Nonnegative adaptive Lasso for ultra-high dimensional regression models and a two-stage method applied in financial modeling

DOI10.1016/j.jspi.2016.01.011zbMath1353.62076OpenAlexW2276809835MaRDI QIDQ274029

Lan Wu, Yuehan Yang

Publication date: 22 April 2016

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jspi.2016.01.011

zbMATH Keywords

asymptotic normalityindex trackingasymptotic unbiasednessmultiplicative updatesnonnegative adaptive Lassovariable selection consistency

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Ridge regression; shrinkage estimators (Lasso) (62J07) Asymptotic properties of nonparametric inference (62G20)

Related Items (14)

Penalized and constrained LAD estimation in fixed and high dimension ⋮ Variable selection in high-dimensional linear model with possibly asymmetric errors ⋮ Model selection consistency of Lasso for empirical data ⋮ Rates of convergence of the adaptive elastic net and the post-selection procedure in ultra-high dimensional sparse models ⋮ Nonnegative estimation and variable selection via adaptive elastic-net for high-dimensional data ⋮ An integrated precision matrix estimation for multivariate regression problems ⋮ High-dimensional sparse index tracking based on a multi-step convex optimization approach ⋮ One-step sparse estimates in the reverse penalty for high-dimensional correlated data ⋮ Efficient sparse portfolios based on composite quantile regression for high-dimensional index tracking ⋮ High-dimensional sparse portfolio selection with nonnegative constraint ⋮ High-dimensional index tracking based on the adaptive elastic net ⋮ Detecting direct causality in multivariate time series: a comparative study ⋮ High-dimensional sign-constrained feature selection and grouping ⋮ Multivariate sparse Laplacian shrinkage for joint estimation of two graphical structures

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• The Adaptive Lasso and Its Oracle Properties
• Non-negative least squares for high-dimensional linear models: consistency and sparse recovery without regularization
• A note on adaptive group Lasso
• The composite absolute penalties family for grouped and hierarchical variable selection
• Nonnegative-Lasso and application in index tracking
• Asymptotics for Lasso-type estimators.
• Sign-constrained least squares estimation for high-dimensional regression
• Simultaneous analysis of Lasso and Dantzig selector
• On the adaptive elastic net with a diverging number of parameters
• Nonnegative elastic net and application in index tracking
• The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).
• High-dimensional graphs and variable selection with the Lasso
• Better Subset Regression Using the Nonnegative Garrote
• Greed is Good: Algorithmic Results for Sparse Approximation
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• 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
• Regularization and Variable Selection Via the Elastic Net
• On the Non-Negative Garrotte Estimator
• Model Selection and Estimation in Regression with Grouped Variables
• Multiplicative Updates for Nonnegative Quadratic Programming

This page was built for publication: Nonnegative adaptive Lasso for ultra-high dimensional regression models and a two-stage method applied in financial modeling