  The Birnbaum-Saunders regression model is commonly used in reliability
studies. We address the issue of performing inference in this class of models
when the number of observations is small. We show that the likelihood ratio
test tends to be liberal when the sample size is small, and we obtain a
correction factor which reduces the size distortion of the test. The correction
makes the error rate of he test vanish faster as the sample size increases. The
numerical results show that the modified test is more reliable in finite
samples than the usual likelihood ratio test. We also present an empirical
application.
