Bayesian predictive modeling for Inverse Gamma-Pareto composite distribution. Issue 8 (18th April 2019)

• Record Type: Journal Article
• Authors: Aminzadeh, M. S.; Deng, M.
• Is Part Of: Communications in statistics. Volume 48:Issue 8(2019)
• Page Start: 1938
• Page End: 1954
• Publication Date: 2019-04-18
• Subjects: Bayesian-inference -- IG-Pareto composite density -- MLE -- Predictive density -- Risk measures; 62F15 -- 62P05 -- 62G05; Mathematical statistics -- Periodicals; Mathematics; Statistics; 519.2
• DOI: 10.1080/03610926.2018.1440595
• Languages: English
• ISSNs: 0361-0926

Abstract

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.