Results 11 to 20 of about 1,787,003 (299)
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
Sampled-data filtering with error covariance assignment [PDF]
, 2001 Copyright [2001] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services.
Huang, B, Wang, Z, Huo, P
core +6 more sources
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 +3 more sources
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
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
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 +4 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
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
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