The work in this thesis is concerned with the investigation of the finite
sample performance of asymptotic inference procedures based on the likelihood
function when applied to the regression model based on parallel systems with
censored data. The study includes investigating the adequacy of these inferential
procedures as well as investigating the relative performances of asymptotically
equivalent likelihood-based statistics in small samples.
The maximum likelihood estimator of the parameters of this model is not
available in closed form. Thus, its actual sampling distribution is intractable. A
simulation study is conducted to investigate the bias, the finite sample variance, the
asymptotic variance obtained from the inverse of the observed Fisher information
matrix, the adequacy of this approximate asymptotic variance, and the mean square
