Multivariate regression methodology is a commonly used approach for emulating dynamic models.
Multivariate Logistic Regression methodology was used to identify the most important biophysical and human variables to explain the emergence of ignition points, incorporating spatial analysis from Remote Sensing and Geographical Information Systems GISS) data.
The investigation was conducted using design-of-experiments methodology and evaluated by multivariate regression analysis.
Among multivariate techniques, Bayesian networks, kernel-based classifiers and multivariate regression are making their way as candidate new methodologies for the analysis of GWAS data [ 6- 11].
The methodology used was non-linear multivariate regression analysis, and the decision of which variables to use was based on previous publications [ 1, 8].
These methodologies have the advantage that they can systematically use multivariate regression models to select and combine multiple baseline covariates from different levels of analysis to define subgroups.
In this paper, a methodology is tested which combines Markov chains and logistic multivariate regression to model secondary succession after farmland abandonment in environments where abiotic constraints play a major role, like mountain areas.
Multivariate regression analysis.
Difference-in-difference model (multivariate regression).
Statistical analysis was by regression and multivariate regression techniques.
