Results 1 to 10 of about 134,557 (305)

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, Jan Hannig, Randy C. S. Lai, Thomas C. M. Lee   +3 more
doaj   +2 more sources

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
doaj   +2 more sources

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, Qiule Sun, Yaqing Hou, Jianxin Zhang, Qiang Zhang, Xiaopeng Wei   +5 more
doaj   +2 more sources

Gridless DOA Estimator for 1.5-Bit Sparse Massive MIMO Systems Based on Covariance Matrix Estimation [PDF]

Entropy
To 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
doaj   +2 more sources

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, Steven T. Smith, Youssef M. Marzouk   +2 more
openaire   +3 more sources

A Novel Clutter Covariance Matrix Estimation Method Based on Feature Subspace for Space-Based Early Warning Radar

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, Zhihao Wang, Ning Qiao, Shuangxi Zhang, Mengdao Xing, Yongliang Wang   +5 more
doaj   +1 more source

Estimation of Bergsma’s covariance

Journal of the Korean Statistical Society, 2023
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Arup Bose, Divya Kappara, Madhuchhanda Bhattacharjee   +2 more
openaire   +1 more source

An Expectation-Maximization Algorithm for Combining a Sample of Partially Overlapping Covariance Matrices

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, Mohamed Somo, Julio Isidro-Sanchéz   +2 more
doaj   +1 more source

List-decodable covariance estimation

Proceedings of the 54th Annual ACM SIGACT Symposium on Theory of Computing, 2022
Abstract slightly clipped.
Misha Ivkov, Pravesh K. Kothari
openaire   +2 more sources