Abstract
In this study, different frequentist estimation procedures for the parameters of the exponential-Poisson distribution are considered, such as the maximum likelihood, method of moments, ordinary and weighted least-squares, percentile, maximum product of spacings, Cramér-von Mises and Anderson-Darling maximum goodness-of-fit estimators. We compare them using extensive numerical simulations, which show that using a nested expectation-maximization algorithm in the maximum likelihood estimators with bootstrap bias correction does not require numerical procedures to solve nonlinear equations and returns accurate parameter estimates. Finally, our proposed methodology is fully illustrated using two real data sets (rainfall and aircraft data) with the occurrence of zero values.
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