Random matrix theory in statistics: A review
D Paul, A Aue - Journal of Statistical Planning and Inference, 2014 - Elsevier
We give an overview of random matrix theory (RMT) with the objective of highlighting the
results and concepts that have a growing impact in the formulation and inference of …
results and concepts that have a growing impact in the formulation and inference of …
[PDF][PDF] Sample covariance matrices and high-dimensional data analysis
In a multivariate analysis problem, we are given a sample x1, x2,..., xn of random
observations of dimension p. Statistical methods such as Principal Components Analysis …
observations of dimension p. Statistical methods such as Principal Components Analysis …
Sample eigenvalue based detection of high-dimensional signals in white noise using relatively few samples
RR Nadakuditi, A Edelman - IEEE Transactions on Signal …, 2008 - ieeexplore.ieee.org
The detection and estimation of signals in noisy, limited data is a problem of interest to many
scientific and engineering communities. We present a mathematically justifiable …
scientific and engineering communities. We present a mathematically justifiable …
Spectrum estimation for large dimensional covariance matrices using random matrix theory
N El Karoui - 2008 - projecteuclid.org
Estimating the eigenvalues of a population covariance matrix from a sample covariance
matrix is a problem of fundamental importance in multivariate statistics; the eigenvalues of …
matrix is a problem of fundamental importance in multivariate statistics; the eigenvalues of …
Performance of statistical tests for single-source detection using random matrix theory
This paper introduces a unified framework for the detection of a single source with a sensor
array in the context where the noise variance and the channel between the source and the …
array in the context where the noise variance and the channel between the source and the …
Covariance estimation in high dimensions via Kronecker product expansions
T Tsiligkaridis, AO Hero - IEEE Transactions on Signal …, 2013 - ieeexplore.ieee.org
This paper presents a new method for estimating high dimensional covariance matrices. The
method, permuted rank-penalized least-squares (PRLS), is based on a Kronecker product …
method, permuted rank-penalized least-squares (PRLS), is based on a Kronecker product …
Concentration of measure and spectra of random matrices: Applications to correlation matrices, elliptical distributions and beyond
N El Karoui - 2009 - projecteuclid.org
We place ourselves in the setting of high-dimensional statistical inference, where the
number of variables p in a data set of interest is of the same order of magnitude as the …
number of variables p in a data set of interest is of the same order of magnitude as the …
Detection of the number of superimposed signals using modified MDL criterion: A random matrix approach
A Bazzi, DTM Slock, L Meilhac - 2016 IEEE International …, 2016 - ieeexplore.ieee.org
The problem of estimating the number of superimposed signals using noisy observations
from N antennas is addressed. In particular, we are interested in the case where a low …
from N antennas is addressed. In particular, we are interested in the case where a low …
[图书][B] Cognitive radio communication and networking: Principles and practice
The author presents a unified treatment of this highly interdisciplinary topic to help define the
notion of cognitive radio. The book begins with addressing issues such as the fundamental …
notion of cognitive radio. The book begins with addressing issues such as the fundamental …