论文标题
重新审视P值的有效P值和期望
Valid p-Values and Expectations of p-Values Revisited
论文作者
论文摘要
在理论和应用文献中都观察到了有关基于P值的程序的有利或批判性出版物的风暴。当复合空模型有效时，我们专注于场景中p值的有效定义。有效的P值（VPV）统计量可用于进行前缀级别决定。在这种情况下，考虑了Kolmogorov Smirnov拟合优度测试和正常的两个样本问题。特别是，我们研究了基于单个观察结果的拟合效果的问题。本文说明了新测试程序的构建，提倡实施基于VPV机制的实际理由。 VPV框架引起了传统预期p值（EPV）工具的扩展，以测量测试的性能。将EPV概念与接收器操作特征（ROC）曲线方法（一种建立的生物统计学方法）相关联，我们提出了基于YouDen指数的最佳原理，以得出决策过程的关键价值。在这些术语中，在许多情况下，可以提出显着性水平alpha = 0.05。鉴于ROC曲线分析，我们介绍了部分EPV，以表征包括其无偏见在内的测试的特性。我们还提供了贝叶斯因子（BF）测试统计量与测试统计的BF之间的内在关系。 关键字：AUC；贝叶斯因子；预期的p值； Kolmogorov Smirnov测试；似然比；滋扰参数； p值； ROC曲线；汇总数据；单个观察； I类错误率； Youden索引
A storm of favorable or critical publications regarding p-values-based procedures has been observed in both the theoretical and applied literature. We focus on valid definitions of p-values in the scenarios when composite null models are in effect. A valid p-value (VpV) statistic can be used to make a prefixed level-decision. In this context, Kolmogorov Smirnov goodness-of-fit tests and the normal two sample problem are considered. In particular, we examine an issue regarding the goodness-of-fit testability based on a single observation. This article exemplifies constructions of new test procedures, advocating practical reasons to implement VpV-based mechanisms. The VpV framework induces an extension of the conventional expected p-value (EPV) tool for measuring the performance of a test. Associating the EPV concept with the receiver operating characteristic (ROC) curve methodology, a well-established biostatistical approach, we propose a Youden index based optimality principle to derive critical values of decision making procedures. In these terms, the significance level alpha=0.05 can be suggested, in many situations. In light of an ROC curve analysis, we introduce partial EPVs to characterize properties of tests including their unbiasedness. We also provide the intrinsic relationship between the Bayes Factor (BF) test statistic and the BF of test statistics. Keywords: AUC; Bayes Factor; Expected p-value; Kolmogorov Smirnov tests; Likelihood ratio; Nuisance parameters; P-value; ROC curve; Pooled data; Single observation; Type I error rate; Youden index
