To learn the graph structure as a multivariate Gaussian graphical model, we can use either L-1 regularization, or neighborhood selection algorithms.

Other variables were included in the multivariate models if univariate p-values fell below 0.20.

Patients with missing information for variables were excluded from the multivariate models, resulting in models smaller than the total population.

Two sets of multivariate models were developed.

Certain deterministic recursive multivariate models which include threshold effects have been shown to produce fractal effects.

Therefore, in all multivariate models the drinking water variable was retained regardless of its statistical significance.

So we had probably 20 other kinds of control variables in a multivariate model, and here's what popped out.

The factor regression model, or hybrid factor model, is a special multivariate model with the following form.

Some of the estimation methods for multivariate linear models are:

Artificial neural networks extend regression and clustering methods to non-linear multivariate models.