Explores statistical estimation, comparing estimators based on mean and variance, and delving into mean squared error and Cramér-Rao bound.
Explores optimal testing methods in statistics, focusing on the Neyman-Pearson framework and the construction of test functions for different types of hypotheses.
Explores model selection in statistics, discussing principles, probabilistic models, characteristics evaluation, and data visualization methods.
Covers Maximum Likelihood Estimation in statistical inference, discussing MLE properties, examples, and uniqueness in exponential families.
Delves into the duality between confidence intervals and hypothesis tests, emphasizing the importance of precision and accuracy in estimation.
Covers statistical estimation methods and explores practical examples of parameter estimation for different distributions.
