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A cautionary note on robust covariance plug-in methods [PDF]
, 2014 Many multivariate statistical methods rely heavily on the sample covariance matrix. It is well known though that the sample covariance matrix is highly non-robust.
Nordhausen, Klaus, Tyler, David E.
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The Power of (Non-)Linear Shrinking: A Review and Guide to Covariance Matrix Estimation
Journal of Financial Econometrics, 2020 Many econometric and data-science applications require a reliable estimate of the covariance matrix, such as Markowitz’s portfolio selection. When the number of variables is of the same magnitude as the number of observations, this constitutes a ...
Olivier Ledoit, Michael Wolf
semanticscholar +1 more source
Discriminant methods for high dimensional data [PDF]
Songklanakarin Journal of Science and Technology (SJST), 2019 The main purpose of discriminant analysis is to enable classification of new observations into one of g classes or populations. Discriminant methods suffer when applied to high dimensional data because the sample covariance matrix is singular.
Poompong Kaewumpai, Samruam Chongcharoen
doaj +1 more source
Nonparametric Stein-type Shrinkage Covariance Matrix Estimators in High-Dimensional Settings [PDF]
, 2014 Estimating a covariance matrix is an important task in applications where the number of variables is larger than the number of observations. Shrinkage approaches for estimating a high-dimensional covariance matrix are often employed to circumvent the ...
Touloumis, Anestis
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Remarks on covariant matrix strings [PDF]
Physics Letters B, 1999 15 pages, harvmac.
Baulieu, Laurent +2 more
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A Kernel Gabor-Based Weighted Region Covariance Matrix for Face Recognition
Sensors, 2012 This paper proposes a novel image region descriptor for face recognition, named kernel Gabor-based weighted region covariance matrix (KGWRCM). As different parts are different effectual in characterizing and recognizing faces, we construct a weighting ...
Yantao Li +3 more
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Large Covariance Estimation by Thresholding Principal Orthogonal Complements [PDF]
, 2013 This paper deals with the estimation of a high-dimensional covariance with a conditional sparsity structure and fast-diverging eigenvalues. By assuming sparse error covariance matrix in an approximate factor model, we allow for the presence of some cross-
Fan, Jianqing +2 more
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Analysis of Semi-Blind Channel Estimation in Multiuser Massive MIMO Systems With Perturbations
IEEE Access, 2019 In the massive multiple-input multiple-output (MIMO) systems, pilot contamination and signal perturbation are two important issues in the semi-blind channel estimation methods.
Cheng Hu, Hong Wang, Rongfang Song
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Covariance estimation via fiducial inference
Statistical Theory and Related Fields, 2021 As a classical problem, covariance estimation has drawn much attention from the statistical community for decades. Much work has been done under the frequentist and Bayesian frameworks.
W. Jenny Shi +3 more
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Target Detection Using Nonsingular Approximations for a Singular Covariance Matrix
Journal of Electrical and Computer Engineering, 2012 Accurate covariance matrix estimation for high-dimensional data can be a difficult problem. A good approximation of the covariance matrix needs in most cases a prohibitively large number of pixels, that is, pixels from a stationary section of the image ...
Nir Gorelik +3 more
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