Sentence examples for useful multivariate from inspiring English sources

This paper applies the Lotka Volterra model to investigate the competitive interactions among energy, environment, and economy (3Es) in the U.S. The proposed LV-COMSUD (Lotka Volterra COmpetition Model for SUstainable Development) has satisfactory performance for model fitting and provides a useful multivariate framework to predict outcomes concerning these interactions.

How to best measure functional diversity is a much debated question, but Cadotte et al. [ 83] summarize five useful multivariate functional diversity measures.

Whereas, this may not be sufficiently high to forego adjuvant chemotherapy, these observations pave the way to develop a clinically useful multivariate prognostic model for TNBC.

It is useful to use multivariate GMDR with correlated multiple phenotypes of interests.

With this number of analytes, the information would be useful for both regulatory actions as well as for source identification using multivariate analysis or other "fingerprinting" methods.

In particular, the so-called canonical transformation was found to be extremely useful for multivariate analyses, as it allowed multivariate analyses to be broken into a series of corresponding, univariate analyses.

One of the most efficient and useful robust multivariate regressions is the minimum covariance determinant, which was proposed by Rousseeuw et al. (2004).

Problems associated with the use of multivariate analysis to produce useful prognostic groupings in patients from different centres, are discussed.

Therefore, although the RSM has been broadly used before and its intuitive interpretation is very useful, the use of multivariate methods such as geometric morphometrics is much more precise and should, at least, be combined with other traditional measures of wing shape.

Chromatographic fingerprinting as e.g. used in metabolomics experiments usually requires the use of multivariate techniques in order to obtain useful information.

Multiple imputation (MI) methods are particularly useful for imputing multivariate missing data, and use available data from multiple predictors and covariates to create a set of datasets for each missing value [ 76, 77].