In neuroimaging, data are often modeled using general linear models. Here, we focus on GLMs with error covariances which are modeled as a linear combination of multiple variance/covariance components. Each of these components is weighted by one variance parameter. In many analyses variance parameters are estimated using restricted maximum likelihood (ReML). Most classical approaches assume the ...

In this article, we are talking about the random subspaces method in forecasting under condition of incomplete data and estimation a full forecast based on set partial predictions. Centered predictions considered without loss generality. According to statistical model, off-diagonal elements correlation matrix with known mathematical expectation variance. case matrix, analytical expressions obta...

In this paper we propose a new multivariate GARCH model with timevarying correlations. We adopt the vech representation based on the conditional variances and the conditional correlations. While each conditional-variance term is assumed to follow a univariate GARCH formulation, the conditional-correlation matrix is postulated to follow an autoregressive moving average type of analogue. By impos...