Results 1 to 10 of about 80,418 (162)

Covariance Matrix Estimation in Complex Surveys [PDF]

The Egyptian Statistical Journal, 1989
An estimator of asymptotic covariance matrix of vector of second-order sample moments under cluster sampling design is derived by the Taylor expansion method. The form of the estimator under stratified cluster sampling design is obtained as well.
Muhammad Pervaiz
doaj   +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   +3 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

Covariance Matrix Estimation in Massive MIMO [PDF]

IEEE Signal Processing Letters, 2018
submitted to IEEE Signal Processing ...
Wolfgang Utschick, Michael Joham, David Neumann   +2 more
exaly   +3 more sources

Sparse Covariance Matrix Estimation With Eigenvalue Constraints. [PDF]

J Comput Graph Stat, 2014
We propose a new approach for estimating high-dimensional, positive-definite covariance matrices. Our method extends the generalized thresholding operator by adding an explicit eigenvalue constraint. The estimated covariance matrix simultaneously achieves sparsity and positive definiteness.
Liu H, Wang L, Zhao T.
europepmc   +4 more sources

The Effects of Data Imputation on Covariance and Inverse Covariance Matrix Estimation

IEEE Access
Various data analysis techniques and procedures (correlation heatmap, linear discriminant analysis, quadratic discriminant analysis) rely on the estimation of the covariance matrix or its inverse (the precision matrix).
Tuan L. Vo, Quan Huu Do, Thu Nguyen, Pal Halvorsen, Michael A. Riegler, Binh T. Nguyen   +5 more
doaj   +2 more sources

k-Covariance: An Approach of Ensemble Covariance Estimation and Undersampling to Stabilize the Covariance Matrix in the Global Minimum Variance Portfolio

Applied Sciences, 2022
A covariance matrix is an important parameter in many computational applications, such as quantitative trading. Recently, a global minimum variance portfolio received great attention due to its performance after the 2007–2008 financial crisis, and this ...
Tuan Tran, Nhat Nguyen, Trung Nguyen
doaj   +1 more source

Sparse estimation of a covariance matrix [PDF]

Biometrika, 2011
We suggest a method for estimating a covariance matrix on the basis of a sample of vectors drawn from a multivariate normal distribution. In particular, we penalize the likelihood with a lasso penalty on the entries of the covariance matrix. This penalty plays two important roles: it reduces the effective number of parameters, which is important even ...
Jacob Bien, Robert J. Tibshirani
openaire   +3 more sources

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, Ye Tian, Yonghui Huang, Zhugang Wang   +3 more
doaj   +1 more source

Weighted covariance matrix estimation [PDF]

Computational Statistics & Data Analysis, 2019
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Guangren Yang, Yiming Liu, Guangming Pan
openaire   +3 more sources