Quadratic Programming and Penalized Regression

DOI10.1080/03610926.2012.732177zbMath1347.62061OpenAlexW1970002611MaRDI QIDQ4929194

Andrew D. A. C. Smith

Publication date: 13 June 2013

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: http://eprints.uwe.ac.uk/27627/1/AuthorFinalVersion27627.pdf

zbMATH Keywords

total variationquadratic programmingmultiresolutionnonparametric regressionpenalized regression

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Ridge regression; shrinkage estimators (Lasso) (62J07) Linear inference, regression (62J99) Quadratic programming (90C20)

Cites Work

• Nonlinear total variation based noise removal algorithms
• Improved predictions penalizing both slope and curvature in additive models
• Locally adaptive regression splines
• Image sharpening by flows based on triple well potentials
• Local extremes, runs, strings and multiresolution. (With discussion)
• An \(O(n^ 3L)\) primal interior point algorithm for convex quadratic programming
• Approximating data with weighted smoothing splines
• Numerical Optimization
• Ideal spatial adaptation by wavelet shrinkage
• Sparsity and Smoothness Via the Fused Lasso
• Regularization and Variable Selection Via the Elastic Net

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