Lectures related to Factor analysis

Latent variables in choice models: Optima case study

Explores the Optima case study, analyzing attitudes and mode choice.

Latent Factor Analysis: Movie Genre Classification

Explores latent factor analysis for movie genre classification based on male versus female leads.

Principal Component Analysis: Properties and Applications

Explores Principal Component Analysis theory, properties, applications, and hypothesis testing in multivariate statistics.

Factor Models: Latent Variables and Asset Pricing

Covers Factor Models, including PCA, asset pricing, factor investing, GMM, and Fama-McBeth estimation.

Wurzelkriterium: Majorantenkriterium

Covers the Majorantenkriterium and Wurzelkriterium for convergence tests of series.

Quantitative Risk Management: Distributions and Techniques

Covers distributions and techniques in Quantitative Risk Management for financial modeling.

Multivariate Distributions: Spherical and Elliptical

Explores spherical and elliptical distributions, normal variance mixtures, factor models, and principal component analysis.

Statistical Analysis of Networks: Link Prediction and Biclustering

Explores link prediction, logistic regression, causal inference, and biclustering in statistical network analysis.

Functional Factor Models: Forecasting Mortality Curves in Japan

Explores high-dimensional functional factor models for forecasting mortality curves in Japan, discussing estimation, consistency, and application.

Understanding Preference Elicitation

Explores preference elicitation, latent variables, and the impact of external events on decision-making.

Structuring exploratory spatial data analysis

Explores the structured approach to exploratory spatial data analysis, emphasizing the importance of analytical frameworks and the Visual Seeking Mantra.

Factor Models: Portfolio Optimization and APT

Covers mean-variance portfolio choice, factor models, APT, Sharpe ratio, size and value anomalies, Fama and French models, and factor search.

Dimensionality Reduction: PCA & LDA

Covers PCA and LDA for dimensionality reduction, explaining variance maximization, eigenvector problems, and the benefits of Kernel PCA for nonlinear data.

Stochastic Simulation: LHS Estimator and Variances Analysis

Covers the analysis of the LHS estimator and variances in stochastic simulation.

Principal Component Analysis: Dimensionality Reduction

Covers Principal Component Analysis for dimensionality reduction, exploring its applications, limitations, and importance of choosing the right components.

Convergence Criteria: Limits Calculation

Covers the convergence criteria for sequences and limits calculation, discussing properties, rules, examples, and boundedness.

PCA: Interactive class

On PCA includes interactive exercises and emphasizes minimizing information loss.

Statistical Signal Processing

Covers Gaussian Mixture Models, Denoising, Data Classification, and Spike Sorting using Principal Component Analysis.

Principal Component Analysis: Eigenfaces

Covers the application of Principal Component Analysis in facial recognition using a famous faces dataset.

Multivariate Methods II: ICA for Functional Brain Imaging

Explores Independent Component Analysis in functional brain imaging, focusing on non-Gaussian sources, cleaning artefacts, and group studies.