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Assessing the estimation of nearly singular covariance matrices for modeling spatial variables
(Wiley, 2023)
© 2023, Institute of Mathematical Statistics. All rights reserved.Spatial analysis commonly relies on the estimation of a co-variance matrix associated with a random field. This estimation strongly impacts the prediction ...
On the robustness of the principal volatility components
(2018-03)
In this paper, we analyse the recent principal volatility components analysis procedure. The procedure overcomes several diculties in modelling and forecasting the conditional covariance matrix in large dimensions arising ...
Disparities Affecting Organ Donation Rates in Chile
(Wiley, 2024)
© 2023, Institute of Mathematical Statistics. All rights reserved.Spatial analysis commonly relies on the estimation of a co-variance matrix associated with a random field. This estimation strongly impacts the prediction ...
Error Covariance Penalized Regression: A novel multivariate model combining penalized regression with multivariate error structure
(Elsevier Science, 2018-06)
A new multivariate regression model, named Error Covariance Penalized Regression (ECPR) is presented. Following a penalized regression strategy, the proposed model incorporates information about the measurement error ...
Robust minimum information loss estimation
(Elsevier Science, 2013-09)
Two robust estimators of a matrix-valued location parameter are introduced and discussed. Each is the average of the members of a subsample–typically of covariance or cross-spectrum matrices–with the subsample chosen to ...
Multiradial matrix covariance functions: characterization and applications
(2014)
All results presented here concern to radial (isotropic) and multiradial (danisotropic) matrix-valued covariance functions. We specify some important properties of matrix-valued covariance functions associated to ...
Forecasting large covariance matrices: comparing autometrics and LASSOVAR
(2019)
This study aims to compare the performance of two well known automatic model selection algorithms, Autometrics (Hendry and Krolzig, 1999; Doornik, 2009), LASSOVAR and adaptive LASSOVAR (Callot et al., 2017) for modelling ...
Object Tracking Based on Covariance Descriptors and On-Line Naive Bayes Nearest Neighbor Classifier
(IEEE, 2010)
Object tracking in video sequences has been extensively studied in computer vision. Although promising results have been achieved, often the proposed solutions are tailored for particular objects, structured to specific ...
Signal detection in high dimension: the multispiked case
(Inst Mathematical Statistics, 2014-02)
This paper applies Le Cam's asymptotic theory of statistical experiments to the signal detection problem in high dimension. We consider the problem of testing the null hypothesis of sphericity of a high-dimensional covariance ...
Selection Criterion of Working Correlation Structure for Spatially Correlated Data
(2023)
To obtain regression parameter estimates in generalized estimation equation modeling, whether in longitudinal or spatially correlated data, it is necessary to specify the structure of the working correlation matrix. The ...