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 the concept of point estimation in statistics, focusing on methods to estimate unknown parameters from a given sample.
Explores variance estimation, creating personal estimators, correcting bias, and understanding Mean Square Error in statistical analysis.
Explores Maximum Likelihood Estimation, covering assumptions, properties, distribution, shrinkage estimation, and loss functions.
Covers Maximum Likelihood Estimation in statistical inference, discussing MLE properties, examples, and uniqueness in exponential families.
