EM Algorithm for Estimating the Parameters of Quasi-Lindley Model with Application

The quasi-Lindley distribution is a flexible model useful in reliability analysis, management science, and engineering analysis. In this paper, an expectation-maximization (EM) algorithm was applied to estimate the parameters of this model for uncensored and right-censored data. Simulation studies show that the estimates of EM perform better than maximum likelihood estimates (MLEs) for both uncensored and censored data. In an illustrative example, the waiting times of a bank's customers are analyzed and the estimator of the EM algorithm is compared with the MLE. The analysis of the data can be useful for the management of the bank.

Automated summary

The quasi-Lindley distribution is a flexible model that is useful in various fields. This paper utilizes the expectation-maximization (EM) algorithm to estimate the parameters for both uncensored and right-censored data. Simulation studies demonstrate that the EM estimates perform better than maximum likelihood estimates (MLEs) in both scenarios. An illustrative example regarding the waiting times of a bank's customers is provided to compare the EM estimator with the MLE. The analysis of such data can be crucial in bank management.