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Lecture
Principal Components: Properties & Applications
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Related lectures (30)
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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.
Principal Component Analysis: Introduction
Introduces Principal Component Analysis, focusing on maximizing variance in linear combinations to summarize data effectively.
Principal Component Analysis: Properties and Applications
Explores Principal Component Analysis theory, properties, applications, and hypothesis testing in multivariate statistics.
Multivariate Statistics: Normal Distribution
Covers the multivariate normal distribution, properties, and sampling methods.
Maximum Likelihood Estimation: Multivariate Statistics
Explores maximum likelihood estimation and multivariate hypothesis testing, including challenges and strategies for testing multiple hypotheses.
Canonical Correlation Analysis: Overview
Covers Canonical Correlation Analysis, a method to find relationships between two sets of variables.
Multivariate Statistics: Normal Distribution
Introduces multivariate statistics, covering normal distribution properties and characteristic functions.
Multivariate Statistics: Introduction and Methods
Introduces multivariate statistics, focusing on uncovering associations between components in data in vector form.
Copulas: Properties and Applications
Explores copulas in multivariate statistics, covering properties, fallacies, and applications in modeling dependence structures.
Estimating the Term Structure: Principal Component Analysis
Covers Principal Component Analysis for yield curve shape estimation and dimension reduction in interest rate models.
