Exploration of human encephalitis data using advanced multivariate statistical modelling approaches that can capture the inherent complexity in the data is, therefore, crucial in understanding the causes of human encephalitis.
A multivariate model-based approach was used to determine end-point and variability at the end-point of blending processes.
A multivariate model selection approach was implemented to test for effects due to statin use.
A multivariate model selection approach was implemented to test for effects due to statin use, where we used a backward selection procedure to identify significant confounders.
With SUA data, we performed two types of analyses: one using the same CSP+ECOC algorithm as described above, and one using the regularized linear discriminant analysis (rLDA), which was shown to provide better results than other methods, such as the population vector and multivariate Gaussian modeling approaches [14].
In this paper, we will not deal with studies based on large samples of sites and using multivariate modelling, for which Bayesian approaches were proposed in the recent period [4, 30, 35, 37].
This requires a solid bioinformatics approach involving multivariate data analysis, mathematical modelling approaches, statistical learning techniques, and a team of experts.
Nonetheless, we can compare the performance of a high-dimensional multivariate approach in the same examples as the preceding two-area simulations: as the mass univariate approach fails here, success with multivariate modelling would show that the approach is at least worth pursuing, even if never guaranteed to succeed.
Multivariate modelling using a genetic algorithm approach was used to assess relationships between DNA methylation and age, anthropometry, nutrient status and blood cell counts.
