Explores estimators, bias, and efficiency in statistics, emphasizing the trade-off between bias and variability.
Explores statistical estimation, comparing estimators based on mean and variance, and delving into mean squared error and Cramér-Rao bound.
Introduces maximum likelihood estimation for statistical parameter estimation, covering bias, variance, and mean squared error.
Explains statistical estimators for random variables and Gaussian distributions, focusing on error functions for integration.
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.
