Explores estimators, bias, and efficiency in statistics, emphasizing the trade-off between bias and variability.
Covers likelihood ratio statistic, confidence intervals, and hypothesis testing concepts.
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.
Delves into maximum likelihood estimators, their properties, and asymptotic behavior, emphasizing consistency and asymptotic normality.
Introduces Discrete Choice Analysis, covering scale, depth, data collection, and statistical inference.
