Results 271 to 280 of about 3,211,049 (333)

Robust adaptive beamforming via subspace for interference covariance matrix reconstruction

Signal Processing, 2020
Adaptive beamforming may cause performance degradation when model mismatch errors exist. In this paper, we have developed subspace methods for robust adaptive beamforming (RAB).
Xingyu Zhu, Xu Xu, Z. Ye
semanticscholar   +1 more source

Covariance Matrix Reconstruction for DOA Estimation in Hybrid Massive MIMO Systems

IEEE Wireless Communications Letters, 2020
Multiple signal classification (MUSIC) has been widely applied in wireless communications for direction-of-arrival (DOA) estimation. For massive multiple-input multiple-output (MIMO) systems operating at millimeter-wave bands, hybrid analog-digital ...
Si Li, Yinsheng Liu, Li You, Wenjin Wang, Hongtao Duan, Xu Li   +5 more
semanticscholar   +1 more source

Toeplitz Structured Covariance Matrix Estimation for Radar Applications

IEEE Signal Processing Letters, 2020
Following a geometric paradigm, the estimation of a Toeplitz structured covariance matrix is considered. The estimator minimizes the distance from the Sample Covariance Matrix (SCM) while complying with some specific constraints modeling the covariance ...
Xiaolin Du, A. Aubry, A. De Maio, G. Cui
semanticscholar   +1 more source

Large-scale Sparse Inverse Covariance Matrix Estimation

SIAM Journal on Scientific Computing, 2019
The estimation of large sparse inverse covariance matrices is a ubiquitous statistical problem in many application areas such as mathematical finance, geology, health, and many others.
M. Bollhöfer, Aryan Eftekhari, S. Scheidegger, O. Schenk   +3 more
semanticscholar   +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

Covariance Matrix Simulation

2003
Abstract This chapter develops further the idea of LU decomposition and applies it to the simulation of covariance matrices. The vast majority of cash flow models used to analyze the creditworthiness of structured securities or to investigate foreign exchange risk will include an implementation.
Sylvain Raynes, Ann Rutledge
openaire   +1 more source

Shrinking the Covariance Matrix

The Journal of Portfolio Management, 2007
The subject here is construction of the covariance matrix for portfolio optimization. In terms of the ex post standard deviation of the global minimum-variance portfolio, there is no statistically significant gain in using more sophisticated shrinkage estimators rather than simpler portfolios of estimators.
David J. Disatnik, Simon Benninga
openaire   +1 more source

Online Inverse Covariance Matrix

Proceedings of the 2019 International Conference on Mathematics, Science and Technology Teaching and Learning, 2019
Some statistical analysis needs an inverse covariance matrix computing. A Gaussian process is a non-parametric method in statistical analysis that has been applied to some research. The Gaussian process needs an inverse covariance matrix computing by given data. Inverse matrix on Gaussian process becomes interesting problems in Gaussian process when it
Seli Siti Sholihat, Sapto Wahyu Indratno, Utriweni Mukhaiyar   +2 more
openaire   +1 more source

The Covariance Matrix of the Information Matrix Test

Econometrica, 1984
In this note we point out how the covariance matrix of the information matrix test, due to \textit{H. White} [ibid. 50, 1-25 (1982; Zbl 0478.62088)], can be estimated without the computation of analytic third derivatives of the density function.
openaire   +2 more sources

Posterior Covariance Matrix Approximations

Journal of Verification, Validation and Uncertainty Quantification
Abstract The Davis equation of state (EOS) is commonly used to model thermodynamic relationships for high explosive (HE) reactants. Typically, the parameters in the EOS are calibrated, with uncertainty, using a Bayesian framework and Markov Chain Monte Carlo (MCMC) methods.
Abigail C. Schmid, Stephen A. Andrews
openaire   +1 more source