  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.
