Estimator of prediction error based on approximate message passing for penalized linear regression
From MaRDI portal
Publication:4964647
DOI10.1088/1742-5468/aac910zbMath1459.62135arXiv1802.06939OpenAlexW2788188237WikidataQ129625799 ScholiaQ129625799MaRDI QIDQ4964647
Publication date: 2 March 2021
Published in: Journal of Statistical Mechanics: Theory and Experiment (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1802.06939
Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05) Learning and adaptive systems in artificial intelligence (68T05)
Related Items (3)
Unnamed Item ⋮ Perfect reconstruction of sparse signals with piecewise continuous nonconvex penalties and nonconvexity control ⋮ Prediction errors for penalized regressions based on generalized approximate message passing
Cites Work
- Unnamed Item
- Unnamed Item
- Nearly unbiased variable selection under minimax concave penalty
- Degrees of freedom in lasso problems
- The solution path of the generalized lasso
- Asymptotic optimality for \(C_ p\), \(C_ L\), cross-validation and generalized cross-validation: Discrete index set
- Estimation of the mean of a multivariate normal distribution
- Ordered linear smoothers
- On the ``degrees of freedom of the lasso
- Adapting to Unknown Smoothness via Wavelet Shrinkage
- Evaluation of generalized degrees of freedom for sparse estimation by replica method
- Decoding by Linear Programming
- Information, Physics, and Computation
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Adaptive Model Selection
- Approximate message passing for nonconvex sparse regularization with stability and asymptotic analysis
- Some Comments on C P
- A CDMA multiuser detection algorithm on the basis of belief propagation
- The Estimation of Prediction Error
- Compressed sensing
This page was built for publication: Estimator of prediction error based on approximate message passing for penalized linear regression
