This paper studies computational aspects of an asymptotically distribution-free goodness-of-fit test for non-Gaussian distributions based on the Khmaladze martingale transformation when the location and scale parameters of the distribution are unknown. On top of that, we propose another goodness-of-fit test better than existing one in terms of a statistical power. Simulation studies demonstrate that the proposed test compares favorably with the existing test.

翻译：本文研究了当分布的位置参数与尺度参数未知时，基于Khmaladze鞅变换的非高斯分布渐近分布自由拟合优度检验的计算问题。在此基础上，我们提出了另一种在统计功效上优于现有方法的拟合优度检验。仿真研究表明，所提出的检验方法相比现有检验具有更优的表现。