Multivariate Extremes: Structure and Dependence

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Copulas and Tail Dependence

Explores copulas, rank correlations, and tail dependence measures in risk management.

Continuous Random Variables

Explores continuous random variables, density functions, joint variables, independence, and conditional densities.

Multivariate Statistics: Introduction and Methods

Introduces multivariate statistics, focusing on uncovering associations between components in data in vector form.

Principal Component Analysis: Introduction

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

Copulas: Properties and Applications

Covers the properties and applications of copulas, including examples and coefficients of tail dependence.

Multivariate Statistics: Conditional Distributions

Covers conditional distributions and correlations in multivariate statistics, including partial variance and covariance, with applications to non-normal distributions.

Quantitative Risk Management: Distributions and Techniques

Covers distributions and techniques in Quantitative Risk Management for financial modeling.

Central Limit Theorem: Properties and Applications

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

Probabilities and Statistics

Covers fundamental concepts in probabilities and statistics, including linear regression, exploratory statistics, and the analysis of probabilities.

Copula Densities: Tail Dependence

Covers copula densities and tail dependence in quantitative risk management.