Penalized Regressions: The Bridge versus the Lasso
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Summary
It is shown that the bridge regression performs well compared to the lasso and ridge regression, and is demonstrated through an analysis of a prostate cancer data.
- Type
- article
- Published
- 1998-09-01
- Cited by
- 1,219
- References
- 14
- OpenAlex
- https://openalex.org/W2102760656
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:123095463
Keywords
Lasso (programming language), Elastic net regularization, Estimator, Ordinary least squares, Regression
References
- Ridge Regression: Applications to Nonorthogonal Problems
- Ridge Regression: Biased Estimation for Nonorthogonal Problems
- Solving least squares problems
- A Statistical View of Some Chemometrics Regression Tools
- Regression Shrinkage and Selection via the Lasso
- Smoothing noisy data with spline functions
- An Introduction to the Bootstrap
- Prostate specific antigen in the diagnosis and treatment of adenocarcinoma of the prostate. II. Radical prostatectomy treated patients.
- Re: Prostate specific antigen in the diagnosis and treatment of adenocarcinoma of the prostate.
- Smoothing noisy data with spline functions
- Linear regression analysis
- Regression Analysis: Theory, Methods, and Applications
- Regressions by Leaps and Bounds
- Practical optimization
- Regression Analysis: Theory, Methods, and Applications
- An Introduction to the Bootstrap
- Regressions by Leaps and Bounds
- Regressions by Leaps and Bounds
- Regression Analysis: Theory, Methods, and, Applications.
- Ridge Regression: Biased Estimation for Nonorthogonal Problems
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- Variable Selection by Bayesian Adaptive Lasso and Iterative Adaptive Lasso, with Application for Genome-wide Multiple Loci Mapping
- Algorithmes d'ensemble actif pour le LASSO
- Sparse coding for machine learning, image processing and computer vision
- Classifiers of massive and structured data problems: algorithms and applications
- A Comparison of Optimization Methods and Software for Large-scale L1-regularized Linear Classification
- Penalization Methods for Group Identification and Variable Selection in Models with Correlated Predictors
- Subgroup Identification and Variable Selection from Randomized Clinical Trial Data.
- Maximum de vraisemblance et moindre carrés pénalisés dans des modèles de durée de vie censurées
- System-scale network modeling of cancer using EPoC.
- Nonparametric covariance estimation in functional mapping of complex dynamic traits
- Penalized methods in genome-wide association studies
- Enhancements of sparse clustering with resampling and considerations on tuning parameter
- Sparse model learning for inferring genotype and phenotype associations
- Variable Selection Via Subtle Uprooting
- Generalized and smooth James-Stein model selection
- Orthogonalizing Penalized Regression
- Sparse Model Identification for High Dimensional Data.
- Regularized Learning of High-dimensional Sparse Graphical Models
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