When prior estimates of regression coe cients along with their stan dard errors or their variance covariance matrix are available they can be incorporated into the estimation procedure through minimax linear and mixed regression approaches It is demonstrated that the mixed regres sion approach provides more e cient estimators at least asymptotically in comparison to the minimax linear approach ...

Functional connectivity refers to shared signals among brain regions and is typically assessed in a task free state. Functional connectivity commonly is quantified between signal pairs using Pearson correlation. However, resting-state fMRI is a multivariate process exhibiting a complicated covariance structure. Partial covariance assesses the unique variance shared between two brain regions exc...

Adaptive beamforming methods degrade in the presence of model mismatch. In this paper, we develop a modified interference covariance matrix reconstruction based beamformer that is robust against large array calibration errors. The calibration errors can come from the element position errors, and/or amplitude and phase errors, etc.. The proposed method is based on the fact that the sample covari...

Herein, a novel eigenstructure-based method for direction estimation is presented. The method assumes that the emitter signals are uncorrelated. Ideas from subspace and covariance matching methods are combined to yield a non-iterative estimation algorithm when a uniform linear array is employed. The large sample performance of the estimator is analyzed. It is shown that the asymptotic variance ...

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 ...