The weighted Lindley-G family of probabilistic models: properties, inference, and applications to real-life data

Abstract

We propose a new wider family called the weighted Lindley-G family. We derive some mathematical properties and special sub-models of the new family. We address the estimation of the model parameters by eight approaches of estimation. The estimation approaches are ranked and compared by using detailed simulations to develop a guideline for choosing the best approach for estimating the distribution parameters. The potentiality of the new family is illustrated via two applications to real-life data. It is shown that the proposed WLi-G family is more flexible as compared to some of the most cited families in the distribution theory literature such as the exponentiated-G, beta-G, transmuted-G, and alpha-power-G families under the same baseline model.