Computationally efficient confidence intervals for cross-validated area under the ROC curve estimates

DOI10.1214/15-EJS1035zbMath1327.62298OpenAlexW2151699048WikidataQ40636719 ScholiaQ40636719MaRDI QIDQ140311

Maya Petersen, Mark J. Van Der Laan, Erin Ledell, Maya Petersen, Erin LeDell

Publication date: 1 January 2015

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.ejs/1437742107

zbMATH Keywords

AUCconfidence intervalsmodel selectioninfluence functioncross-validationmachine learningvariance estimationinfluence curveROCbinary classification

Mathematics Subject Classification ID

Asymptotic properties of nonparametric inference (62G20) Nonparametric estimation (62G05) Nonparametric tolerance and confidence regions (62G15)

Related Items (5)

A General Framework for Inference on Algorithm-Agnostic Variable Importance ⋮ Estimation of predictive performance in high-dimensional data settings using learning curves ⋮ nlpred ⋮ Asymptotic linear expansion of regularized M-estimators ⋮ Improved Small-Sample Estimation of Nonlinear Cross-Validated Prediction Metrics

Uses Software

• R
• MASS (R)
• ROCR
• glmnet
• Julia
• MLbase
• bootstrap
• GitHub
• cvAUC
• Distributions.jl
• GLMNet.jl

Cites Work

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• Bootstrap methods: another look at the jackknife
• Subsampling
• Weak convergence and empirical processes. With applications to statistics
• Julia: A Fresh Approach to Numerical Computing
• Statistical Analysis of Financial Data in S-Plus
• The Predictive Sample Reuse Method with Applications
• The Relationship between Variable Selection and Data Agumentation and a Method for Prediction
• Linear Model Selection by Cross-Validation

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