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.
Covers Maximum Likelihood Estimation, focusing on ML Estimation-Distribution, Shrinkage Estimation, and Loss functions.
Explores distribution estimation, constraints in estimators, and competitive analysis for robust estimation.
Explores Maximum Likelihood Estimation, covering assumptions, properties, distribution, shrinkage estimation, and loss functions.
Explores statistical estimation, regression models, and model selection in parametric models.
