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Lecture
Canonical Correlation Analysis
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Related lectures (31)
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Canonical Correlation Analysis: Overview
Covers Canonical Correlation Analysis, a method to find relationships between two sets of variables.
Maximum Likelihood Estimation: Multivariate Statistics
Explores maximum likelihood estimation and multivariate hypothesis testing, including challenges and strategies for testing multiple hypotheses.
Principal Components: Properties & Applications
Explores principal components, covariance, correlation, choice, and applications in data analysis.
Multivariate Statistics: Normal Distribution
Covers the multivariate normal distribution, properties, and sampling methods.
Principal Component Analysis: Introduction
Introduces Principal Component Analysis, focusing on maximizing variance in linear combinations to summarize data effectively.
Multivariate Statistics: Wishart and Hotelling T²
Explores the Wishart distribution, properties of Wishart matrices, and the Hotelling T² distribution, including the two-sample Hotelling T² statistic.
Multivariate Statistics: Conditional Distributions
Covers conditional distributions and correlations in multivariate statistics, including partial variance and covariance, with applications to non-normal distributions.
Copulas: Properties and Applications
Explores copulas in multivariate statistics, covering properties, fallacies, and applications in modeling dependence structures.
Dependence Concepts and Copulas
Explores dependence concepts, copulas, correlation fallacies, and rank correlations in statistics.
Principal Component Analysis: Properties and Applications
Explores Principal Component Analysis theory, properties, applications, and hypothesis testing in multivariate statistics.
