Multivariate Extremes: Structure and Dependence

Related lectures (30)

Page 3 of 3

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

Covers the estimation of R using bivariate normal distributions and explores the marginal and conditional distributions of X₁ and X₂.

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

Introduces key statistical concepts like probability, random variables, and correlation, with examples and explanations.

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

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

Explores joint random variables, conditional density, and independence in probability and statistics.

Covers statistical estimation methods, including maximum likelihood and Bayesian estimation.

Delves into copulas, margins, and extremal dependence in statistics, covering topics like logistic copulas and Kendall's tau.

Explores Principal Component Analysis theory, properties, applications, and hypothesis testing in multivariate statistics.