Sentence examples for methods to multivariate from inspiring English sources

We applied time-series methods to multivariate sentinel surveillance data recorded in Hong Kong during 1998 2007.

Here, we extend the RQA analysis methods to multivariate observations and present a method by which the "length scale" parameter ε (the only parameter required for RQA) may be selected.

A straight-forward approach to extend multi-stage methods to multivariate models is lacking and requires further research.

Methods of phylogenetic regression [ 12, 80, 81] extend least squares methods to multivariate phylogenetic data by incorporating branch length and topological information into the model's covariance matrix.

Using Mx to apply sophisticated modelling methods to multivariate and longitudinal sets of dental data from twins offers great potential for clarifying how genetic and environmental factors influence development of the human dentition.

It is straightforward to extend this method to multivariate DFs. 1) The Gaussian copula LF is sensitive to the linear correlation parameter ρ.   2) Even so, ρ in the copula LF is remarkably stable regarding redshifts (from 0.95 at z = 0 to 0.85 at z = 1.0).

A recent study found that for small sample sizes, univariate methods faired comparably to multivariate methods, whose performance may be affected by overfitting (Lai et al, 2006).

A variety of topics is explored, from the importance of theory for the scientific method in general to multivariate data analysis in particular.

Finally, the orthogonal LS PLS method is extended to multivariate responses and used to identify the source of the observed block effect and to build models that can be used for statistical process control at several points in the process.

This is the ninth in a series of monographs on research design and analysis, and the third in a set of these monographs devoted to multivariate methods.

In many ways, neural networks can be viewed as nonlinear approaches to multivariate statistical methods, not bound by assumptions of normality or linearity.