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Nonparametric Estimation of ROC Curves in the Absence of a Gold Standard

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  • Xiao-Hua Zhou
  • Pete Castelluccio
  • Chuan Zhou

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  • Xiao-Hua Zhou & Pete Castelluccio & Chuan Zhou, 2005. "Nonparametric Estimation of ROC Curves in the Absence of a Gold Standard," Biometrics, The International Biometric Society, vol. 61(2), pages 600-609, June.
    • Handle: RePEc:bla:biomet:v:61:y:2005:i:2:p:600-609
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    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2005.00324.x
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    References listed on IDEAS

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    1. Biernacki, Christophe & Celeux, Gilles & Govaert, Gerard, 2003. "Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 561-575, January.
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    Cited by:

    1. Peizhou Liao & Hao Wu & Tianwei Yu, 2017. "ROC Curve Analysis in the Presence of Imperfect Reference Standards," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(1), pages 91-104, June.
    2. Hiroyuki Kasahara & Katsumi Shimotsu, 2007. "Nonparametric Identification And Estimation Of Multivariate Mixtures," Working Paper 1153, Economics Department, Queen's University.
    3. Cheng, Dunlei & Branscum, Adam J. & Stamey, James D., 2010. "A Bayesian approach to sample size determination for studies designed to evaluate continuous medical tests," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 298-307, February.
    4. Paul S. Albert, 2007. "Random Effects Modeling Approaches for Estimating ROC Curves from Repeated Ordinal Tests without a Gold Standard," Biometrics, The International Biometric Society, vol. 63(2), pages 593-602, June.
    5. Zhenqiu Liu & Ming Tan, 2008. "ROC-Based Utility Function Maximization for Feature Selection and Classification with Applications to High-Dimensional Protease Data," Biometrics, The International Biometric Society, vol. 64(4), pages 1155-1161, December.

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