MATH-408: Regression methods

Lectures in this course (33)

Covers inference, weighted least squares, spring barley data analysis, and smoothing techniques.

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

Covers regression methods focusing on spline smoothing and penalised fitting to balance data fidelity and smoothness.

Covers iterative weighted least squares, generalized linear models, and model checking.

Covers point estimation, confidence intervals, and hypothesis testing for smooth functions using mixed models and spline smoothing.

Covers iterative weighted least squares, Poisson regression, and Bayesian analysis of spring barley data using mixed models.

Covers analysis of variance, model building, variable selection, and function estimation in regression methods.

Covers Inference, Model Building, Variable Selection, Robustness, Regularised Regression, Mixed Models, and Regression Methods.

Covers the introduction to generalized additive models and iterative weighted least squares for model checking and smooth fits.

Explores Laplace approximation for integral estimation using smooth convex functions and Gaussian distribution.

Explores the EM algorithm, extreme-value distribution fitting, and seasonal decomposition in regression models.

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