Results 281 to 290 of about 134,557 (305)
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Estimation in covariate-adjusted regression
Computational Statistics & Data Analysis, 2006zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Damla Sentürk, Danh V. Nguyen
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IEEE Transactions on Industrial Electronics, 2014
The Kalman filter has been found to be useful in vast areas. However, it is well known that the successful use of the standard Kalman filter is greatly restricted by the strict requirements on a priori information of the model structure and statistics information of the process, and measurement noises.
Bo Feng +4 more
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The Kalman filter has been found to be useful in vast areas. However, it is well known that the successful use of the standard Kalman filter is greatly restricted by the strict requirements on a priori information of the model structure and statistics information of the process, and measurement noises.
Bo Feng +4 more
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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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Robust Estimation of Multivariate Covariance Components
Biometrics, 2005Summary In many settings, such as interlaboratory testing, small area estimation in sample surveys, and heritability studies, investigators are interested in estimating covariance components for multivariate measurements. However, the presence of outliers can seriously distort estimates obtained using standard procedures such as maximum likelihood.
Dueck, Amylou, Lohr, Sharon
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DoA Estimation Using Neural Network-Based Covariance Matrix Reconstruction
IEEE Signal Processing Letters, 2021Andreas Barthelme, Wolfgang Utschick
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Least Squares Estimation When the Covariance Matrix and Parameter Vector are Functionally Related
Journal of the American Statistical Association, 1980W A Fuller
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SPICE: A Sparse Covariance-Based Estimation Method for Array Processing
IEEE Transactions on Signal Processing, 2011Petre Stoica, Prabhu Babu, Jian Li
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Covariance matrix estimation and classification with limited training data
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996D A Landgrebe
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