Results 301 to 310 of about 108,134 (338)

Sparse and Low-Rank Covariance Matrix Estimation

Journal of the Operations Research Society of China, 2014
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zhou, Shenglong, Kong, LingChen, Luo, Ziyan, Xiu, Naihua   +3 more
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Large-scale Sparse Inverse Covariance Matrix Estimation

SIAM Journal on Scientific Computing, 2019
A short summary of the mathematical problem of sparse inverse covariance estimation and its formulation as a convex optimization problem are given. \par The given topic is rather challenging. The proposed method for that problem is the QUIC method. The QUIC method is briefly reviewed after that.
Bollhöfer, Matthias, Eftekhari, Aryan, Scheidegger, Simon, Schenk, Olaf   +3 more
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Error covariance matrix estimation using ridge estimator

Statistics & Probability Letters, 2013
Abstract This article considers sparse covariance matrix estimation of high dimension. In contrast to the existing methods which are based on the residual estimation from least squares estimator, we utilize residuals from ridge estimator with the adaptive thresholding technique to estimate the error covariance matrix in high dimensional factor model.
June Luo, K.B. Kulasekera
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Sparse Covariance Matrix Estimation by DCA-Based Algorithms

Neural Computation, 2017
This letter proposes a novel approach using the [Formula: see text]-norm regularization for the sparse covariance matrix estimation (SCME) problem. The objective function of SCME problem is composed of a nonconvex part and the [Formula: see text] term, which is discontinuous and difficult to tackle.
Phan, Duy Nhat, Le Thi, Hoai An, Dinh, Tao Pham   +2 more
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CONSISTENT COVARIANCE MATRIX ESTIMATION FOR LINEAR PROCESSES

Econometric Theory, 2002
Consistency of kernel estimators of the long-run covariance matrix of a linear process is established under weak moment and memory conditions. In addition, it is pointed out that some existing consistency proofs are in error as they stand.
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ESTIMATING A COVARIANCE MATRIX IN MANOVA MODEL

Statistics & Risk Modeling, 2002
Summary: For the estimation of the covariance matrix in the framework of multivariate analysis of variance (MANOVA) model, \textit{B.K. Sinha} and \textit{M. Ghosh} [ibid. 5, 201-227 (1987; Zbl 0634.62050)] proposed a Stein type truncated estimator improving on the uniformly minimum variance unbiased (UMVU) estimator under the entropy loss.
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DOA estimation using modified covariance matrix

2012 Loughborough Antennas & Propagation Conference (LAPC), 2012
This work proposes a new method to estimate direction-of-arrival (DOA) for directional antenna arrays. An obvious modification in the proposed method is the inclusion of changes of array gain in matrix calculation. This method is proposed in order to suit the characteristic of directional antenna array.
Rahmat Sanudin, Nurul Hazlina Noordin, Tughrul Arslan   +2 more
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Consistent Covariance Matrix Estimation for Dependent Heterogeneous Processes

Econometrica, 1992
This note presents a simple consistency proof for general kernel-based covariance estimators, requiring the existence of only slightly more than second moments. Covariance stationarity is not required. Instead, the data are assumed to satisfy either an \(\alpha\)-mixing or a \(\phi\)-mixing condition.
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Covariance Matrix Estimation in Linear Models

Journal of the American Statistical Association, 1970
Abstract In regression analysis with heteroscedastic and/or correlated errors, the usual assumption is that the covariance matrix σ of the errors is completely specified, except perhaps for a scalar multiplier. This condition is relaxed in this paper by assuming only that σ has a certain pattern; for example, that σ is diagonal or partitionable into a ...
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Equivariant estimators of the covariance matrix

Canadian Journal of Statistics, 1990
AbstractGiven a Wishart matrix S [S ∽ Wp(n, Σ)] and an independent multinomial vector X [X ∽ Np (μ, Σ)], equivariant estimators of Σ are proposed. These estimators dominate the best multiple of S and the Stein‐type truncated estimators.
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