Sentence examples for modelling of multivariate from inspiring English sources

Extensions to joint modelling of multivariate extremes, incorporating extremal spatial dependence (using max-stable processes) or more general extremal dependence (using the conditional extremes approach) are outlined.

Effective modelling of multivariate extremes will improve the specification of design conditions for systems whose response cannot be easily characterised in terms of one variable.

In the scope of the scenario a theoretical model of multivariate oligomeric autocatalyst is presented.

In this paper, we introduce the concepts of asymmetric copulas for the modeling of multivariate ocean data.

This is a very important aspect for effective modeling of multivariate models, especially when part of the goal is to study the evolution of the internal states of the particles (i.e., composition, phase behavior, etc.).

The models of multivariate analysis were tested for their appropriateness before deciding to interpret the results.

This book introduces the main theoretical findings related to copulas and shows how statistical modeling of multivariate continuous distributions using copulas can be carried out in the R statistical environment with the package copula (among others).

Starting from this stylized facts, several authors build models of multivariate unobserved components in order to obtain in a joint manner the cycles and trends of different series.

The individual regression coefficients of these factors were saved and used as dependent variables in a model of multivariate analysis of variance (MANOVA).

The association between lipidomic profile and hypertension was subsequently examined using a model of multivariate analysis of variance (MANOVA) with these factors as dependent variables and hypertension status as an independent factor, thereby regarding BMI and HOMA as covariates.

We present two models of multivariate analysis.