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Lecture
Multivariate Methods I
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Related lectures (56)
Principal Components: Properties & Applications
Explores principal components, covariance, correlation, choice, and applications in data analysis.
Principal Component Analysis: Dimension Reduction
Covers Principal Component Analysis for dimension reduction in biological data, focusing on visualization and pattern identification.
Canonical Correlation Analysis: Overview
Covers Canonical Correlation Analysis, a method to find relationships between two sets of variables.
Principal Component Analysis: Properties and Applications
Explores Principal Component Analysis theory, properties, applications, and hypothesis testing in multivariate statistics.
Multivariate Normal Distribution: Correlation and Covariance
Covers correlation, covariance, empirical estimates, eigenvalues, normality testing, and factor models.
Quantifying Statistical Dependence: Covariance and Correlation
Explores covariance, correlation, and mutual information in quantifying statistical dependence between random variables.
Market Response Functions
Explores market response functions, flash crashes, correlation estimation, and noise filtering in finance.
Principal Component Analysis: Theory and Applications
Covers the theory and applications of Principal Component Analysis, focusing on dimension reduction and eigenvectors.
Estimating the Term Structure: Principal Component Analysis
Covers Principal Component Analysis for yield curve shape estimation and dimension reduction in interest rate models.
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.
Dependence in Random Vectors
Explores dependence in random vectors, covering joint density, conditional independence, covariance, and moment generating functions.
Multivariate Statistics: Introduction and Methods
Introduces multivariate statistics, focusing on uncovering associations between components in data in vector form.
Spectral Clustering: Theory and Applications
Explores spectral clustering theory, eigenvalue decomposition, Laplacian matrix, and practical applications in identifying clusters.
Variance and Covariance: Properties and Examples
Explores variance, covariance, and practical applications in statistics and probability.
Clustering: K-means & LDA
Covers clustering using K-means and LDA, PCA, K-means properties, Fisher LDA, and spectral clustering.
PCA: Key Concepts
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Covers the key concepts of Principal Component Analysis (PCA) and its practical applications in data dimensionality reduction and feature extraction.
PCA: Key Concepts
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Covers the key concepts of PCA, including reducing data dimensionality and extracting features, with practical exercises.
Principal Component Analysis: Olympic Medals & Image Compression
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Explores PCA for predicting medals distribution and compressing face images.
Dimensionality Reduction: PCA & LDA
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Covers PCA and LDA for dimensionality reduction, explaining variance maximization, eigenvector problems, and the benefits of Kernel PCA for nonlinear data.
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