MM for penalized estimation

• Published in: Test (Madrid, Spain) Vol. 31; no. 1; pp. 54 - 75
• Main Author: Wang, Zhu
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2022; Springer Nature B.V
• Subjects: Algorithms; Economics; Feature selection; Finance; Generalized linear models; Insurance; Machine learning; Management; Mathematics and Statistics; Optimization; Original Paper; Outliers (statistics); Parameter estimation; Robustness; Statistical models; Statistical Theory and Methods; Statistics; Statistics for Business; 62J07; Classification; 68U01; MM algorithm; Regression; Quadratic majorization; 62H30; 62-08; Robust estimation; 62F35; Nonconvex; Variable selection

Penalized estimation can conduct variable selection and parameter estimation simultaneously. The general framework is to minimize a loss function subject to a penalty designed to generate sparse variable selection. The majorization–minimization (MM) algorithm is a computational scheme for stability...