Non-parametric Regression: Smoothing Techniques

This lecture by the instructor covers non-parametric regression techniques, focusing on splines, natural spline basis functions, penalized likelihood, smoothing matrices, cubic spline fits, bias-variance tradeoff, cross-validation, orthogonal functions, Fourier series expansion, kernel smoothing, wavelets, and modulation estimators. The lecture also discusses the estimation of smooth functions, orthogonal bases, Sobolev ellipsoids, modulators, risk minimization, and thresholding techniques. Additionally, it explores the use of orthogonal functions in regression, the choice of basis functions, and the implications of dimensionality in multivariate regression.