Results 41 to 50 of about 192,869 (306)
SensorsThis paper aims at achieving real-time optimal speed estimation for an induction motor using the Extended Kalman filter (EKF). Speed estimation is essential for fault diagnosis in Motor Current Signature Analysis (MCSA).
Ines Miloud +4 more
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MCMC Estimation of Restricted Covariance Matrices
Journal of Computational and Graphical Statistics, 2009 This article is motivated by the difficulty of applying standard simulation techniques when identification constraints or theoretical considerations induce covariance restrictions in multivariate models. To deal with this difficulty, we build upon a decomposition of positive definite matrices and show that it leads to straightforward Markov chain Monte
Chan, Joshua Chi-Chun, Jeliazkov, Ivan
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Validation on Residual Variation and Covariance Matrix of USSTRATCOM Two Line Element [PDF]
Journal of Astronomy and Space Sciences, 2012 Satellite operating agencies are constantly monitoring conjunctions between satellites and space objects. Two line element (TLE) data, published by the Joint Space Operations Center of the United States Strategic Command, are available as raw data for a ...
Hyeonjeong Yim, Daewon Chung
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Multivariate Empirical Bayes and Estimation of Covariance Matrices
The Annals of Statistics, 1976 The problem of estimating several normal mean vectors in an empirical Bayes situation is considered. In this case, it reduces to the problem of estimating the inverse of a covariance matrix in the standard multivariate normal situation using a particular loss function.
Efron, Bradley, Morris, Carl
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Inference for High-dimensional Differential Correlation Matrices [PDF]
, 2015 Motivated by differential co-expression analysis in genomics, we consider in this paper estimation and testing of high-dimensional differential correlation matrices.
Cai, T. Tony, Zhang, Anru
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Generative Adversarial Estimation of Channel Covariance in Vehicular Millimeter Wave Systems
, 2018 Enabling highly-mobile millimeter wave (mmWave) systems is challenging because of the huge training overhead associated with acquiring the channel knowledge or designing the narrow beams.
Alkhateeb, Ahmed +2 more
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Smooth Interpolation of Covariance Matrices and Brain Network Estimation
IEEE Transactions on Automatic Control, 2018 We propose an approach to use the state covariance of autonomous linear systems to track time-varying covariance matrices of nonstationary time series. Following concepts from the Riemannian geometry, we investigate three types of covariance paths obtained by using different quadratic regularizations of system matrices. The first quadratic form induces
Lipeng Ning
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Shrinkage estimation of high dimensional covariance matrices [PDF]
2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009 We address covariance estimation under mean-squared loss in the Gaussian setting. Specifically, we consider shrinkage methods which are suitable for high dimensional problems with small number of samples (large p small n). First, we improve on the Ledoit-Wolf (LW) method by conditioning on a sufficient statistic via the Rao-Blackwell theorem, obtaining
Yilun Chen, Ami Wiesel, Alfred O. Hero
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AIMS MathematicsWe estimated convex-structured covariance/correlation matrices by minimizing the entropy loss corresponding to the given matrix. We first considered the estimation of the Weighted sum of known Rank-one matrices with unknown Weights (W-Rank1-W) structural
Chen Chen, Xiangbing Chen, Yi Ai
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Joint Covariance Estimation with Mutual Linear Structure
, 2015 We consider the problem of joint estimation of structured covariance matrices. Assuming the structure is unknown, estimation is achieved using heterogeneous training sets.
Soloveychik, Ilya, Wiesel, Ami
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