Nonparametric Bayesian estimation of the three-way receiver operating characteristic surface.
Nonparametric Bayesian estimation of the three-way receiver operating characteristic surface.
复制标题
三路接收机工作特征面的非参数贝叶斯估计。
DOI：
10.1002/bimj.201100070
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
Alonzo,ToddA
中科院分区：
文献类型：
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作者：
Inacio,Vanda;Turkman,AntoniaA;Nakas,ChristosT;Alonzo,ToddA
我们描述了一种基于有限 Polya 树 (MFPT) 先验混合来估计三向 ROC 表面的非参数贝叶斯方法。有限 Polya 树的混合是稳健的模型，可以处理数据中的非标准特征。我们解决了对具有偏斜、多模态或其他非标准特征的连续诊断数据进行建模的困难，以及参数方法如何在这种情况下导致误导性结果。获得 ROC 表面和 ROC 表面下体积的稳健、数据驱动的推断。进行模拟研究以评估所提出方法的性能。该方法适用于人类免疫缺陷病毒患者的磁共振波谱研究数据。
We describe a nonparametric Bayesian approach for estimating the three‐way ROC surface based on mixtures of finite Polya trees (MFPT) priors. Mixtures of finite Polya trees are robust models that can handle nonstandard features in the data. We address the difficulties in modeling continuous diagnostic data with skewness, multimodality, or other nonstandard features, and how parametric approaches can lead to misleading results in such cases. Robust, data‐driven inference for the ROC surface and for the volume under the ROC surface is obtained. A simulation study is performed to assess the performance of the proposed method. Methods are applied to data from a magnetic resonance spectroscopy study on human immunodeficiency virus patients.