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arXiv 0710.1346math.PRmath.SP
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对数凹分布秩一矩阵和的极限经验测度

On the Limiting Empirical Measure of the sum of rank one matrices with log-concave distribution

Alain Pajor, Leonid Pastur

摘要:

我们认为$n\times n$实对称厄米随机矩阵$H_{n,m}$等于非随机矩阵$H_{n}^{(0)}$矩阵和由$m$ i.i.d.确定的$m$秩一矩阵之和。具有对数凹概率律和独立同分布的各向同性随机向量随机振幅$\{τ_{α}\}_{α=1}^{m}$。这是一个推广的情况下,向量均匀分布在单位球,研究[马琴科-帕斯图尔(1967)]。我们证明,如果$n\to \infty, m\to \infty, m/n\to c\in \lbrack 0,\infty)$和$H_{n}^{(0)}$的经验本征值测度弱收敛,则$H_{n,m}$的经验本征值测度概率收敛到[Marchenko-Pastur(1967)]中找到的非随机极限。

英文摘要:

We consider $n\times n$ real symmetric and hermitian random matrices $H_{n,m}$ equals the sum of a non-random matrix $H_{n}^{(0)}$ matrix and the sum of $m$ rank-one matrices determined by $m$ i.i.d. isotropic random vectors with log-concave probability law and i.i.d. random amplitudes $\{τ_{α}\}_{α=1}^{m}$. This is a generalization of the case of vectors uniformly distributed over the unit sphere, studied in [Marchenko-Pastur (1967)]. We prove that if $n\to \infty, m\to \infty, m/n\to c\in \lbrack 0,\infty)$ and that the empirical eigenvalue measure of $H_{n}^{(0)}$ converges weakly, then the empirical eigenvalue measure of $H_{n,m}$ converges in probability to a non-random limit, found in [Marchenko-Pastur (1967)].

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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