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The sampling distributions of Gaussian ROC statistics

机译:高斯ROC统计量的抽样分布

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摘要

For a discrimination experiment, a plot of the hit rate against the false-alarm rate—the ROC curve— summarizes performance across a range of confidence levels. In many content areas, ROCs are well described by a normal-distribution model and the z– transformed hit and false-alarm rates are approximately linearly related. We examined the sampling distributions of three parameters of this model when applied to a ratings procedure: the area under the ROC (A_z), the normalized difference between the means of the underlying signal and noise distributions (d_a), and the slope of the ROC on z–coordinates (s). Statistical bias (the degree to which the mean of the sampling distribution differed from the true value) was trivial for A_z, small but noticeable for d_a, and substantial for s. Variability of the sampling distributions decreased with the number of trials and was also affected by the number of response categories available to the participant and by the overall sensitivity level. Figures in the article and tables available on line can be used to construct confidence intervals around ROC statistics and to test statistical hypotheses.
机译:对于区分实验,命中率与错误警报率之间的关系图-ROC曲线-汇总了一系列置信度水平下的性能。在许多内容区域中,ROC可以用正态分布模型很好地描述,并且z转换的命中率和误报警率近似线性相关。当应用到评级程序时,我们检查了该模型的三个参数的采样分布:ROC下的面积(A_z),基础信号和噪声分布的均值之间的归一化差异(d_a)以及ROC的斜率在z坐标上。对于A_z来说,统计偏差(抽样分布的平均值与真实值的差异程度)很小,对于d_a来说很小,但是很明显,对于s来说很大。样本分布的变异性随着试验次数的减少而降低,并且还受参与者可用的响应类别数和总体敏感性水平的影响。在线提供的文章和表格中的数字可用于构建ROC统计信息的置信区间并测试统计假设。

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