We present an intertemporal, rational expectations equilibrium model where the cross-sectional covariances and correlations of international market returns increase during bad times, as a consequence of an endogenous increase in the “uncertainty” about the global economy. We assume that the drift rates of the fundamental processes of international economies are jointly affected by an unobservab...

Collocation is a popular method in geodesy for combining heterogeneous data of different kind. It comprises adjustment, interpolation and extrapolation as special cases. Current methods of collocation apply however only if the trend parameters are real valued. In the present contribution we will generalize the theory of collocation by permitting the trend parameters to be integer valued. It wil...

Genetic evaluation by best linear unbiased prediction (BLUP) requires modeling genetic means, variances, and covariances. This paper presents theory to model means, variances, and covariances in a multibreed population, given marker and breed information, in the presence of gametic disequilibrium between the marker locus (ML) and linked quantitative trait locus (MQTL). Theory and algorithms are...

The SPAM model was recently proposed as a very general method for modeling Gaussians with constrained means and covariances. It has been shown to yield significant error rate improvements over other methods of constraining covariances such as diagonal, semi-tied covariances, and extended maximum likelihood linear transformations. In this paper we address the problem of discriminative estimation...

In this paper we propose a new algorithm to perform clustering of multivariate and functional data. We study the case of two populations different in their covariances, rather than in their means. The algorithm relies on a proper quantification of distance between the estimated covariance operators of the populations, and subdivides data in two groups maximising the distance between their induc...

The importance of diversification and risk management was recognized by a few visionaries in the early days of investing. Only in the 1950's, however, did these concepts become central. Since then, increasingly sophisticated mathematical and statistical tools have been brought to bear on the problem of estimating the aggregate risk of a portfolio. This risk depends crucially on the covariances ...

Data association is one of the core problems of simultaneous localization and mapping (SLAM), and it requires knowledge about the uncertainties of the estimation problem in the form of marginal covariances. However, it is often difficult to access these quantities without calculating the full and dense covariance matrix, which is prohibitively expensive. We present a dynamic programming algorit...

The Matérn covariance function is a popular choice for prediction in spatial statistics and uncertainty quantification literature. A key benefit of the class that it possible to get precise control over degree mean-square differentiability random process. However, possesses exponentially decaying tails, thus, may not be suitable modeling polynomially dependence. This problem can remedied using ...