Results 1 to 10 of about 1,787,003 (299)
Covariance estimation via fiducial inference [PDF]
Statistical Theory and Related Fields, 2021 As a classical problem, covariance estimation has drawn much attention from the statistical community for decades. Much work has been done under the frequentist and Bayesian frameworks.
W. Jenny Shi +3 more
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Accurate genetic and environmental covariance estimation with composite likelihood in genome-wide association studies. [PDF]
PLoS Genetics, 2021 Genetic and environmental covariances between pairs of complex traits are important quantitative measurements that characterize their shared genetic and environmental architectures.
Boran Gao, Can Yang, Jin Liu, Xiang Zhou
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Robust Polarization-Domain Adaptive Anti-Jamming via Forgetting-Factor Covariance Estimation and Adaptive Diagonal Loading [PDF]
SensorsTo address robust polarization-domain adaptive anti-jamming for dual-polarized radars with limited secondary data and time-varying interference, this paper proposes a covariance-reliability-driven MVDR framework based on forgetting-factor covariance ...
Yuancong Xiong +4 more
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Deep Covariance Estimation Hashing [PDF]
IEEE Access, 2019 Deep hashing, the combination of advanced convolutional neural networks and efficient hashing, has recently achieved impressive performance for image retrieval.
Yue Wu +5 more
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Gridless DOA Estimator for 1.5-Bit Sparse Massive MIMO Systems Based on Covariance Matrix Estimation [PDF]
EntropyTo reduce the hardware cost of massive multiple-input multiple-output (MIMO) systems, low-bit analog-to-digital converters (ADCs) and sparse arrays are widely used.
Yuan Peng, Xiongbo Zheng, Zhiyong Cheng
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Condition Number Regularized Covariance Estimation. [PDF]
J R Stat Soc Series B Stat Methodol, 2013 SummaryEstimation of high dimensional covariance matrices is known to be a difficult problem, has many applications and is of current interest to the larger statistics community. In many applications including the so-called ‘large p, small n’ setting, the estimate of the covariance matrix is required to be not only invertible but also well conditioned.
Won JH, Lim J, Kim SJ, Rajaratnam B.
europepmc +4 more sources
Geodesically Parameterized Covariance Estimation [PDF]
SIAM Journal on Matrix Analysis and Applications, 2021 Statistical modeling of spatiotemporal phenomena often requires selecting a covariance matrix from a covariance class. Yet standard parametric covariance families can be insufficiently flexible for practical applications, while non-parametric approaches may not easily allow certain kinds of prior knowledge to be incorporated.
Antoni Musolas +2 more
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Accurate estimation of the clutter covariance matrix for the cell under test (CUT) is a committed step in the spatial-temporal adaptive processing (STAP) algorithm.
Tianfu Zhang +5 more
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Estimation of Bergsma’s covariance
Journal of the Korean Statistical Society, 2023 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Arup Bose +2 more
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2D-DOA Estimation in Switching UCA Using Deep Learning-Based Covariance Matrix Completion
Sensors, 2022 In this paper, we study the two-dimensional direction of arrival (2D-DOA) estimation problem in a switching uniform circular array (SUCA), which means performing 2D-DOA estimation with a reduction in the number of radio frequency (RF) chains.
Ruru Mei +3 more
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