In recent decades, survival analysis techniques have been extended far beyond the medical, biomedical, and reliability research areas to fields such as engineering, criminology, sociology, marketing, insurance, economics, etc. The study of survival data has previously focused on predicting the probability of response, survival, or mean lifetime, and comparing the survival distributions. More recently, the identification of risk and/or prognostic factors related to response, survival, and the development of a certain condition has become equally important (Lee, 1992). Conventional statistical methods are not adequate to analyze survival data because some observations are censored, i.e., for some observations there is incomplete information about the time to the event of interest. A common type of censoring in practice is Type I censoring, where the event of interest is observed only if it occurs prior to some pre-