Estimation Methods for the Gompertz–Makeham Distribution Under Progressively Type-I Interval Censoring Scheme

Abstract

Gompertz–Makeham distribution has been widely used in describing human mortality, establishing actuarial tables and growth models. In some real applications researchers are faced with incomplete data. Employing the efficient estimation method in this situations is very important. In this paper, we propose an EM algorithm-based estimator (EME), moment-based estimator (MME) and mid-point estimator (MPE) for the Gompertz-Makeham distribution under the well-known progressively type-I interval censoring scheme. Then, some comparisons are made of the following estimators: the maximum likelihood estimator (MLE), EME, MME and MPE to choose the best one.