Bootstrap-based goodness-of-fit test for parametric families of conditional distributions
提出一种用于分布回归的拟合优度检验，通过比较非参数与半参数估计的差异构造统计量，利用参数自助法确定临界值，在特定场景下比现有检验功效更高，且无需超参数，易于通过R包应用。
A consistent goodness-of-fit test for distributional regression is introduced. The test statistic is based on a process that traces the difference between a nonparametric and a semi-parametric estimate of the marginal distribution function of Y . As its asymptotic null distribution is not distribution-free, a parametric bootstrap method is used to determine critical values. Empirical results suggest that, in certain scenarios, the test outperforms existing specification tests by achieving a higher power and thereby offering greater sensitivity to deviations from the assumed parametric distribution family. Notably, the proposed test does not involve any hyperparameters and can easily be applied to individual datasets using the gofreg-package in R.