J Yao, S Zheng, ZD Bai - Cambridge UP, New York, 2015 - researchgate.net
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 …
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 …
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 …
P Bianchi, M Debbah, M Maïda… - IEEE Transactions on …, 2011 - ieeexplore.ieee.org
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 …
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 …
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 …
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 …
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 …