Sentence examples for multivariate data modeling from inspiring English sources

A new approach for powder characterization based on image analysis, "Angle Measure Technique" (AMT) and multivariate data modeling is presented.

In PCM interaction activity data are correlated to the physicochemical and structural descriptions of proteins and ligands and their-derived protein-ligand cross-description, using a suited multivariate data modeling technique.

In this paper, a multivariate data modeling approach is proposed based on modified kernel partial least squares (MKPLS) with the signal filtering method.

Each solution image was characterised by a range of dedicated, quantitative descriptors, and the results interpreted by multivariate data modelling.

Multivariate data modelling in latent variables has proven to be a highly useful approach for extracting and displaying the main co-variations in high-dimensional response profiles.

This should be addressed so that we can make a realistic assessment of to which degree a combined use of system dynamics methodology and multivariate data modelling in latent variables has the potential to become a generic approach to explain higher-order differentiation phenomena.

The study derives the analytical expressions of fuzzy membership functions on variables of the multivariate data model by maximizing the over-uncertainties-averaged-log-membership values of data samples around an initial guess.

Because CFA models involve fitting complex multivariate data, model fit is evaluated by inspecting several fit indices [ 15].

By combining computer simulations according to a succession of statistical experimental designs, computer graphics, automatic image analysis, human sensory descriptive analysis and multivariate data modelling, we derive a pattern descriptor model of those macroscopic, emergent aspects of the patterns that we consider of interest.

Density and strength of the basic sites of the materials were related to the synthesis parameters by applying the multivariate analysis to data modelling.

Successful research programs in Europe include the development and application of statistically designed experiments combined with multivariate data analysis and modeling in vitro and in vivo studies on a wide variety of chemicals such as petroleum hydrocarbons, aldehydes, food contaminants, industrial solvents, and mycotoxins.