Results 271 to 280 of about 1,787,003 (299)
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Estimation of hyperspectral covariance matrices
2011 IEEE International Geoscience and Remote Sensing Symposium, 2011Estimation of covariance matrices is a fundamental step in hyperspectral remote sensing where most detection algorithms make use of the covariance matrix in whitening procedures. We present a simple method to improve the estimation of the eigenvalues of a sample covariance matrix. With the improved eigenvalues we construct an improved covariance matrix.
Avishai Ben-David, Charles E. Davidson
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The decentralized estimation of the sample covariance
2008 42nd Asilomar Conference on Signals, Systems and Computers, 2008In this paper we consider the problem of estimating the eigenvectors of the sample covariance matrix of decentralized measurements in a distributed fashion. The need for a distributed scheme is motivated by the many moment based methods that resort to the covariance of the data to extract information from the measurements.
Anna Scaglione +2 more
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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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A Nonparametric Prewhitened Covariance Estimator
Journal of Time Series Analysis, 2002This paper proposes a new nonparametric spectral density estimator for time series models with general autocorrelation. The conventional nonparametric estimator that uses a positive kernel has mean squared error no better than n−4/5. We show that the best implementation of our estimator has mean squared error of order n−8/9, provided there is ...
Xiao, Zhijie, Linton, Oliver
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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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Kalman Filter With Recursive Covariance Estimation—Sequentially Estimating Process Noise Covariance
IEEE Transactions on Industrial Electronics, 2014The 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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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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Estimation of the Finite Population Covariance
2007Some calibrated estimators of the finite population covariance are presented. The estimators are constructed using different calibration equations and different loss functions. In the most cases the explicit solution of the calibration problem does not exist. The approximate iterative equations for the calibrated weights can be derived.
Plikusas, Aleksandras, Pumputis, Dalius
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Covariance estimation under one-bit quantization
Annals of Statistics, 2022Johannes Malý +2 more
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DoA Estimation Using Neural Network-Based Covariance Matrix Reconstruction
IEEE Signal Processing Letters, 2021Wolfgang Utschick, Andreas Barthelme
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