The Stein Phenomenon and Superefficiency

Related lectures (32)

Page 3 of 4

Covers maximum likelihood theory, applications, and hypothesis testing principles in econometrics.

Delves into statistical estimation, highlighting the Gaussian linear model and the limitations of ML estimators.

Covers the concepts of parameter estimation, including unbiased estimators and Fisher information.

Covers estimation of parameters, MSE, Fisher information, and the Rao-Blackwell Theorem.

Covers the fundamental concepts of statistics and their application in data science.

Covers probability theory, distributions, and estimation in statistics, emphasizing accuracy, precision, and resolution of measurements.

Delves into maximum likelihood estimators, their properties, and asymptotic behavior, emphasizing consistency and asymptotic normality.

Covers the estimation of distributions using samples and probability models.

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Explores bias, variance, and confidence intervals in parameter estimation using examples and distributions.