Confidence Bands for ROC Curves With Serially Dependent Data
针对序列相关数据，提出一种稳健的ROC曲线及其AUC的渐近置信带方法，模拟显示该方法在小样本下表现良好且对非正态性稳健，而传统方法在序列相关强时覆盖概率偏低。
We propose serial correlation-robust asymptotic confidence bands for the receiver operating characteristic (ROC) curve and its functional, viz., the area under ROC curve (AUC), estimated by quasi-maximum likelihood in the binormal model. Our simulation experiments confirm that this new method performs fairly well in finite samples, and confers an additional measure of robustness to nonnormality. The conventional procedure is found to be markedly undersized in terms of yielding empirical coverage probabilities lower than the nominal level, especially when the serial correlation is strong. An example from macroeconomic forecasting demonstrates the importance of accounting for serial correlation when the probability forecasts for real GDP declines are evaluated using ROC. Supplementary materials for this article are available online.