Proper receiver operating characteristic analysis: The bigamma model
Proper receiver operating characteristic analysis: The bigamma model
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DOI：
10.1016/s1076-6332(97)80013-x
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发表时间：
1997-02-01
影响因子：
4.8
通讯作者：
AbuDagga, H
中科院分区：
文献类型：
--
作者：
Dorfman, DD;Berbaum, KS;AbuDagga, H
理由和目标。标准的副正态模型是最常用的模型，用于拟合受试者操作特征评级数据;然而，它有时会产生不适当的拟合，与退化数据集的机会线交叉。提出并评价了一种适合于处理副正态退化的常形双伽马模型。在一系列的Monte Carlo研究中，分别从标准的副正态总体模型和适当的常形双伽马模型中产生Monte Carlo样本。结果表明，标准副正态模型在无退化数据集的大样本下具有较好的稳健性，而在小样本下由于退化数据集的存在，标准副正态模型的稳健性较差。一个适当的恒定形状的双伽马模型似乎可以解决简并问题，没有不适当的机会线交叉。双伽马拟合模型在小样本下优于标准副正态拟合模型，在大样本下得到相似的结果。
Rationale and Objectives. The standard binormal model is the most commonly used model for fitting receiver operating characteristic rating data; however, it sometimes produces inappropriate fits that cross the chance line with degenerate data sets. The authors proposed and evaluated a proper constant-shape bigamma model to handle binormal degeneracy.Methods. Monte Carlo samples were generated from both a standard binormal population model and a proper constant-shape bigamma model in a series of Monte Carlo studies.Results. The results confirm that the standard binormal model is robust in large samples with no degenerate data sets and that the standard binormal model is not robust in small samples because of degenerate data sets.Conclusion. A proper constant-shape bigamma model seems to solve the problem of degeneracy without inappropriate chance line crossings. The bigamma fitting model outperformed the standard binormal fitting model in small samples and gave similar results in large samples.