Multivariate statistics

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Related lectures (32)

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Canonical Correlation Analysis

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

Elliptical Distributions: Properties and Applications

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

Principal Components: Properties & Applications

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

Discriminant Analysis: Bayes Rule

Covers the Bayes discriminant rule for allocating individuals to populations based on measurements and prior probabilities.

Principal Component Analysis: Introduction

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

Multivariate Extremes: Structure and Dependence

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

Random Walks on Discrete Spaces

Explores random walks on discrete spaces and their properties, including multivariate random variables and Poisson distributions.

Multivariate Statistics: Conditional Distributions

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

Acceptance-Rejection Methods: Advanced Techniques

Explores advanced techniques in acceptance-rejection methods and sampling from normal distributions.

Random Vectors & Distribution Functions

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