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A HeteroskedasticityConsistent Covariance Matrix Estimator And A Direct Test For Heteroskedasticity
, 1980
"... This paper presents a parameter covariance matrix estimator which is consistent even when the disturbances of a linear regression model are heteroskedastic. This estimator does not depend on a formal model of the structure of the heteroskedasticity. By comparing the elements of the new estimator ..."
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Cited by 3211 (5 self)
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This paper presents a parameter covariance matrix estimator which is consistent even when the disturbances of a linear regression model are heteroskedastic. This estimator does not depend on a formal model of the structure of the heteroskedasticity. By comparing the elements of the new estimator
Consistent Covariance Matrix Estimator by
, 2001
"... heteroskedasticity consistent covariance matrix estimator ..."
Rank Covariance Matrix For A Partially Known Covariance Matrix Rank Covariance Matrix For A Partially Known Covariance Matrix
"... Abstract Classical multivariate methods are often based on the sample covariance matrix, which is very sensitive to outlying observations. One alternative to the covariance matrix is the ane equivariant rank covariance matrix (RCM) that has been studied for example in Visuri et al. (2003). In this ..."
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Abstract Classical multivariate methods are often based on the sample covariance matrix, which is very sensitive to outlying observations. One alternative to the covariance matrix is the ane equivariant rank covariance matrix (RCM) that has been studied for example in Visuri et al. (2003
The intraclass covariance matrix
 Behavior Genetics
, 2005
"... Introduced by C.R. Rao in 1945, the intraclass covariance matrix has seen little use in behavioral genetic research, despite the fact that it was developed to deal with family data. Here, I reintroduce this matrix, and outline its estimation and basic properties for data sets on pairs of relatives. ..."
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Cited by 1 (0 self)
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Introduced by C.R. Rao in 1945, the intraclass covariance matrix has seen little use in behavioral genetic research, despite the fact that it was developed to deal with family data. Here, I reintroduce this matrix, and outline its estimation and basic properties for data sets on pairs of relatives
Estimating the Factors of the Covariance Matrix
"... this paper, and probably other places too, the estimation of the covariance matrices proceeds simultaneously with the optimization, and it too is an optimization problem. The nonlinear step accomodates the limitation of the maximumliklihood estimation paradyme that the residual covariance matrix be ..."
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this paper, and probably other places too, the estimation of the covariance matrices proceeds simultaneously with the optimization, and it too is an optimization problem. The nonlinear step accomodates the limitation of the maximumliklihood estimation paradyme that the residual covariance matrix
Convex Banding of the Covariance Matrix
, 2014
"... We introduce a new sparse estimator of the covariance matrix for highdimensional models in which the variables have a known ordering. Our estimator, which is the solution to a convex optimization problem, is equivalently expressed as an estimator which tapers the sample covariance matrix by a Toep ..."
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We introduce a new sparse estimator of the covariance matrix for highdimensional models in which the variables have a known ordering. Our estimator, which is the solution to a convex optimization problem, is equivalently expressed as an estimator which tapers the sample covariance matrix by a
Estimation of Covariance Matrix in Signal Processing When the Noise Covariance Matrix is Arbitrary
"... An estimator of the covariance matrix in signal processing is derived when the noise covariance matrix is arbitrary based on the method of maximum likelihood estimation. The estimator is a continuous function of the eigenvalues and eigenvectors of the matrix 2 1 1 ..."
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An estimator of the covariance matrix in signal processing is derived when the noise covariance matrix is arbitrary based on the method of maximum likelihood estimation. The estimator is a continuous function of the eigenvalues and eigenvectors of the matrix 2 1 1
Improved estimation of the covariance matrix of stock returns with an application to portfolio selection.
 Journal of Empirical Finance,
, 2003
"... Abstract This paper proposes to estimate the covariance matrix of stock returns by an optimally weighted average of two existing estimators: the sample covariance matrix and singleindex covariance matrix. This method is generally known as shrinkage, and it is standard in decision theory and in emp ..."
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Cited by 238 (5 self)
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Abstract This paper proposes to estimate the covariance matrix of stock returns by an optimally weighted average of two existing estimators: the sample covariance matrix and singleindex covariance matrix. This method is generally known as shrinkage, and it is standard in decision theory
Probing the covariance matrix
"... Abstract. By drawing an analogy between the logarithm of a probability distribution and a physical potential, it is natural to ask the question, “what is the effect of applying an external force on model parameters? " In Bayesian inference, parameters are frequently estimated as those that ..."
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that maximize the posterior, yielding the maximum a posteriori (MAP) solution, which corresponds to minimizing ϕ = −log(posterior). The uncertainty in the estimated parameters is typically summarized by the covariance matrix for the posterior distribution, C. I describe a novel approach to estimating specified
Results 1  10
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6,183