Non-parametric estimation of ROC curve
نویسندگان
چکیده
Receiver operating characteristic (ROC) curve is widely applied in measuring discriminatory ability of diagnostic or prognostic tests. This makes ROC analysis one of the most active research areas in medical statistics. Many parametric and semiparametric estimation methods have been proposed for estimating the ROC curve and its functionals. In this paper, we propose a fully nonparametric Bayesian bootstrap (BB) estimation method for ROC curve and its functionals. The BB method gives a bandwidth free automatically smooth estimate. The area under the curve (AUC) is used to measure the accuracy of different diagnostic methods. The accuracy of the estimate of the ROC curve in the simulation studies is examined by the integrated absolute error (IAE). In comparison with other existing curve estimation methods, BB method performs well in terms of accuracy, robustness and simplicity. We also propose a procedure based on the BB approach to test the binormality assumption.
منابع مشابه
Estimation and Comparison of Receiver Operating Characteristic Curves.
The receiver operating characteristic (ROC) curve displays the capacity of a marker or diagnostic test to discriminate between two groups of subjects, cases versus controls. We present a comprehensive suite of Stata commands for performing ROC analysis. Non-parametric, semiparametric and parametric estimators are calculated. Comparisons between curves are based on the area or partial area under...
متن کاملEstimation of Area under Receiver Operating Characteristic Curve for Bi-Pareto and Bi-Two Parameter Exponential Models
In this paper, we find the ROC curves for Bi-Pareto and Bi-two parameter exponential distributions. Theoretical, parametric and non-parametric values of area under receiver operating characteristic (AUROC) curve for different parametric combinations have been calculated using simulations. These values are compared in terms of root mean square and mean absolute errors. The results are demonstrat...
متن کاملReceiver Operating Characteristic (ROC) Curve: comparing parametric estimation, Monte Carlo simulation and numerical integration
A receiver operating characteristic (ROC) curve is a plot of predictive model probabilities of true positives (sensitivity) as a function of probabilities of false positives (1 – specificity) for a set of possible cutoff points. Some of the SAS/STAT procedures do not have built-in options for ROC curves and there have been a few suggestions in previous SAS forums to address the issue by using e...
متن کاملA Linear Regression Framework for the Receiver Operating Characteristic (ROC) Curve Analysis.
The receiver operating characteristic (ROC) curve has been a popular statistical tool for characterizing the discriminating power of a classifier, such as a biomarker or an imaging modality for disease screening or diagnosis. It has been recognized that the accuracy of a given procedure may depend on some underlying factors, such as subject's demographic characteristics or disease risk factors,...
متن کاملBayesian bootstrap estimation of ROC curve.
Receiver operating characteristic (ROC) curve is widely applied in measuring discriminatory ability of diagnostic or prognostic tests. This makes the ROC analysis one of the most active research areas in medical statistics. Many parametric and semiparametric estimation methods have been proposed for estimating the ROC curve and its functionals. In this paper, we propose the Bayesian bootstrap (...
متن کامل