We investigate the conditional interest rate risk premium in Treasury bill futures returns. A one-factor model predicts that the premium depends on the conditional variance. An Intertemporal CAPM based two-factor model predicts that it also depends on conditional covariance with the equity premium. Univariate and bivariate Integrated GARCH-in-Mean models suggest that the premium relates positiv...

Integration and modularity are fundamental determinants of how natural selection effects evolutionary change in complex multivariate traits. Interest in the study of the specific developmental basis of integration through experimental approaches is fairly recent and it has mainly focused on its genetic determinants. In this study, we present evidence that postnatal environmental perturbations c...

Some new identities for quantum variance and covariance involving commutators are presented, in which the density matrix and the operators are treated symmetrically. A measure of entanglement is proposed for bipartite systems, based upon covariance. This works for two-and three-component systems but produces ambiguities for multicomponent systems of composite diemnsion. Its relationship to angu...

We compare the asymptotic covariance matrix of the ML estimator in a nonlinear measurement error model to the asymptotic covariance matrices of the CS and SQS estimators studied in Kukush et al (2002). For small measurement error variances they are equal up to the order of the measurement error variance and thus nearly equally efficient.

In Tables 1–4, we present key excerpts from the variance–covariance matrices of the coefficients for the original Models 3, 4, 5 and 6, all rounded to two significant digits. These matrices will allow interested readers to approximately replicate the calculation of the confidence intervals for the marginal effects of dimensionality that were presented in the main paper’s Table 3. The complete v...

Estimation of the autoregressive coefficient % in linear models with firstorder autoregressive disturbances has been broadly studied in the literature. Based on C.R. Rao’s MINQE-theory, Azäıs et al. (1993) gave a new general approach for computing locally optimum estimators of variance-covariance components in models with non-linear structure of the variance-covariance matrix. As a special case...

The rebmix package provides R functions for random univariate and multivariate finite mixture model generation, estimation, clustering and classification. The paper is focused on multivariate normal mixture models with unrestricted variance-covariance matrices. The objective is to show how to generate datasets for a known number of components, numbers of observations and component parameters, h...

BACKGROUND An increasing number of neuroscientific studies gain insights by focusing on differences in functional connectivity-between groups, individuals, temporal windows, or task conditions. We found using simulations that additional insights into such differences can be gained by forgoing variance normalization, a procedure used by most functional connectivity measures. Simulations indicate...

Patterns of co-occurrence of species are widely used to assess the fit of ecological neutral models to empirical patterns. The mathematically equivalent patterns of co-diversity of sites, in contrast, have been considered only indirectly and analyses normally are focused on the spatial distribution of species richness, rather than on the patterns of species sharing. Here we use two analytical t...

Body size is an important determinant of fitness in many organisms. While size will typically change over the lifetime of an individual, heritable components of phenotypic variance may also show ontogenetic variation. We estimated genetic (additive and maternal) and environmental covariance structures for a size trait (June weight) measured over the first 5 years of life in a natural population...