Results 21 to 30 of about 192,768 (193)
Axioms, 2023 The generation of unprecedented amounts of data brings new challenges in data management, but also an opportunity to accelerate the identification of processes of multiple science disciplines.
Deniz Akdemir +2 more
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Estimation of Large-Dimensional Covariance Matrices via Second-Order Stein-Type Regularization
Entropy, 2022 This paper tackles the problem of estimating the covariance matrix in large-dimension and small-sample-size scenarios. Inspired by the well-known linear shrinkage estimation, we propose a novel second-order Stein-type regularization strategy to generate ...
Bin Zhang, Hengzhen Huang, Jianbin Chen
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On the Properties of Estimates of Monotonic Mean Vectors for Multivariate Normal Distributions [PDF]
Journal of Statistical Theory and Applications (JSTA), 2015 .Problems concerning estimation of parameters and determination the statistic, when it is known a priori that some of these parameters are subject to certain order restrictions, are of considerable interest.
Abouzar Bazyari
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IEEE Access, 2022 Covariance matrices of spatially-correlated wireless channels in millimeter wave (mmWave) vehicular networks can be employed to design environment-aware beamforming codebooks.
Imtiaz Nasim, Ahmed S. Ibrahim
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Sensors, 2020 Aiming at the problem that the performance of adaptive Kalman filter estimation will be affected when the statistical characteristics of the process and measurement of the noise matrices are inaccurate and time-varying in the linear Gaussian state-space ...
Chenghao Shan +3 more
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Pose estimation by extended Kalman filter using noise covariance matrices based on sensor output
ROBOMECH Journal, 2020 This paper presents an extended Kalman filter for pose estimation using noise covariance matrices based on sensor output. Compact and lightweight nine-axis motion sensors are used for motion analysis in widely various fields such as medical welfare and ...
Ayuko Saito +3 more
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Covariance Estimation in High Dimensions via Kronecker Product Expansions [PDF]
, 2013 This paper presents a new method for estimating high dimensional covariance matrices. The method, permuted rank-penalized least-squares (PRLS), is based on a Kronecker product series expansion of the true covariance matrix. Assuming an i.i.d.
Alfred O. Hero Iii +2 more
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Applicability evaluation of Akaike’s Bayesian information criterion to covariance modeling in the cross-section adjustment method [PDF]
EPJ Web of Conferences, 2023 The applicability of Akaike’s Bayesian Information Criterion (ABIC) to the covariance modeling in the cross-section adjustment method has been investigated.
Maruyama Shuhei +2 more
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Multiple-Toeplitz Matrices Reconstruction Algorithm for DOA Estimation of Coherent Signals
IEEE Access, 2019 In this paper, a new direction-of-arrival (DOA) estimation method based on multiple Toeplitz matrices reconstruction is proposed for coherent narrowband signals with a uniform linear array (ULA).
Wei Zhang +3 more
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Identification of Noise Covariance Matrices to Improve Orientation Estimation by Kalman Filter
Sensors, 2018 Magneto-inertial measurement units (MIMUs) are a promising way to perform human motion analysis outside the laboratory. To do so, in the literature, orientation provided by an MIMU is used to deduce body segment orientation. This is generally achieved by
Alexis Nez +4 more
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