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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 3060 (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 ..."
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
Structured Covariance Matrix Estimation: A . . .
, 2000
"... The problem of estimating a positive semidefinite Toeplitz covariance matrix consisting of a low rank matrix plus a scaled identity from noisy data arises in many applications. We propose a computationally attractive (noniterative) covariance matrix estimator with certain optimality properties. For ..."
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Cited by 2 (1 self)
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The problem of estimating a positive semidefinite Toeplitz covariance matrix consisting of a low rank matrix plus a scaled identity from noisy data arises in many applications. We propose a computationally attractive (noniterative) covariance matrix estimator with certain optimality properties
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
Covariance Matrix Estimation in Time Series
, 2011
"... Covariances play a fundamental role in the theory of time series and they are critical quantities that are needed in both spectral and time domain analysis. Estimation of covariance matrices is needed in the construction of confidence regions for unknown parameters, hypothesis testing, principal com ..."
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component analysis, prediction, discriminant analysis among others. In this paper we consider both low and highdimensional covariance matrix estimation problems and present a review for asymptotic properties of sample covariances and covariance matrix estimates. In particular, we shall provide
Results 1  10
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328,723