We propose a spectral turning-bands approach for the simulation of second-order stationary vector Gaussian random fields. The approach improves existing spectral methods through coupling with importance sampling techniques. ...

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 ...

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 ...

Multi-Output Gaussian Processes (MOGPs) are the multivariate extension of Gaussian processes (GPs \cite{Rasmussen:2006}), a Bayesian nonparametric method for univariate regression. MOGPs address the multi-channel regression ...

Model error covariances play a central role in the performance of data assimilation methods applied to nonlinear state-space models. However, these covariances are largely unknown in most of the applications. A misspecification ...

The fluid velocity inside the tank of agricultural sprayers is an indicator of the quality of the mixture. This study aims to formulate the best generalized linear mixed model to infer the fluid velocity inside a tank under ...

In the analysis of spatial data, one is often interested in modeling conditional probability distributions, in order to assess the uncertainty in the values of the attribute under study and to predict functions of this ...

We describe and test the fiducial covariance matrix model for the combined two-point function analysis of the Dark Energy Survey Year 3 (DES-Y3) data set. Using a variety of new ansatzes for covariance modelling and testing, ...

Contemporary groups (CG) are used in genetic evaluation to account for systematic environmental effects of management, nutritional level, or any other differentially expressed group effect; however, because the functional ...