This article demonstrates how recent developments in the theory of empirical processes allow us to construct a new family of asymptotically distribution-free smooth tests. Their distribution-free property is preserved even when the parameters are estimated, model selection is performed, and the sample size is only moderately large. A computationally efficient alternative to the classical parametric bootstrap is also discussed.

翻译：本文展示了经验过程理论的最新进展如何使我们能够构建一类新的渐近分布自由光滑检验。即使在参数被估计、模型选择被执行且样本量仅适度大的情况下，其分布自由性质仍得以保持。文中还讨论了经典参数自助法的一种计算高效替代方案。