Lectures related to Ronald Fisher

Fisher Information, Cramér-Rao Inequality, MLE

Explains Fisher information, Cramér-Rao inequality, and MLE properties, including invariance and asymptotics.

Sampling Distributions: Estimators and Variance

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

Explores Poisson GLM for analyzing count data in categorical analysis, focusing on contingency tables and Bayesian inference.

Estimation: Measures of Performance

Explores estimation measures of performance, including the Cramér-Rao bound and maximum likelihood estimation.

Linear Regression: Estimation and Inference

Explores linear regression estimation, linearity assumptions, and statistical tests in the context of model comparison.

Sampling Theory: Sufficiency and Ancillarity

Explores sufficiency and ancillarity in sampling theory, emphasizing the importance of sufficient statistics in compressing data without losing information.