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Lecture
Principal Component Analysis: Introduction
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Related lectures (53)
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.
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.
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.
Quantitative Risk Management: Distributions and Techniques
Covers distributions and techniques in Quantitative Risk Management for financial modeling.
Principal Component Analysis: Dimensionality Reduction
Covers Principal Component Analysis for dimensionality reduction, exploring its applications, limitations, and importance of choosing the right components.
Multivariate Statistics: Introduction and Methods
Introduces multivariate statistics, focusing on uncovering associations between components in data in vector form.
Multivariate Normal Distribution: Correlation and Covariance
Covers correlation, covariance, empirical estimates, eigenvalues, normality testing, and factor models.
Multivariate Statistics: Introduction and Methods
Introduces major statistical methodologies for uncovering associations between vector components in multivariate data.
Estimation Methods in Probability and Statistics
Discusses estimation methods in probability and statistics, focusing on maximum likelihood estimation and confidence intervals.
Estimating the Term Structure: Principal Component Analysis
Covers Principal Component Analysis for yield curve shape estimation and dimension reduction in interest rate models.
Red bus/Blue bus paradox
Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.
Principal Component Analysis: Dimension Reduction
Covers Principal Component Analysis for dimension reduction in biological data, focusing on visualization and pattern identification.
PCA: Directions of Largest Variance
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Covers PCA, finding directions of largest variance, data dimensionality reduction, and limitations of PCA.
Elements of Statistics: Probability, Distributions, and Estimation
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Covers probability theory, distributions, and estimation in statistics, emphasizing accuracy, precision, and resolution of measurements.
Clustering & Density Estimation
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Covers clustering, PCA, LDA, K-means, GMM, KDE, and Mean Shift algorithms for density estimation and clustering.
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