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Let $bY =bR+bX$ be an $Mtimes N$ matrix, where $bR$ is a rectangular diagonal matrix and $bX$ consists of $i.i.d.$ entries. This is a signal-plus-noise type model. Its signal matrix could be full rank, which is rarely studied in literature compared with the low rank cases. This paper is to study the extreme eigenvalues of $bYbY^*$. We show that under the high dimensional setting ($M/Nrightarrow cin(0,1]$) and some regularity conditions on $bR$ the rescaled extreme eigenvalue converges in distribution to Tracy-Widom distribution ($TW_1$).
Consider two $p$-variate populations, not necessarily Gaussian, with covariance matrices $Sigma_1$ and $Sigma_2$, respectively, and let $S_1$ and $S_2$ be the sample covariances matrices from samples of the populations with degrees of freedom $T$ and
In this paper, we study a high-dimensional random matrix model from nonparametric statistics called the Kendall rank correlation matrix, which is a natural multivariate extension of the Kendall rank correlation coefficient. We establish the Tracy-Wid
We consider general high-dimensional spiked sample covariance models and show that their leading sample spiked eigenvalues and their linear spectral statistics are asymptotically independent when the sample size and dimension are proportional to each
Consider a standard white Wishart matrix with parameters $n$ and $p$. Motivated by applications in high-dimensional statistics and signal processing, we perform asymptotic analysis on the maxima and minima of the eigenvalues of all the $m times m$ pr
The auto-cross covariance matrix is defined as [mathbf{M}_n=frac{1} {2T}sum_{j=1}^Tbigl(mathbf{e}_jmathbf{e}_{j+tau}^*+mathbf{e}_{j+ tau}mathbf{e}_j^*bigr),] where $mathbf{e}_j$s are $n$-dimensional vectors of independent standard complex components