Concept

Smoothing

Related lectures (12)

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

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

Covers nonparametric regression, scatterplot smoothing, kernel methods, and bias-variance tradeoff.

Explores nonparametric regression using local polynomial estimation to balance data fidelity and smoothness.

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

Covers nonparametric regression using kernel-based estimation techniques to model complex relationships between variables.

Explores B-splines, natural cubic splines, and smoothing splines in regression problems and their practical applications.

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

Covers neural signals, brain enthusiasm, neuroimaging, and statistical analysis in neuroimaging studies.

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

Explores overdispersion, model assessment, and regression techniques for count data.

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