Likelihood Inference In Parallel Systems Regression Models With Censored Data

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 squared

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