Copulas: Properties and Applications

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Related lectures (31)

Page 3 of 4

Probability and Statistics

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

Dependence Measures: Rank Correlations

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

Probability and Statistics: Fundamental Theorems

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

Probability and Statistics

Covers p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.

Joint Distributions

Explores joint distributions, marginal laws, covariance, correlation, and variance properties.

Multivariate Statistics: Conditional Distributions

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

Probability Models: Fundamentals

Introduces the basics of probability models, covering random variables, distributions, and statistical estimation.

Multivariate Normal Distribution: Correlation and Covariance

Covers correlation, covariance, empirical estimates, eigenvalues, normality testing, and factor models.

Multivariate Statistics: Introduction and Methods

Introduces major statistical methodologies for uncovering associations between vector components in multivariate data.

Estimating R: Marginal and Conditional Distributions

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