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Partial AUC Estimation and Regression

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  • Lori E. Dodd
  • Margaret S. Pepe

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  • Lori E. Dodd & Margaret S. Pepe, 2003. "Partial AUC Estimation and Regression," Biometrics, The International Biometric Society, vol. 59(3), pages 614-623, September.
    • Handle: RePEc:bla:biomet:v:59:y:2003:i:3:p:614-623
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    File URL: http://hdl.handle.net/10.1111/1541-0420.00071
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    References listed on IDEAS

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    1. Margaret Sullivan Pepe, 2000. "An Interpretation for the ROC Curve and Inference Using GLM Procedures," Biometrics, The International Biometric Society, vol. 56(2), pages 352-359, June.
    2. P. J. Heagerty & M. S. Pepe, 1999. "Semiparametric estimation of regression quantiles with application to standardizing weight for height and age in US children," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 48(4), pages 533-551.
    3. Stuart G. Baker, 2000. "Identifying Combinations of Cancer Markers for Further Study as Triggers of Early Intervention," Biometrics, The International Biometric Society, vol. 56(4), pages 1082-1087, December.
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    2. Gigliarano, Chiara & Figini, Silvia & Muliere, Pietro, 2014. "Making classifier performance comparisons when ROC curves intersect," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 300-312.
    3. Soutik Ghosal & Zhen Chen, 2022. "Discriminatory Capacity of Prenatal Ultrasound Measures for Large-for-Gestational-Age Birth: A Bayesian Approach to ROC Analysis Using Placement Values," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(1), pages 1-22, April.
    4. Yousef, Waleed A., 2013. "Assessing classifiers in terms of the partial area under the ROC curve," Computational Statistics & Data Analysis, Elsevier, vol. 64(C), pages 51-70.
    5. Gunther Glehr & Paloma Riquelme & Katharina Kronenberg & Robert Lohmayer & Víctor J. López-Madrona & Michael Kapinsky & Hans J. Schlitt & Edward K. Geissler & Rainer Spang & Sebastian Haferkamp & Jame, 2024. "Restricting datasets to classifiable samples augments discovery of immune disease biomarkers," Nature Communications, Nature, vol. 15(1), pages 1-21, December.
    6. Sergio Picart-Armada & Steven J Barrett & David R Willé & Alexandre Perera-Lluna & Alex Gutteridge & Benoit H Dessailly, 2019. "Benchmarking network propagation methods for disease gene identification," PLOS Computational Biology, Public Library of Science, vol. 15(9), pages 1-24, September.
    7. Yu, Wenbao & Park, Taesung, 2015. "Two simple algorithms on linear combination of multiple biomarkers to maximize partial area under the ROC curve," Computational Statistics & Data Analysis, Elsevier, vol. 88(C), pages 15-27.
    8. Holly Janes & Gary Longton & Margaret S. Pepe, 2009. "Accommodating covariates in receiver operating characteristic analysis," Stata Journal, StataCorp LP, vol. 9(1), pages 17-39, March.
    9. Jialiang Li & Jason P. Fine, 2010. "Weighted area under the receiver operating characteristic curve and its application to gene selection," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(4), pages 673-692, August.
    10. Siyan Liu & Qinglong Tian & Yukun Liu & Pengfei Li, 2024. "Joint Statistical Inference for the Area under the ROC Curve and Youden Index under a Density Ratio Model," Mathematics, MDPI, vol. 12(13), pages 1-21, July.
    11. Tianxi Cai & Yingye Zheng, 2007. "Model Checking for ROC Regression Analysis," Biometrics, The International Biometric Society, vol. 63(1), pages 152-163, March.
    12. Mei-Cheng Wang & Shanshan Li, 2012. "Bivariate Marker Measurements and ROC Analysis," Biometrics, The International Biometric Society, vol. 68(4), pages 1207-1218, December.
    13. Margaret Sullivan Pepe & Tianxi Cai, 2004. "The Analysis of Placement Values for Evaluating Discriminatory Measures," Biometrics, The International Biometric Society, vol. 60(2), pages 528-535, June.
    14. Peterson, A. Townsend & Papeş, Monica & Soberón, Jorge, 2008. "Rethinking receiver operating characteristic analysis applications in ecological niche modeling," Ecological Modelling, Elsevier, vol. 213(1), pages 63-72.
    15. Schmid Matthias & Hothorn Torsten & Krause Friedemann & Rabe Christina, 2012. "A PAUC-based Estimation Technique for Disease Classification and Biomarker Selection," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(5), pages 1-26, October.
    16. Xiao Song & Xiao-Hua Zhou, 2004. "A Marginal Model Approach for Analysis of Multi-reader Multi-test Receiver Operating Characteristic (ROC) Data," UW Biostatistics Working Paper Series 1067, Berkeley Electronic Press.
    17. Margaret S. Pepe & Gary Longton & Holly Janes, 2009. "Estimation and comparison of receiver operating characteristic curves," Stata Journal, StataCorp LP, vol. 9(1), pages 1-16, March.
    18. Merve Basol & Dincer Goksuluk & Ergun Karaagaoglu, 2023. "Comparing the diagnostic performance of methods used in a full-factorial design multi-reader multi-case studies," Computational Statistics, Springer, vol. 38(3), pages 1537-1553, September.
    19. Pardo-Fernandez, Juan Carlos & Rodriguez-alvarez, Maria Xose & Van Keilegom, Ingrid, 2013. "A review on ROC curves in the presence of covariates," LIDAM Discussion Papers ISBA 2013050, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    20. Ángel Beade & Manuel Rodríguez & José Santos, 2024. "Business failure prediction models with high and stable predictive power over time using genetic programming," Operational Research, Springer, vol. 24(3), pages 1-41, September.
    21. Man-Jen Hsu & Huey-Miin Hsueh, 2013. "The linear combinations of biomarkers which maximize the partial area under the ROC curves," Computational Statistics, Springer, vol. 28(2), pages 647-666, April.
    22. Eunhee Kim & Zheng Zhang & Youdan Wang & Donglin Zeng, 2014. "Power calculation for comparing diagnostic accuracies in a multi-reader, multi-test design," Biometrics, The International Biometric Society, vol. 70(4), pages 1033-1041, December.

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