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On the grouped selection and model complexity of the adaptive elastic net
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Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/s11222-008-9088-5openalex-snapshot
Penalized regression with correlation-based penalty
inferredcites →https://doi.org/10.1016/s0022-5347(17)39781-1openalex-snapshot
Re: Prostate Specific Antigen in the Diagnosis and Treatment of Adenocarcinoma of the Prostate. I. Untreated Patients, by T. A. Stamey and J. N. Kabalin, J. Urol., 141: 1070–1075, 1989. II. Radical Prostatectomy Treated Patients, by T. A. Stamey, J. N. Kabalin, J. E. McNeal, I. M. Johnstone, F.…
inferredcites →https://doi.org/10.1016/s0022-5347(17)41175-xopenalex-snapshot
Prostate Specific Antigen in the Diagnosis and Treatment of Adenocarcinoma of the Prostate. II. Radical Prostatectomy Treated Patients
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Ridge Regression: Applications to Nonorthogonal Problems
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Ideal Spatial Adaptation by Wavelet Shrinkage
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Adaptive Lasso for Cox's proportional hazards model
inferredcites →https://doi.org/10.1111/j.1467-9868.2005.00490.xopenalex-snapshot
Sparsity and Smoothness Via the Fused Lasso
inferredcites →https://doi.org/10.1111/j.1467-9868.2005.00503.xopenalex-snapshot
Regularization and Variable Selection Via the Elastic Net
inferredcites →https://doi.org/10.1111/j.1467-9868.2007.00577.xopenalex-snapshot
Regression coefficient and autoregressive order shrinkage and selection via the lasso
inferredcites →https://doi.org/10.1111/j.1541-0420.2007.00843.xopenalex-snapshot
Simultaneous Regression Shrinkage, Variable Selection, and Supervised Clustering of Predictors with OSCAR
inferredcites →https://doi.org/10.1111/j.2517-6161.1996.tb02080.xopenalex-snapshot
Regression Shrinkage and Selection Via the Lasso
inferredcites →https://doi.org/10.1198/016214501753382273openalex-snapshot
Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
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The Estimation of Prediction Error
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The Adaptive Lasso and Its Oracle Properties
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On the “degrees of freedom” of the lasso
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On the adaptive elastic-net with a diverging number of parameters
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Asymptotics for lasso-type estimators
inferredcites →https://doi.org/10.21236/ada485557openalex-snapshot
On Model Selection Consistency of the Elastic Net When p >> n
inferredcites →https://doi.org/10.2307/1267205openalex-snapshot
Generalized Inverses, Ridge Regression, Biased Linear Estimation, and Nonlinear Estimation
inferredcites →https://doi.org/10.2307/1267351openalex-snapshot
Ridge Regression: Biased Estimation for Nonorthogonal Problems
inferredcites →https://doi.org/10.2307/1271436openalex-snapshot
Ridge Regression: Biased Estimation for Nonorthogonal Problems
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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DeepSCP: utilizing deep learning to boost single-cell proteome coverage
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Reader reaction to “Outcome‐adaptive lasso: Variable selection for causal inference” by Shortreed and Ertefaie (2017)
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International Stock Return Predictability: What Is the Role of the United States?
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Sparse hashing for fast multimedia search
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Priority-Elastic net for binary disease outcome prediction based on multi-omics data
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Models and algorithms to solve robust causal inference problems from large-scale observational data
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High Dimensional Logistic Regression Model using Adjusted Elastic Net Penalty
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Group Variable Selection via a Combination of Lq Norm and Correlation-Based Penalty
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Régularisation et sélection de variables par le biais de la vraisemblance pénalisée
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