Applicability evaluation of Akaike’s Bayesian information criterion to covariance modeling in the cross-section adjustment method [PDF]
EPJ Web of Conferences, 2023 The applicability of Akaike’s Bayesian Information Criterion (ABIC) to the covariance modeling in the cross-section adjustment method has been investigated.
Maruyama Shuhei +2 more
doaj +1 more source
Identification of Noise Covariance Matrices to Improve Orientation Estimation by Kalman Filter
Sensors, 2018 Magneto-inertial measurement units (MIMUs) are a promising way to perform human motion analysis outside the laboratory. To do so, in the literature, orientation provided by an MIMU is used to deduce body segment orientation. This is generally achieved by
Alexis Nez +4 more
doaj +1 more source
Multiple-Toeplitz Matrices Reconstruction Algorithm for DOA Estimation of Coherent Signals
IEEE Access, 2019 In this paper, a new direction-of-arrival (DOA) estimation method based on multiple Toeplitz matrices reconstruction is proposed for coherent narrowband signals with a uniform linear array (ULA).
Wei Zhang +3 more
doaj +1 more source
Robust Estimation of Constrained Covariance Matrices for Confirmatory Factor Analysis
Computational Statistics & Data Analysis, 2010 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Elise Dupuis‐Lozeron +1 more
openalex +6 more sources
Asymptotically Efficient Estimation of Covariance Matrices with Linear Structure [PDF]
The Annals of Statistics, 1973 One or more observations are made on a random vector, whose covariance matrix may be a linear combination of known symmetric matrices and whose mean vector may be a linear combination of known vectors; the coefficients of the linear combinations are unknown parameters to be estimated.
T. W. Anderson
openalex +3 more sources
Covariance Estimation in High Dimensions via Kronecker Product Expansions [PDF]
, 2013 This paper presents a new method for estimating high dimensional covariance matrices. The method, permuted rank-penalized least-squares (PRLS), is based on a Kronecker product series expansion of the true covariance matrix. Assuming an i.i.d.
Alfred O. Hero Iii +2 more
core +1 more source
Estimation of Deviation for Random Covariance Matrices
Michigan Mathematical Journal, 2019 The authors consider random covariance matrices of the form \(W=M^* M\), where \(M\) is a \(p\times n\)-random matrix whose entries are independent (not necessarily identically distributed) random variables with zero mean, unit variance, and uniformly bounded fourth moments.
Dinh, Tien-Cuong, Vu, Duc-Viet
openaire +3 more sources
Estimation Method of Covariance Matrix in Atmospheric Inversion of CO2 Emissions [PDF]
E3S Web of ConferencesAtmospheric inversion of CO2 Emissions is based on the correction of prior carbon dioxide flux inventories using concentration monitoring data and atmospheric transport models to obtain posterior carbon dioxide flux.
Han Yubin +4 more
doaj +1 more source
Adaptive covariance matrix estimation through block thresholding [PDF]
, 2012 Estimation of large covariance matrices has drawn considerable recent attention, and the theoretical focus so far has mainly been on developing a minimax theory over a fixed parameter space.
Cai, T. Tony, Yuan, Ming
core +3 more sources
A Robust Adaptive Unscented Kalman Filter for Nonlinear Estimation with Uncertain Noise Covariance
Sensors, 2018 The Unscented Kalman filter (UKF) may suffer from performance degradation and even divergence while mismatch between the noise distribution assumed as a priori by users and the actual ones in a real nonlinear system.
Binqi Zheng +3 more
doaj +1 more source