Multivariate models and inference

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Classical statistical methods for multivariate statistics and time series have an assumption that the distributions of observations has a bell-shaped curve. Multivariate statistics consists of methods for analyzing data that has the form of many different numerical measurements or variables for many different subjects or cases; one inference of interest is the amount of association or relationship among different variables. Also a form of dependence called tail dependence is very important to study when the variables are related to financial and insurance risks. Time series consists of methods for analyzing data of the form of measurements of a main variable obtained sequentially over time and possibly other co-variables that are measured simultaneously; one inference of interest is to predict or forecast future values of the time series.