Sentence examples for multivariate normal data from inspiring English sources

The proposed method is applied to simulated multivariate normal data via MATLAB software.

The most common way of identifying multivariate outliers in a multivariate normal data set is to calculate Mahalanobis distance.

Kuiper and Fisher [15] compared six hierarchical clustering procedures (single linkage, complete linkage, median, average linkage, centroid and Ward's method) for multivariate normal data, assuming that the true number of clusters was known.

That means, these models employed here are more flexible and more appropriate in case of categorical (ordinal) response variables, as they do not assume a linear relationship between the observed and latent variables and are not based on the assumption of multivariate normal data.

On multivariate normal data, the calculated RTM estimates are identical to true estimates.

Multivariate normal data (Yi1, Zi1,…Yij, Zij,….,Yik, Zik) are generated with mean zero and correlation matrix ∑k.

Both strategies investigated for building the model using the multivariate normal imputed data resulted in the identical set of variables and interactions being identified; while models built using data imputed by fully conditional specification were marginally different for the two strategies.

This approach has been used in the program BayesTraits [ 37], which can fit multiple regression models to multivariate Normal trait data.

Given the SNP data and a (possibly cyclic) directed network structure, G, with continuous (gene expression) and discrete (SNP) nodes, we generated multivariate normal gene expression data with a covariance structure implied by the structural equation model (SEM) describing G.

During the first step, for each of the cornerstones the density of the feature vector is approximated by a multivariate normal distribution using data intervals with staging labels only.

Bootstrapping (5000 samples) was used to manage the presence of multivariate non-normal data within the subsample [ 20].