Explores the Stein Phenomenon, showcasing the benefits of bias in high-dimensional statistics and the superiority of the James-Stein Estimator over the Maximum Likelihood Estimator.
Explores the consistency and asymptotic properties of the Maximum Likelihood Estimator, including challenges in proving its consistency and constructing MLE-like estimators.
Covers quantile regression, focusing on linear optimization for predicting outputs and discussing sensitivity to outliers, problem formulation, and practical implementation.
Covers extremal limit theorems, basic statistical analysis, and applications to multivariate extremes, emphasizing the importance of understanding the distribution of maxima.