Lectures related to Copulas and Margins: Extremal Dependence 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.

Copulas and Tail Dependence

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

Copulas: Properties and Applications

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

Multivariate Extremes: Structure and Dependence

Delves into multivariate extremes, analyzing structure variables, extremal dependence, componentwise maxima, and copulas.

Copulas: Properties and Applications

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

Dependence Concepts and Copulas

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

Probability Distributions in Environmental Studies

Explores probability distributions for random variables in air pollution and climate change studies, covering descriptive and inferential statistics.

Linear Independence: Covariance and Statistical Dependence

Explores covariance, statistical dependence, education-fertility relationship, hypothesis testing, and comparison statistics for continuous outcomes.

Multivariate Statistics: Normal Distribution

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

Sampling Theory: Statistics for Mathematicians

Covers the theory of sampling, focusing on statistics for mathematicians.

Multivariate Statistics: Conditional Distributions

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

Introduction to Continuous Random Variables: Probability Distributions

Introduces continuous random variables and their probability distributions, emphasizing their applications in statistics and data science.

Random Vectors & Distribution Functions

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

Probability Distributions: Central Limit Theorem and Applications

Discusses probability distributions and the Central Limit Theorem, emphasizing their importance in data science and statistical analysis.

Elliptical Distributions: Properties and Applications

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

Probability and Statistics: Fundamental Theorems

Explores fundamental theorems in probability and statistics, joint probability laws, and marginal distributions.

Copulas and Extreme Values

Explores copulas for measuring dependence strength in distributions and transforming variables to unit Fréchet margins.

Central Limit Theorem: Properties and Applications

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

Describing Data: Statistics and Hypothesis Testing

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