Results 91 to 100 of about 192,768 (193)

Jointly Iterative Adaptive Approach Based Space Time Adaptive Processing Using MIMO Radar

IEEE Access, 2018
To solve the problem of large training samples requirement of space time adaptive processing (STAP), a jointly sparse matrices recovery-based method is proposed for clutter plus noise covariance matrix estimation by exploiting the transmitting waveform ...
Weike Feng, Yiduo Guo, Xingyu He, Hanwei Liu, Jian Gong   +4 more
doaj   +1 more source

Flexible multivariate GARCH modeling with an application to international stock markets [PDF]

The goal of this paper is to estimate time-varying covariance matrices. Since the covariance matrix of financial returns is known to change through time and is an essential ingredient in risk measurement, portfolio selection, and tests of asset pricing ...
Michael Wolf, Olivier Ledoit, Pedro Santa Clara   +2 more
core  

Robust estimation of constrained covariance matrices for confirmatory factor analysis

Computational Statistics & Data Analysis, 2010
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Lozeron, ED, Victoria-Feser, MP
openaire   +5 more sources

Application of regularized covariance matrices in logistic regression and portfolio optimization

Scientific Reports
Covariance estimation has widespread applications in various fields such as logistic regression and portfolio optimization. However, in high-dimensional or small-sample scenarios, traditional covariance matrix estimation often encounters the problem of ...
Fang Sun, Xiaoqing Huang
doaj   +1 more source

Speed Estimation of a Double-Star Permanent Magnet Synchronous Motor Using an Optimized Extended Kalman Filter

International Transactions on Electrical Energy Systems
A mathematical model of a double-star permanent magnet synchronous motor is proposed, coupled with an estimator for speed, position, and currents based on an extended Kalman filter. This filter is optimized using a novel methodology.
Badreddine Naas, Lazhari Nezli, Mohamed Elbar, Ievgen Zaitsev   +3 more
doaj   +1 more source

Efficient nonparametric estimation of Toeplitz covariance matrices

Biometrika
Abstract A new efficient nonparametric estimator for Toeplitz covariance matrices is proposed. This estimator is based on a data transformation that translates the problem of Toeplitz covariance matrix estimation to the problem of mean estimation in an approximate Gaussian regression. The resulting Toeplitz covariance matrix estimator is
Klockmann, K, Krivobokova, T
openaire   +5 more sources

L0 Sparse Inverse Covariance Estimation

, 2015
Recently, there has been focus on penalized log-likelihood covariance estimation for sparse inverse covariance (precision) matrices. The penalty is responsible for inducing sparsity, and a very common choice is the convex $l_1$ norm.
Hero III, Alfred O., Marjanovic, Goran
core  

Asymptotically Efficient Estimation of Covariance Matrices with Linear Structure

The Annals of Statistics, 1973
One or more observations are made on a random vector, whose covariance matrix may be a linear combination of known symmetric matrices and whose mean vector may be a linear combination of known vectors; the coefficients of the linear combinations are unknown parameters to be estimated.
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Robust Estimation of Covariance Matrices: Adversarial Contamination and Beyond

Statistica Sinica
We consider the problem of estimating the covariance structure of a random vector $Y\in \mathbb R^d$ from a sample $Y_1,\ldots,Y_n$. We are interested in the situation when $d$ is large compared to $n$ but the covariance matrix $Σ$ of interest has (exactly or approximately) low rank.
Minsker, Stanislav, Wang, Lang
openaire   +3 more sources

Asymptotics of estimators for structured covariance matrices

Journal of Multivariate Analysis
We show that the limiting variance of a sequence of estimators for a structured covariance matrix has a general form that appears as the variance of a scaled projection of a random matrix that is of radial type and a similar result is obtained for the corresponding sequence of estimators for the vector of variance components.
openaire   +2 more sources