  Consider a model parameterized by a scalar parameter of interest and a
nuisance parameter vector. Inference about the parameter of interest may be
based on the signed root of the likelihood ratio statistic R. The standard
normal approximation to the conditional distribution of R typically has error
of order O(n^{-1/2}), where n is the sample size. There are several
modifications for R, which reduce the order of error in the approximations. In
this paper, we mainly investigate Barndorff-Nielsen's modified directed
likelihood ratio statistic, Severini's empirical adjustment, and DiCiccio and
Martin's two modifications, involving the Bayesian approach and the conditional
likelihood ratio statistic. For each modification, two formats were employed to
approximate the conditional cumulative distribution function; these are
Barndorff-Nielson formats and the Lugannani and Rice formats. All
approximations were applied to inference on the ratio of means for two
independent exponential random variables. We constructed one and two-sided
hypotheses tests and used the actual sizes of the tests as the measurements of
accuracy to compare those approximations.
