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Lecture
Principal Component Analysis: Introduction
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Related lectures (31)
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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.
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.
Dependence Concepts and Copulas
Explores dependence concepts, copulas, correlation fallacies, and rank correlations in statistics.
Copulas: Properties and Applications
Explores copulas in multivariate statistics, covering properties, fallacies, and applications in modeling dependence structures.
Principal Component Analysis: Properties and Applications
Explores Principal Component Analysis theory, properties, applications, and hypothesis testing in multivariate statistics.
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.
Multivariate Statistics: Normal Distribution
Introduces multivariate statistics, covering normal distribution properties and characteristic functions.
Discriminant Analysis: Bayes Rule
Covers the Bayes discriminant rule for allocating individuals to populations based on measurements and prior probabilities.
