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Abstract
Recently, the application of compound distributions has increased due to the flexibility in fitting to actual data in various fields such as economics, insurance, etc. Poisson-half-logistic distribution is one of these distributions with an increasing-constant hazard rate that can be used in parallel systems and complementary risk models. Because of the complexity of the form of this distribution, it is not possible to obtain classical parameter estimates (such as MLE) by the analytical method for the location and scale parameters. We present a simple way of deriving explicit estimators by approximating the likelihood equations appropriately. This paper presents AMLE (Approximate MLE) method to obtain the location and scale parameters estimation. Using simulation, we show that this method is as efficient as the maximum likelihood estimators (MLEs), we obtain the variance of estimators from the inverse of the observed Fisher information matrix, and we see that when sample size increases bias and variance of these estimators, MSEs of parameters decrease. Finally, we present a numerical example to illustrate the methods of inference developed here.
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