Nonnegative-Lasso and application in index tracking

DOI10.1016/j.csda.2013.08.012zbMath1471.62220OpenAlexW2009099296MaRDI QIDQ1615217

Hanzhong Liu, Lan Wu, Yuehan Yang

Publication date: 2 November 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2013.08.012

zbMATH Keywords

index trackingmultiplicative updatesvariable selection consistencynonnegative-Lasso

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Ridge regression; shrinkage estimators (Lasso) (62J07) Applications of statistics to actuarial sciences and financial mathematics (62P05)

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Cites Work

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• The Adaptive Lasso and Its Oracle Properties
• The composite absolute penalties family for grouped and hierarchical variable selection
• Least angle regression. (With discussion)
• Simultaneous analysis of Lasso and Dantzig selector
• Sup-norm convergence rate and sign concentration property of Lasso and Dantzig estimators
• Sharp Thresholds for High-Dimensional and Noisy Sparsity Recovery Using $\ell _{1}$-Constrained Quadratic Programming (Lasso)
• Regression coefficient and autoregressive order shrinkage and selection via the lasso
• Model Selection and Estimation in Regression with Grouped Variables
• Multiplicative Updates for Nonnegative Quadratic Programming
• A unified framework for high-dimensional analysis of \(M\)-estimators with decomposable regularizers

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