Bayesian predictive modeling for Inverse Gamma-Pareto composite distribution

M. S. Aminzadeh and M. Deng

Communications in Statistics - Theory and Methods, 2019, vol. 48, issue 8, 1938-1954

Abstract: Inverse Gamma-Pareto composite distribution is considered as a model for heavy tailed data. The maximum likelihood (ML), smoothed empirical percentile (SM), and Bayes estimators (informative and non-informative) for the parameter θ, which is the boundary point for the supports of the two distributions are derived. A Bayesian predictive density is derived via a gamma prior for θ and the density is used to estimate risk measures. Accuracy of estimators of θ and the risk measures are assessed via simulation studies. It is shown that the informative Bayes estimator is consistently more accurate than ML, Smoothed, and the non-informative Bayes estimators.

Date: 2019
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Citations: View citations in EconPapers (6)

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DOI: 10.1080/03610926.2018.1440595

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