By combining expressions A.1 and A.2 it becomes clear that the variance ratio is equal to 1 when the sum of population covariances is zero (population dynamics are independent), whereas values greater (less) than 1 indicate positive (negative) covariances and thus synchronous (compensatory) dynamics amongst populations. The variance ratio reflects the cumulative population dynamics rather than ...

The light curve of an exoplanetary transit can be used to estimate the planetary radius and other parameters of interest. Because accurate parameter estimation is a nonanalytic and computationally intensive problem, it is often useful to have analytic approximations for the parameters as well as their uncertainties and covariances. Here, we give such formulae, for the case of an exoplanet trans...

The mixture of inverse covariances model is a low-complexity, approximate decomposition of the inverse covariance matrices in a Gaussian mixture model which achieves high modeling accuracy with very good computational efficiency. In this model, the inverse covariances are decomposed into a linear combination of K shared prototype matrices. In this paper, we introduce an extension of this model ...

We consider problems of estimation of structured covariance matrices, and in particular of matrices with a Toeplitz structure. We follow a geometric viewpoint that is based on some suitable notion of distance. To this end, we overview and compare several alternatives metrics and divergence measures. We advocate a specific one which represents the Wasserstein distance between the corresponding G...

This paper describes a novel method to incorporate significantly time-lagged data into a sequential variational data assimilation framework. The proposed method can assimilate data that appear many assimilation window lengths in the future, providing a mechanism to gradually dynamically adjust the model towards those data. The method avoids the need for an adjoint model, significantly reducing ...

Many macroeconomic policy exercises consider the mean values of parameter estimates but do not use the variances and covariances. One can argue that the uncertainty of these parameter estimates is sufficiently small that it can safely be ignored. Or one can take the position that this kind of uncertainty cannot be avoided no matter what one does. Thus it is just as well to ignore it while makin...

Modeling data with Gaussian distributions is an important statistical problem. To obtain robust models one imposes constraints the means and covariances of these distributions [6, 4, 10, 8]. Constrained ML modeling implies the existence of optimal feature spaces where the constraints are more valid [2, 3]. This paper introduces one such constrained ML modeling technique called factor analysis i...