Optimal classification scores based on multivariate marker transformations
DOI10.1007/s10182-020-00388-zzbMath1478.62335OpenAlexW3118556935MaRDI QIDQ2068896
Sonia Pérez-Fernández, Susana Díaz-Coto, Pablo Martínez-Camblor
Publication date: 20 January 2022
Published in: AStA. Advances in Statistical Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10182-020-00388-z
kernel density estimatorclassification problemoptimal transformationmultivariate markerreceiver-operating characteristic (ROC) curve
Density estimation (62G07) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Learning and adaptive systems in artificial intelligence (68T05)
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- Central limit theorem for integrated square error of multivariate nonparametric density estimators
- A decision-theoretic generalization of on-line learning and an application to boosting
- Support-vector networks
- Nonparametric and semiparametric estimation of the receiver operating characteristic curve
- Visualizing the decision rules behind the ROC curves: understanding the classification process
- Studying the bandwidth in \(k\)-sample smooth tests
- General nonparametric ROC curve comparison
- Optimal Combinations of Diagnostic Tests Based on AUC
- Powerful nonparametric statistics to compare k independent ROC curves
- Combining Several Screening Tests: Optimality of the Risk Score
- Remarks on Some Nonparametric Estimates of a Density Function
- A Neyman–Pearson Approach to Statistical Learning
- Linear Combinations of Multiple Diagnostic Markers
- Overestimation of the receiver operating characteristic curve for logistic regression
- Combining diagnostic test results to increase accuracy
- The strong uniform convergence of multivariate variable kernel estimates
- Improving the biomarker diagnostic capacity via functional transformations
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