A two-dimensional form of cross-covariance function between the radar radialand tangential-components (with respect to the direction of radar beam) of background wind errors is derived. Like the previously derived auto-covariance function for the radial-component, this cross-covariance function is homogeneous but non-isotropic in the horizontal. The autoand cross-covariance functions are used w...

Canonical correlation analysis (CCA) is a statistical method that can be preferable to the general linear model (GLM) for analysis of functional magnetic resonance imaging (fMRI) data. There are, however, two problems with CCA based fMRI analysis. First, it is not feasible to use a parametric approach to calculate an activity threshold for a certain significance level. Second, two covariance ma...

We specify a class of Gaussian rank-r latent models for cross-covariance. We show by construction that any variance-covariance matrix for the observed variables induced by rank-r reduced-rank regression can be induced by a rank-r latent model. 1 Model specification Basic terms are introduced which will be used to state the result. 1.1 Rank-r constraint models Let p be the number of X-variables ...

We develop a method for estimating well-conditioned and sparse covariance and inverse covariance matrices from a sample of vectors drawn from a sub-gaussian distribution in high dimensional setting. The proposed estimators are obtained by minimizing the quadratic loss function and joint penalty of `1 norm and variance of its eigenvalues. In contrast to some of the existing methods of covariance...

A two-dimensional form of cross-covariance function between the radar radialand tangential-components (with respect to the direction of radar beam) of background wind errors is derived. Like the previously derived auto-covariance function for the radial-component, this cross-covariance function is homogeneous but non-isotropic in the horizontal. The autoand cross-covariance functions are used w...

Purpose The hippocampus plays a central role in cognitive and affective processes and is commonly implicated in neurodegenerative diseases. Our study aimed to identify and describe a hippocampal network model (HNM) using trans-diagnostic MRI data from the BrainMap® database. We used meta-analysis to test the network degeneration hypothesis (NDH) (Seeley et al., 2009) by identifying structural a...

In this paper we consider covariance structural models with which we associate semidefinite programming problems. We discuss statistical properties of estimates of the respective optimal value and optimal solutions when the ‘true’ covariance matrix is estimated by its sample counterpart. The analysis is based on perturbation theory of semidefinite programming. As an example we discuss asymptoti...

This paper presents algorithms for efficiently computing the covariance matrix for features that form sub-windows in a large multidimensional image. For example, several image processing applications, e.g. texture analysis/synthesis, image retrieval, and compression, operate upon patches within an image. These patches are usually projected onto a low-dimensional feature space using dimensionali...

This report presents a thorough convergence analysis of Kronecker graphical lasso (KGLasso) algorithms for estimating the covariance of an i.i.d. Gaussian random sample under a sparse Kronecker-product covariance model. The KGlasso model, originally called the transposable regularized covariance model by Allen et al [1], implements a pair of `1 penalties on each Kronecker factor to enforce spar...