Nested covariance models, defined as linear combinations of basic covariance functions, are very popular in many branches of applied statistics, and in particular in geostatistics. A notorious limit of nested models is ...

Utilizar modelos no lineales de efectos mixtos para el desarrollo de modelos farmacocinéticos poblacionales es una de las metodologías que más se aplican cuando se requiere caracterizar la disposición de un fármaco en el ...

The covariance is a statistical technique that is widely used to measure the dispersion between two sets of elements. This work develops new covariance measures by using the ordered weighted average (OWA) operator and ...

We propose a spectral turning-bands approach for the simulation of second-order stationary vector Gaussian random fields. The approach improves existing spectral methods through coupling with importance sampling techniques. ...

We propose a probability model for random partitions in the presence of covariates. In other words, we develop a model-based clustering algorithm that exploits available covariates. The motivating application is predicting ...

Linear covariant gauges, such as Feynman gauge, are very useful in perturbative calculations. Their non-perturbative formulation is, however, highly non-trivial. In particular, it is a challenge to define linear covariant ...

Proportionality of phenotypic and genetic distance is of crucial importance to adequately focus on population history and structure, and depends on proportionality of genetic and phenotypic covariance. Constancy of phenotypic ...

In spatial statistics, the screening effect historically refers to the situation when the observations located far from the predict and receive a small (ideally, zero) kriging weight. Several factors play a crucial role ...