Results 291 to 300 of about 108,134 (338)

Covariance Matrix Estimation Via Network Structure

SSRN Electronic Journal, 2016
In this article, we employ a regression formulation to estimate the high dimensional covariance matrix for a given network structure. Using prior information contained in the network relationships, we model the covariance as a polynomial function of the symmetric adjacency matrix.
Wei Lan, Zheng Fang, Hansheng Wang, Chih-Ling Tsai   +3 more
openaire   +1 more source

Rank covariance matrix estimation of a partially known covariance matrix

Journal of Statistical Planning and Inference, 2008
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kuljus, Kristi, von Rosen, Dietrich
openaire   +1 more source

Kronecker Structured Covariance Matrix Estimation

2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07, 2007
The estimation of signal covariance matrices is a crucial part of many signal processing algorithms. In some applications, the structure of the problem suggests that the underlying, true, covariance matrix is the Kronecker product of two matrices. Examples of such problems are channel modelling for MIMO communications and signal modelling of EEG data ...
Karl Werner, Magnus Jansson, Petre Stoica   +2 more
openaire   +1 more source

Covariance Matrix Estimation in Complex Surveys

The Egyptian Statistical Journal, 1989
Summary: An estimator of the asymptotic covariance matrix of the vector of second- order sample moments under cluster sampling design is derived by the Taylor expansion method. The form of the estimator under stratified cluster sampling design is obtained as well.
openaire   +2 more sources

Estimation of the Covariance Matrix

2020
This chapter addresses decision-theoretic estimation of an error covariance matrix in a multivariate linear model relative to a Stein-type entropy loss. With a unified treatment for high and low dimensions, some important improving methods of the best scale and the best triangular invariant estimators are discussed by using the residual sum of squares ...
Hisayuki Tsukuma, Tatsuya Kubokawa
openaire   +1 more source

A Robust Heteroskedasticity Consistent Covariance Matrix Estimator

Statistics, 1997
To deal with heteroskedasticity of unknown form, this paper suggests to robustly estimate the regression coefficients and then to implement an heteroskedasticity consistent covariance matrix estimator. The robust regression reduces the sample bias of the heteroskedasticity consistent covariance matrix estimator, and does not require the specification ...
openaire   +3 more sources

Shrinkage approach for EEG covariance matrix estimation

2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, 2010
We present a shrinkage estimator for the EEG spatial covariance matrix of the background activity. We show that such an estimator has some advantages over the maximum likelihood and sample covariance estimators when the number of available data to carry out the estimation is low.
Leandro, Beltrachini, Nicolas, von Ellenrieder, Carlos H, Muravchik   +2 more
openaire   +2 more sources

Covariance Matrix Estimation

2016
Covariance matrix estimation allows the adaptation of Gaussian-based mutation operators to local solution space characteristics.
openaire   +1 more source

Linguistically Described Covariance Matrix Estimation

2017
In this paper we present a covariance matrix estimation method based on linguistically described data samples. The linguistic variable describes a real data samples that could be used for a calculation of the covariance matrix. In most cases, real dataset contains noise samples that manifest as outliers.
Tomasz Przybyła, Tomasz Pander
openaire   +1 more source