The power inverse Lindley distribution: different methods of estimation




Outlier, Grouping Analysis, Monte Carlo Method


In the last years several probability distributions have been proposed in the literature, especially with the aim of obtaining models that are more flexible relative to the behaviors of the density and hazard rate functions. For instance, Ghitany et al. (2013) proposed a new generalization of the Lindley distribution, called power Lindley distribution, whereas Sharma et al. (2015a) proposed the inverse Lindley distribution. From these two generalizations Barco et al. (2017) studied the inverse power Lindley distribution, also called by Sharma et al. (2015b) as generalized inverse Lindley distribution. Considering the inverse power Lindley distribution, in this paper is evaluate the performance, through Monte Carlo simulations, with respect to the bias and consistency of nine different methods of estimations (the maximum likelihood method and eight others based on the distance between the empirical and theoretical cumulative distribution function). The numerical results showed a better performance of the estimation method based on the Anderson-Darling test statistic. This conclusion is also observed in the analysis of two real data sets.


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Author Biographies

André Felipe Berdusco Menezes, Universidade Estadual de Maringá, Maringá, PR

Graduação em Estatística pela Universidade Estadual de Maringá

Josmar Mazucheli, Universidade Estadual de Maringá, Maringá, PR

Professor Associado na Universidade Estadual de Maringá, Maringá PR

Kelly Vanessa Parede Barco, Faculdade União de Campo Mourão, Campo Mourão, PR

Mestrado em Bioestatística, Universidade Estadual de Maringá


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How to Cite

Menezes, A. F. B., Mazucheli, J., & Barco, K. V. P. (2018). The power inverse Lindley distribution: different methods of estimation. Ciência E Natura, 40, e24.