematics and Complex Systems of Ministry of Education
[2] Dalian University of Technology,School of Mathematical Sciences
来源
Frontiers of Mathematics | 2023年 / 18卷
关键词
Beta-Laguerre ensembles; beta-Jacobi ensembles; total variation distance; Kullback—Leibler divergence; 60B20; 60F99;
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摘要
Consider beta-Laguerre ensembles μ with parameters m, a1 and beta-Jacobi ensembles γ with parameters m, a1, a2. With the help of tridiagonal models of beta ensembles, we are able to prove that lima2→∞d(L(2aλ),L(μ))=0\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\lim _{{a_2} \to \infty }}d\left( {{\cal L}\left( {2a{\bf{\lambda }}} \right),{\cal L}\left( {\bf{\mu }} \right)} \right) = 0$$\end{document} if a1m=o(a2)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${a_1}m = o\left( {{a_2}} \right)$$\end{document} and lim_a2→∞d(L(2aλ),L(μ))>0\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\underline {\lim } _{{a_2} \to \infty }}\,d\left( {{\cal L}\left( {2a{\bf{\lambda }}} \right),{\cal L}\left( {\bf{\mu }} \right)} \right) > 0$$\end{document} if lima2→∞a1ma2=σ>0\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\lim _{{a_2} \to \infty }}\,{{{a_1}m} \over {{a_2}}} = \sigma > 0$$\end{document}, by contrast, where a ≔ a1 + a2 and d is total variation distance or Kullback—Leibler divergence. This result improves the approximation in [9].
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页码:225 / 252
页数:27
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