Results 51 to 60 of about 192,869 (306)
Broadband angle of arrival estimation methods in a polynomial matrix decomposition framework [PDF]
, 2013 A large family of broadband angle of arrival estimation algorithms are based on the coherent signal subspace (CSS) method, whereby focussing matrices appropriately align covariance matrices across narrowband frequency bins.
Alrmah, Mohamed +4 more
core +1 more source
Nonparametric Estimation of Large Covariance Matrices with Conditional Sparsity [PDF]
SSRN Electronic Journal, 2020 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wang, Hanchao +3 more
openaire +3 more sources
IEEE Access, 2018 This paper addresses the estimation of large-dimensional covariance matrices under both normal and nonnormal distributions. The shrinkage estimators are constructed by convexly combining the sample covariance matrix and a structured target matrix.
Jianbo Li, Jie Zhou, Bin Zhang
doaj +1 more source
Asymptotic analysis of the role of spatial sampling for covariance parameter estimation of Gaussian processes [PDF]
, 2013 Covariance parameter estimation of Gaussian processes is analyzed in an asymptotic framework. The spatial sampling is a randomly perturbed regular grid and its deviation from the perfect regular grid is controlled by a single scalar regularity parameter.
Bachoc, François
core +3 more sources
A data driven equivariant approach to constrained Gaussian mixture modeling
, 2016 Maximum likelihood estimation of Gaussian mixture models with different class-specific covariance matrices is known to be problematic. This is due to the unboundedness of the likelihood, together with the presence of spurious maximizers. Existing methods
Di Mari, Roberto +2 more
core +1 more source
Efficient nonparametric estimation of Toeplitz covariance matrices [PDF]
BiometrikaAbstract 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
Karolina Klockmann, Tatyana Krivobokova
openalex +6 more sources
Performance of internal Covariance Estimators for Cosmic Shear Correlation Functions [PDF]
, 2015 Data re-sampling methods such as the delete-one jackknife are a common tool for estimating the covariance of large scale structure probes. In this paper we investigate the concepts of internal covariance estimation in the context of cosmic shear two ...
Eifler, T. F. +3 more
core +2 more sources
International Journal of Adaptive Control and Signal Processing, EarlyView.This paper proposes two projector‐based Hopfield neural network (HNN) estimators for online, constrained parameter estimation under time‐varying data, additive disturbances, and slowly drifting physical parameters. The first is a constraint‐aware HNN that enforces linear equalities and inequalities (via slack neurons) and continuously tracks the ...
Miguel Pedro Silva
wiley +1 more source
Geometric methods for estimation of structured covariances [PDF]
, 2011 We consider problems of estimation of structured covariance matrices, and in particular of matrices with a Toeplitz structure. We follow a geometric viewpoint that is based on some suitable notion of distance. To this end, we overview and compare several
Georgiou, Tryphon +2 more
core +1 more source
Direct Nonlinear Shrinkage Estimation of Large-Dimensional Covariance Matrices [PDF]
SSRN Electronic Journal, 2017 This paper introduces a nonlinear shrinkage estimator of the covariance matrix that does not require recovering the population eigenvalues first. We estimate the sample spectral density and its Hilbert transform directly by smoothing the sample eigenvalues with a variable-bandwidth kernel. Relative to numerically inverting the so-called QuEST function,
Ledoit, Olivier, Wolf, Michael
openaire +2 more sources