Results 11 to 20 of about 134,557 (305)
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 +4 more sources
Covariance Matrix Estimation With Heterogeneous Samples [PDF]
IEEE Transactions on Signal Processing, 2008 We consider the problem of estimating the covariance matrix Mp of an observation vector, using heterogeneous training samples, i.e., samples whose covariance matrices are not exactly Mp. More precisely, we assume that the training samples can be clustered into K groups, each one containing Lk, snapshots sharing the same covariance matrix Mk ...
Olivier Besson +2 more
openaire +4 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 +3 more
doaj +1 more source
Actuators, 2023 In this paper, a novel state estimation approach based on the variational Bayesian adaptive Kalman filter (VBAKF) and road classification is proposed for a suspension system with time-varying and unknown noise covariance.
Qiangqiang Li, Zhiyong Chen, Wenku Shi
doaj +1 more source
Ionospheric Kalman Filter Assimilation Based on Covariance Localization Technique
Remote Sensing, 2022 The data assimilation algorithm is a common algorithm in space weather research. Based on the GNSS data from the China Crustal Movement Observation Network (CMONOC) and the International Reference Ionospheric Model (IRI), a fast three-dimensional (3D ...
Jiandong Qiao +4 more
doaj +1 more source
Shrinkage Estimators for Covariance Matrices [PDF]
Biometrics, 2001 Estimation of covariance matrices in small samples has been studied by many authors. Standard estimators, like the unstructured maximum likelihood estimator (ML) or restricted maximum likelihood (REML) estimator, can be very unstable with the smallest estimated eigenvalues being too small and the largest too big.
Daniels, Michael J., Kass, Robert E.
openaire +3 more sources
Covariance-Aware Private Mean Estimation Without Private Covariance Estimation
CoRR, 2021 We present two sample-efficient differentially private mean estimators for $d$-dimensional (sub)Gaussian distributions with unknown covariance. Informally, given $n \gtrsim d/α^2$ samples from such a distribution with mean $μ$ and covariance $Σ$, our estimators output $\tildeμ$ such that $\| \tildeμ- μ\|_Σ \leq α$, where $\| \cdot \|_Σ$ is the ...
Gavin Brown 0003 +4 more
openaire +3 more sources
SEMIPARAMETRIC ESTIMATION WITH GENERATED COVARIATES [PDF]
Econometric Theory, 2011 We study a general class of semiparametric estimators when the infinite-dimensional nuisance parameters include a conditional expectation function that has been estimated nonparametrically using generated covariates. Such estimators are used frequently to e.g., estimate nonlinear models with endogenous covariates when identification is achieved using ...
Mammen, Enno +2 more
openaire +14 more sources
IEEE Access, 2020 The prior covariance estimation method based on inverse covariance intersection (ICI) is proposed to apply the particle flow filter. The proposed method has better estimate performance and guarantees consistent estimation results compared with previous ...
Chang Ho Kang +2 more
doaj +1 more source
SPICE-ML Algorithm for Direction-of-Arrival Estimation
Sensors, 2019 Sparse iterative covariance-based estimation, an iterative direction-of-arrival approach based on covariance fitting criterion, can simultaneously estimate the angle and power of incident signal.
Yu Zheng, Lutao Liu, Xudong Yang
doaj +1 more source