Multivariate normal distribution

Related lectures (31)

Page 2 of 4

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

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

Covers the Feynman Rules, Asymptotic Statistics, Normal Ordering, and Instantons.

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

Explains multivariate Gaussian distribution properties and moment generating functions for random vectors.

Explores bias, variance, unbiased estimators, and confidence intervals in statistical estimation.

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

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

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

Covers the mathematical development of canonical correlation analysis, including population and sample CCA.