Sentence examples for multivariate statistical modeling from inspiring English sources

For multivariate statistical modeling of different phoneme classes, the first or second best-fitted candidates for different domains (and also for different frame sizes) mostly varied between MLD and MGLD (with p ∈ {0.25, 0.50, 0.75}), i.e., a mixture of Gaussian and Laplace distributions.

As the copula density function c X, which models inter-dimensional dependency, is non-unit for CLD and unit for CLID, the superiority of CLD over CLID shows how the modeling of inter-dimensional dependency contributes to the proper multivariate statistical modeling.

All multivariate statistical modeling was carried out in SIMCA P+12.0 (Umetrics, Umeå, Sweden).

Multivariate statistical modeling was performed by means of orthogonal partial least squares discriminant analysis (OPLS-DA) [25].

Multivariate statistical modeling using principal component analysis (PCA) was performed on the GC-MS spectra of four independently grown replicates.

Multivariate statistical modeling using principal component analysis (PCA) was performed on the 1H-NMR spectra of 12 independently grown replicates from each of the cell lines and the scores plot (principal components 1 vs 2) resulting from PCA (Figure 1c) revealed very clear discrimination between the intracellular metabolome of the three cell lines.

Descriptive statistics and multivariate statistical models were created to determine what factors influenced compliance and public opinions.

It may also help in identifying statistically independent storm events and improve multivariate statistical models of such events.

When each of these factors was incorporated as a continuous variable into a multivariate statistical model, increased proximity and transmitter visibility were each associated with statistically significant increases in interior house average RF power densities (both p < 0.01), whereas elevation was not (p = 0.10).

To tackle this issue, Gilens and Page constructed a multivariate statistical model, which includes three causal variables: the views of Americans in the ninetieth percentile of the income distribution (the rich), the views of Americans in the fiftieth percentile (the middle class), and the opinions of various interest groups, such as business lobbies and trade unions.

Choices are decomposed using discrete multivariate statistical models.