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Methods of estimation of a covariance matrix
Computational Statistics and Data Analysis, 1987Douglas M Hawkins
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Estimation of the Covariance Matrix
2020This 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
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On the calculation of a robustS-estimator of a covariance matrix
Statistics in Medicine, 1998An S-estimator of multivariate location and scale minimizes the determinant of the covariance matrix, subject to a constraint on the magnitudes of the corresponding Mahalanobis distances. The relationship between S-estimators and w-estimators of multivariate location and scale can be used to calculate robust estimates of covariance matrices.
Campbell, N.A. +2 more
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Kronecker Structured Covariance Matrix Estimation
2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07, 2007The 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 +2 more
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Improved estimation of the logarithm of the covariance matrix
2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop (SAM), 2012An improved estimator of certain bilinear forms of the logarithm of the covariance matrix is presented. The new estimator is shown to be consistent, not only for increasing sample size (as traditional estimators), but also when the observation dimension scales up at the same rate as the number of available observations.
Xavier Mestre +2 more
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Linguistically Described Covariance Matrix Estimation
2017In 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 Przybyla, Tomasz Pander
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Rank covariance matrix estimation of a partially known covariance matrix
Journal of Statistical Planning and Inference, 2008zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kuljus, Kristi, von Rosen, Dietrich
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On the Maximum Likelihood Estimation of a Covariance Matrix
Mathematical Methods of Statistics, 2018Stein phenomena (or Stein paradox) is as followed: ``there is a better estimator than the sample mean vector in the case of the multinormal mean vector under a quadratic loss function, and there is a better estimator than the sample covariance matrix in the case of multinormal covariance matrix under the Stein loss function.'' In this paper the set of ...
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ESTIMATING A COVARIANCE MATRIX IN MANOVA MODEL
Statistics & Risk Modeling, 2002Summary: 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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