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Bootstrap estimated true and false positive rates and ROC curve

Adler, W and Lausen, B (2009) 'Bootstrap estimated true and false positive rates and ROC curve.' Computational Statistics & Data Analysis, 53 (3). pp. 718-729. ISSN 0167-9473

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Diagnostic studies and new biomarkers are assessed by the estimated true and false positive rates of the classification rule. One diagnostic rule is considered for high-dimensional predictor data. Cross-validation and the leave-one-out bootstrap are discussed to estimate true and false positive rates of classifiers by the machine learning methods Adaboost, Bagging, Random Forest, (penalized) logistic regression and support vector machines. The .632+ bootstrap estimation of the misclassification error has been previously proposed to adjust the overfitting of the apparent error. This idea is generalized to the estimation of true and false positive rates. Tree-based simulation models with 8 and 50 binary non-informative variables are analysed to examine the properties of the estimators. Finally, a bootstrap estimation of receiver operating characteristic (ROC) curves is suggested and a .632+ bootstrap estimation of ROC curves is discussed. This approach is applied to high-dimensional gene expression data of leukemia and predictors of image data for glaucoma diagnosis.

Item Type: Article
Subjects: H Social Sciences > HA Statistics
Q Science > QA Mathematics
R Medicine > R Medicine (General)
Divisions: Faculty of Science and Health
Faculty of Science and Health > Mathematical Sciences, Department of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 09 Dec 2011 23:40
Last Modified: 06 Jan 2022 13:24

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