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Structured Robust Covariance Estimation
Foundations and Trends® in Signal Processing, 2015We consider robust covariance estimation with an emphasis on Tyler’s M-estimator. This method provides accurate inference of an unknown covariance in non-standard settings, including heavy-tailed distributions and outlier contaminated scenarios. We begin with a survey of the estimator and its various derivations in the classical unconstrained settings.
Wiesel, Ami, Zhang, Teng
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On the estimation of structured covariance matrices
Automatica, 2012zbMATH Open Web Interface contents unavailable due to conflicting licenses.
ZORZI, MATTIA, FERRANTE, AUGUSTO
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Considering covariates in the covariance structure of spatial processes
Environmetrics, 2011In spatial statistics one usually assumes that observations are partial realizations of a stochastic process , where commonly C = 2, and the components of the location vector x are geographical coordinates. Frequently, it is assumed that follows a Gaussian process (GP) with stationary covariance structure.
Alexandra M. Schmidt +2 more
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Bootstrap Methods for Covariance Structures
The Journal of Human Resources, 1998The optimal minimum distance (OMD) estimator for models of covariance structures is asymptotically efficient but has much worse finite-sample properties than does the equally weighted minimum distance (EWMD) estimator. This paper shows how the bootstrap can be used to improve the finite-sample performance of the OMD estimator. The theory underlying the
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2004
For any component in time series analysis (Natke 1983), the concept of covariance between components of a spatially distributed random vector Z(u) leads to: direct covariances, Cov[Zi(u),Zj(u)]; shifted covariances or spatial covariances, Cov [Zi(u), Zj-(u+ h)], also known as cross-covariance functions; and autocovariance functions, Cov[Zi(u),Zi(u + h)]
Vera Pawlowsky-Glahn, Richardo A. Olea
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For any component in time series analysis (Natke 1983), the concept of covariance between components of a spatially distributed random vector Z(u) leads to: direct covariances, Cov[Zi(u),Zj(u)]; shifted covariances or spatial covariances, Cov [Zi(u), Zj-(u+ h)], also known as cross-covariance functions; and autocovariance functions, Cov[Zi(u),Zi(u + h)]
Vera Pawlowsky-Glahn, Richardo A. Olea
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Annual Review of Sociology, 1989
Over the last 16 years, since Karl Joreskog first introduced the LISREL model', the covariance structure model has gained considerable popularity among serious researchers in many research and applied fields. This has happened for two reasons. First, covariance structure models depict in a natural way the "causal" models researchers have in mind when ...
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Over the last 16 years, since Karl Joreskog first introduced the LISREL model', the covariance structure model has gained considerable popularity among serious researchers in many research and applied fields. This has happened for two reasons. First, covariance structure models depict in a natural way the "causal" models researchers have in mind when ...
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Structural covariance of graphs
Theoretica Chimica Acta, 1984Algebraic structures including multiple rank tensors, linear and non-linear operators are related to and represented with various types of graphs. Special emphasis is placed on linear operators e.g. on the Hibert space. A different graph represents the same operator depending on the basis frame used, in general non-orthonormal.
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2002
The covariance structure model overcomes certain weaknesses of both factor analysis and structural equation models by merging them into a single model that simultaneously estimates latent variables from observed variables and the structural relations among the latent variables.
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The covariance structure model overcomes certain weaknesses of both factor analysis and structural equation models by merging them into a single model that simultaneously estimates latent variables from observed variables and the structural relations among the latent variables.
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The Analysis of Covariance Structures
Sociological Methods & Research, 1983A major problem encountered in covariance structure analyses involves decisions concerning whether or not a given theoretical model adequately represents the data used for its assessment. Given that X 2 goodness-of-fit tests are joint functions of the difference between theoretical and ...
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