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Sparse Steinian Covariance Estimation
Journal of Computational and Graphical Statistics, 2017ABSTRACTWe consider a new method for sparse covariance matrix estimation which is motivated by previous results for the so-called Stein-type estimators. Stein proposed a method for regularizing the sample covariance matrix by shrinking together the eigenvalues; the amount of shrinkage is chosen to minimize an unbiased estimate of the risk (UBEOR) under
Brett Naul, Jonathan Taylor
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SSRN Electronic Journal, 2007
This paper investigates the estimation risk in covariance. It is known that covariance can be estimated accurately under the i.i.d. normality assumption. However, time varying volatility and non-normality of asset returns can lead to imprecise covariance estimates, which can incur economic loss to a mean variance investor.
David D. Cho, Jeffrey Russell
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This paper investigates the estimation risk in covariance. It is known that covariance can be estimated accurately under the i.i.d. normality assumption. However, time varying volatility and non-normality of asset returns can lead to imprecise covariance estimates, which can incur economic loss to a mean variance investor.
David D. Cho, Jeffrey Russell
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2016
Covariance matrix estimation allows the adaptation of Gaussian-based mutation operators to local solution space characteristics.
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Covariance matrix estimation allows the adaptation of Gaussian-based mutation operators to local solution space characteristics.
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Covariance Parameter Estimation
2019This chapter reviews fundamental ideas from linear model theory for dealing with dependent or heteroscedastic data when the nature of the dependence or heteroscedasticity is known. It then introduces general ideas for estimating dependence or heteroscedasticity when their exact natures are unknown.
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Journal of Asset Management, 2011
This article investigates the estimation risk in covariance. Although previous research has shown that the covariance can be estimated accurately by assuming independently and identically distributed normal returns, time-varying volatility and non-normality can lead to imprecise covariance estimates, which can cause economic loss to a mean variance ...
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This article investigates the estimation risk in covariance. Although previous research has shown that the covariance can be estimated accurately by assuming independently and identically distributed normal returns, time-varying volatility and non-normality can lead to imprecise covariance estimates, which can cause economic loss to a mean variance ...
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Covariances in Multiplicative Estimates
Transactions of the American Fisheries Society, 1999Abstract Multiplicative estimators, in which a quantity is calculated as the product of two or more estimated factors, are relatively common in fisheries work. Creel surveys and stock assessment data are two obvious examples. If the same multiplicative factor is used in several estimates, or if there is covariance between multiplicative factors, then ...
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Covariance Matrix Estimation in Complex Surveys
The Egyptian Statistical Journal, 1989Summary: An estimator of the asymptotic covariance matrix of the vector of second- order sample moments under cluster sampling design is derived by the Taylor expansion method. The form of the estimator under stratified cluster sampling design is obtained as well.
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DoA Estimation Using Neural Network-Based Covariance Matrix Reconstruction
IEEE Signal Processing Letters, 2021Andreas Barthelme, Wolfgang Utschick
exaly
Data-Driven Covariance Estimation
2022 IEEE International Symposium on Phased Array Systems & Technology (PAST), 2022John T. Rogers +2 more
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