Fast Cross-validation for Multi-penalty High-dimensional Ridge Regression
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Publication:5066470
DOI10.1080/10618600.2021.1904962OpenAlexW3137113880MaRDI QIDQ5066470
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Publication date: 29 March 2022
Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/10618600.2021.1904962
marginal likelihoodmulti-view learningcancer genomicshigh-dimensional predictioniterative weighted least squares
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Cites Work
- Sparse classification with paired covariates
- Improving the INLA approach for approximate Bayesian inference for latent Gaussian models
- An even faster algorithm for ridge regression of reduced rank data
- A survey of cross-validation procedures for model selection
- A faster algorithm for ridge regression of reduced rank data
- Logistic regression diagnostics in ridge regression
- High-dimensional asymptotics of prediction: ridge regression and classification
- IPF-LASSO: integrative \(L_1\)-penalized regression with penalty factors for prediction based on multi-omics data
- Explaining Variational Approximations
- Scalable Bayesian Regression in High Dimensions With Multiple Data Sources
- A generalized ridge regression estimator and its finite sample properties
- Fast Stable Restricted Maximum Likelihood and Marginal Likelihood Estimation of Semiparametric Generalized Linear Models
- Consistent High-Dimensional Bayesian Variable Selection via Penalized Credible Regions
- Efficient approximate k‐fold and leave‐one‐out cross‐validation for ridge regression
- On the marginal likelihood and cross-validation
- Fused lasso algorithm for Cox′ proportional hazards and binomial logit models with application to copy number profiles
- Ridge Regression: Biased Estimation for Nonorthogonal Problems
- Efficient quadratic regularization for expression arrays
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