Covariance matrix estimation arises in multivariate problems including multivariate normal sampling models and regression models where random effects are jointly mod-eled, e.g. random-intercept, random-slope models. A Bayesian analysis of these problems requires a prior on the covariance matrix. Here we compare an inverse Wishart, scaled inverse Wishart, hierarchical inverse Wishart, and a sepa...

This paper addresses the question of the selection of multivariate GARCH models in terms of variance matrix forecasting accuracy with a particular focus on relatively large scale problems. We consider 10 assets from the NYSE and compare 125 model based one, five and twenty-day ahead conditional variance forecasts over a period of 10 years using the Model Confidence Set (MCS) and the Superior Pr...

Introduction Local canonical correlation analysis (CCA) is a multivariate method that takes into account the spatial correlation of neighboring voxels rather than treating each voxel time series individually. Mathematically, CCA is a generalization of the general linear model (GLM), and is defined by maximizing the correlation of a linear combination of voxel time series in a local region and a...

Quantitative genetic theory predicts that when populations diverge by drift the interspecific divergence (D matrix), calculated from species means, will be proportional to the average value of the additive genetic variance-covariance matrix, or G matrix. Most empirical studies in which this hypothesis has been investigated have ignored phylogenetic nonindependence among included taxa. Baker and...

Data processing in geodetic applications often relies on the least-squares method, for which one needs a proper stochastic model of the observables. Such a realistic covariance matrix allows one first to obtain the best (minimum variance) linear unbiased estimator of the unknown parameters; second, to determine a realistic precision description of the unknowns; and, third, along with the distri...

Smartphone apps are closely integrated with our daily lives, and mobile malware has brought about serious security issues. However, the features used in existing traffic-based detection techniques have a large amount of redundancy useless information, wasting computational resources training models. To overcome this drawback, we propose feature selection method; core method involves choosing se...

the objective of this study was to investigate the effect of principal components (pc) of the regression coefficients (co)variance matrix on the lactation curve, and on the formation of selection index to modify the curve, based on these pc’s, and as well to estimate additive genetic and phenotypic variances and heritability of the constructed selection index. lactation period of 301 d (5-305) ...