Estimation of covariance matrices

Related lectures (32)

Page 3 of 4

Explores Principal Component Analysis focusing on maximizing variance and finding eigenvalues.

Covers linear response, steady states, Girsanov transforms, and covariance limits in reversible diffusions.

Covers the derivation of PCA projection, error minimization, and eigenvector optimization.

Covers multi-arm bandits in reinforcement learning, exploring the trade-off between exploration and exploitation to minimize regret.

Discusses Monte Carlo VaR accuracy, confidence intervals, backtesting, and multivariate distributions.

Covers the estimation of R, focusing on moments and covariance.

Covers the Smith and Schlather max-stable models, exploring their validity and interpretation.

Explores the generation of multivariate Gaussian distributions and the challenges of factorizing covariance matrices.

Explores market response functions, flash crashes, correlation estimation, and noise filtering in finance.

Covers unsupervised learning with PCA and K-means for dimensionality reduction and data clustering.