Dependence Concepts and Copulas

Related lectures (31)

Page 1 of 4

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

Introduces Principal Component Analysis, focusing on maximizing variance in linear combinations to summarize data effectively.

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

Covers descriptive statistics, hypothesis testing, and correlation analysis with various probability distributions and robust statistics.

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

Covers Canonical Correlation Analysis, a method to find relationships between two sets of variables.

Covers copulas, dependence structures, simulation techniques, and properties of copula densities.

Explores principal components, covariance, correlation, choice, and applications in data analysis.

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

Covers rank correlations, tail dependence, and copula fitting methods.