Lectures related to Mixed-design analysis of variance

Explores roughness penalty, band matrices, and Bayesian inference in regression smoothing.

Explores the optimization and penalization of natural cubic splines, including roughness penalties and Bayesian inference.

Explains the construction of natural cubic splines, emphasizing smoothness and continuity in the function representation.

Explores GLM II, covering hypothesis testing, F-tests, multiple comparisons, and enriching the model to account for imaging artifacts.