Multivariate Extremes: Structure and Dependence

Related lectures (30)

Page 1 of 3

Introduces multivariate statistics, covering normal distribution properties and characteristic functions.

Explores dependence concepts, copulas, correlation fallacies, and rank correlations in statistics.

Covers copulas, Sklar's Theorem, types of copulas, and simulation of copulas for risk management.

Explores copulas in multivariate statistics, covering properties, fallacies, and applications in modeling dependence structures.

Covers copulas, Sklar's Theorem, meta distributions, and various dependence measures like rank correlations and coefficients of tail dependence.

Covers the multivariate normal distribution, properties, and sampling methods.

Covers elliptical distributions, including properties, applications, and risk management implications.

Introduces major statistical methodologies for uncovering associations between vector components in multivariate data.

Covers random vectors, joint distribution, conditional density functions, independence, covariance, correlation, and conditional expectation.

Explores maximum likelihood estimation and multivariate hypothesis testing, including challenges and strategies for testing multiple hypotheses.