Modern Regression: Smoothing and Modelling Choices

Related lectures (32)

Covers linear and weighted regression, optimal parameters, local solutions, SVR application, and regression techniques' sensitivity.

Introduces regression analysis, covering linear and nonlinear models, Poisson regression, and failure time analysis using various datasets.

Explores non-parametric statistics, Bayesian methods, and linear regression with a focus on kernel density estimation and posterior distribution.

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

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 the solution of linear systems using direct methods and the evolution of matrices.

Covers the transition from linear to nonlinear models, focusing on k-NN and feature expansion techniques.

Introduces supervised learning, covering classification, regression, model optimization, overfitting, and kernel methods.

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

Explains the concept of least squares solutions and their application in finding the closest solution to a system of equations.

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