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A comparison of penalised regression methods for informing the selection of predictive markers
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Scaling regression inputs by dividing by two standard deviations
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Variable selection for multiply‐imputed data with application to dioxin exposure study
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Review and evaluation of penalised regression methods for risk prediction in low‐dimensional data with few events
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The Elements of Statistical Learning
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Predicting serious delinquency and substance use among aggressive boys.
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The development of markers for the Big-Five factor structure.
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The Australian Temperament Project: The First 30 Years
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The Australian Temperament Project
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Development of a sensation-seeking scale.
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Variable selection models based on multiple imputation with an application for predicting median effective dose and maximum effect
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Regularization and Variable Selection Via the Elastic Net
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Regression Shrinkage and Selection Via the Lasso
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Choosing Prediction Over Explanation in Psychology: Lessons From Machine Learning
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The Adaptive Lasso and Its Oracle Properties
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The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets
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Oral, Nasal and Pharyngeal Exposure to Lipopolysaccharide Causes a Fetal Inflammatory Response in Sheep
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Dissociable psychosocial profiles of adolescent substance users
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Regularization Paths for Generalized Linear Models via Coordinate Descent
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mice: Multivariate Imputation by Chained Equations inR
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MULTIVARIATE ASSESSMENT OF CONFLICT IN DISTRESSED AND NONDISTRESSED MOTHER‐ADOLESCENT DYADS
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Ridge Estimators in Logistic Regression
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Utility of Machine-Learning Approaches to Identify Behavioral Markers for Substance Use Disorders: Impulsivity Dimensions as Predictors of Current Cocaine Dependence
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A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection
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Additional file 1 of A novel risk score system for prognostic evaluation in adenocarcinoma of the oesophagogastric junction: a large population study from the SEER database and our center
inferred← citeshttps://doi.org/10.6084/m9.figshare.14978338openalex-snapshot
Additional file 2 of A novel risk score system for prognostic evaluation in adenocarcinoma of the oesophagogastric junction: a large population study from the SEER database and our center
inferred← citeshttps://doi.org/10.6084/m9.figshare.14978341openalex-snapshot
Additional file 3 of A novel risk score system for prognostic evaluation in adenocarcinoma of the oesophagogastric junction: a large population study from the SEER database and our center
inferred← citeshttps://doi.org/10.6084/m9.figshare.14978344openalex-snapshot
Additional file 4 of A novel risk score system for prognostic evaluation in adenocarcinoma of the oesophagogastric junction: a large population study from the SEER database and our center
inferred← citeshttps://doi.org/10.6084/m9.figshare.14978347openalex-snapshot
Additional file 5 of A novel risk score system for prognostic evaluation in adenocarcinoma of the oesophagogastric junction: a large population study from the SEER database and our center
inferred← citeshttps://doi.org/10.6084/m9.figshare.14978350openalex-snapshot
Additional file 6 of A novel risk score system for prognostic evaluation in adenocarcinoma of the oesophagogastric junction: a large population study from the SEER database and our center
inferred← citeshttps://doi.org/10.6084/m9.figshare.14978353openalex-snapshot
Additional file 7 of A novel risk score system for prognostic evaluation in adenocarcinoma of the oesophagogastric junction: a large population study from the SEER database and our center
inferred← citeshttps://doi.org/10.7717/peerj-cs.2579openalex-snapshot
Fast binary logistic regression
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