A Receiver Operating Characteristic(ROC)analysis of a power is important and useful in clinical trials.A Classical Conditional Power(CCP)is a probability of a classical rejection region given values of true treatment ...A Receiver Operating Characteristic(ROC)analysis of a power is important and useful in clinical trials.A Classical Conditional Power(CCP)is a probability of a classical rejection region given values of true treatment effect and interim result.For hypotheses and reversed hypotheses under normal models,we obtain analytical expressions of the ROC curves of the CCP,find optimal ROC curves of the CCP,investigate the superiority of the ROC curves of the CCP,calculate critical values of the False Positive Rate(FPR),True Positive Rate(TPR),and cutoff of the optimal CCP,and give go/no go decisions at the interim of the optimal CCP.In addition,extensive numerical experiments are carried out to exemplify our theoretical results.Finally,a real data example is performed to illustrate the go/no go decisions of the optimal CCP.展开更多
基金supported by the National Social Science Fund of China(Grand No.21XTJ001).
文摘A Receiver Operating Characteristic(ROC)analysis of a power is important and useful in clinical trials.A Classical Conditional Power(CCP)is a probability of a classical rejection region given values of true treatment effect and interim result.For hypotheses and reversed hypotheses under normal models,we obtain analytical expressions of the ROC curves of the CCP,find optimal ROC curves of the CCP,investigate the superiority of the ROC curves of the CCP,calculate critical values of the False Positive Rate(FPR),True Positive Rate(TPR),and cutoff of the optimal CCP,and give go/no go decisions at the interim of the optimal CCP.In addition,extensive numerical experiments are carried out to exemplify our theoretical results.Finally,a real data example is performed to illustrate the go/no go decisions of the optimal CCP.
文摘针对气体绝缘金属封闭式组合电器(gas insulated switchgear,GIS)设备中痕量气体紫外分析光谱信号易出现吸收峰重叠的问题,提出了一种结合格拉姆角场(Gram's angle field,GAF)和VGG16改进模型的多组分痕量气体的定量检测方法。首先利用GAF将一维紫外光谱信号转换为时序图像,将光谱信号映射为具有丰富特征信息的图像形式,从而提升原始光谱信号的特征表达能力。其次将GAF特征图输入到VGG16改进模型中,实现痕量气体浓度的特征识别。最后通过不同浓度下采集到的CS2、SO2和H2S的单组分气体和混合气体,与卷积神经网络(convolutional neural network,CNN)、VGG16和SDP_VGG16等模型进行对比实验,并结合受试者工作特征曲线下面积(area under the curve,AUC)进行验证。结果表明,该方法可以有效地检测出SF6分解所产生的CS2、SO2和H2S痕量气体,是一种行之有效的特征提取方法。
