Robust multivariate and high-dimensional statistics
Multivariate statistics refers to methods that examine the simultaneous effect of multiple variables. Multivariate techniques differ from univariate and bivariate analyses in that they direct attention away from the analysis of the mean and variance of a single variable, or from the pairwise relationships between two variables, and involve the analysis of covariances or correlations that reflect the extent of relationship between three or more variables, and analysis of distances which reflect similarity among variables. Dependence multivariate methods usually seek to explain or predict one or more dependent variable (response, outcome variable(s)) based upon the set of predictor variables (independent , covariate, explanatory variables). Multivariate robust methods attempt to provide valid results when the data contain anomalies such as presence of outliers, while high-dimensional statistics is concerned with the models, where the number of variables/characteristics may tend to infinity together with the sample size.
