Likelihood Based Estimation in the Logistic Model with Time Censored Data

Abstract

Inference procedures based on the likelihood function are considered for the one logistic distribution with time censored data. The finite sample performances of the maximum likelihood estimator as well as the large sample likelihood inferential procedures based on the Wald, the Rao, and the likelihood ratio statistics are investigated. It is found that the obtained from the asymptotic normal distribution of the maximum likelihood estimator are found no accurate. It is found also that interval estimation based on the Wald and Rao statistics need much more sample size than interval estimation based on the likelihood ratio statistics to attain reasonable accuracy.