Results 1 to 10 of about 192,768 (193)
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
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Kronecker Sum Decompositions of Space-Time Data [PDF]
, 2013 In this paper we consider the use of the space vs. time Kronecker product decomposition in the estimation of covariance matrices for spatio-temporal data.
Greenewald, Kristjan +2 more
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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
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Estimation of Time-Varying Covariance Matrices for Large Datasets [PDF]
SSRN Electronic Journal, 2018 Time variation is a fundamental problem in statistical and econometric analysis of macroeconomic and financial data. Recently, there has been considerable focus on developing econometric modelling that enables stochastic structural change in model parameters and on model estimation by Bayesian or nonparametric kernel methods.
Dendramis, Y, Giraitis, L, Kapetanios, G
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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
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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
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Regularized estimation of large covariance matrices
The Annals of Statistics, 2008 This paper considers estimating a covariance matrix of $p$ variables from $n$ observations by either banding or tapering the sample covariance matrix, or estimating a banded version of the inverse of the covariance. We show that these estimates are consistent in the operator norm as long as $(\log p)/n\to0$, and obtain explicit rates.
Bickel, Peter J., Levina, Elizaveta
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Covariance matrices of S robust regression estimators
Journal of Statistical Computation and Simulation, 2021 Asymptotic properties of robust regression estimators are well known. However, it is not always clear what is the best strategy for confidence intervals and hypothesis testing when the sample size is not very large, since the distribution of residuals coming from robust estimates has unknown properties for small samples.
S. Salini +4 more
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Construction of non-diagonal background error covariance matrices for global chemical data assimilation [PDF]
Geoscientific Model Development, 2011 Chemical data assimilation attempts to optimally use noisy observations along with imperfect model predictions to produce a better estimate of the chemical state of the atmosphere.
K. Singh +5 more
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Optimal Estimation and Rank Detection for Sparse Spiked Covariance Matrices [PDF]
, 2015 This paper considers sparse spiked covariance matrix models in the high-dimensional setting and studies the minimax estimation of the covariance matrix and the principal subspace as well as the minimax rank detection.
Cai, Tony, Ma, Zongming, Wu, Yihong
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