Sentence examples for multivariate distributions based from inspiring English sources

Inter-relationships between kindred variables are taken into account using multivariate distributions based on copulas and the marginal distributions involved.

Unlike almost all past models of this type, the inter-relationships between kindred variables are taken into account using multivariate distributions based on copulas and the marginal distributions involved.

In the recent statistical literature several methodologies of constructing bivariate and multivariate distributions based on marginal and conditional distributions have been proposed; see the works by Arnold et al. (1999 20011), Kotz et al. (2000), Sarabia and Gómez-Déniz (2008) and Balakrishnan and Lai (2009) among others.

For each virtual patient, the fixed effects were sampled from a multivariate normal distribution based on the uncertainty of the fixed effect estimate; the random effects were sampled from a multivariate normal distribution to account for intersubject variability.

Multivariate analyses showed that solitary and gregarious individuals from different populations representing the two mtDNA clades had almost non-overlapping distributions based on multiple song and morphological measurements.

Relative excess risks of death (RER) were estimated using a multivariate generalized linear model with a Poisson distribution, based on collapsed relative survival data, using exact survival times.

We provide two stochastic representations for the slash t distribution based on the two stochastic representations of the multivariate t distribution.

The approach based on vine copulas is a way to construct multivariate distributions using bivariate copulas as building blocks.

Multivariate distributions can also be defined using limited experimental data, adopting an efficient approach based on a mixture of one or more multivariate Gaussian distribution components [12].

A number of well-known multivariate distributions, such as a multinomial distribution, multivariate hypergeometric distribution, negatively correlated normal distribution and joint distribution of ranks, possess the NA property.

A number of well-known multivariate distributions possess the NA property, such as the multinomial distribution, multivariate hypergeometric distribution, negatively correlated normal distribution, and joint distribution of ranks.