Lectures related to Distance correlation

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

Explores self-supervised learning, embedding techniques, HSIC, and geometric information in downstream tasks.

Explores Principal Component Analysis, dimensionality reduction, data quality assessment, and error rate control.

Explores the technical derivation and properties of Multivariate Extreme Value models.

Covers the Lindeberg-Rafeller theorem, discussing characteristic functions, moment problems, and the Central Limit Theorem.

Delves into the complexities of extending from finite variance to finite expectation assumptions.

Covers the theory and applications of Principal Component Analysis, focusing on dimension reduction and eigenvectors.

Explores correlation matrices, regression, variance, confidence intervals, and standardized systems in statistical modeling.

Explores Euclidean correlation functions and the transition to real time using Wick rotation, along with a discussion on good and bad habits.

Explores Gaussian processes, covariance functions, intrinsic stationarity, and extreme applications in statistics.

Covers Canonical Correlation Analysis, a method to find relationships between two sets of variables.

Explores shrinkage estimation of high-dimensional covariance matrices, comparing linear and nonlinear approaches for improved accuracy.

Covers the transition from Euclidean to real time by analytic continuation.

Introduces multivariate statistics, focusing on uncovering associations between components in data in vector form.

Explores Yule Walker equations for efficient implementation and correlation analysis in signal processing.

Covers common distributions, moment generating functions, and covariance matrices in statistics for data science.

Explores risk management concepts, including VaR, ES, and measurement methods.

Introduces spatial autocorrelation, focusing on measuring spatial relationships in a neighborhood and using Moran's I as a regression coefficient.

Explains the definition and properties of the Wiener process, focusing on its covariance function and continuous paths.

Explores correlation functions, Gaussian integrals, and perturbation theory in quantum field theory.