Copulas: Properties and Applications

Related lectures (31)

Page 1 of 4

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

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

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

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

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

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

Introduces key concepts in probability and random variables, covering statistics, distributions, and covariance.

Covers probability, statistics, independence, covariance, correlation, and random variables.

Explores the Central Limit Theorem, covariance, correlation, joint random variables, quantiles, and the law of large numbers.

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