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Bayesian predictive modeling for Inverse Gamma-Pareto composite distribution

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  • M. S. Aminzadeh
  • M. Deng

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.

Suggested Citation

  • M. S. Aminzadeh & M. Deng, 2019. "Bayesian predictive modeling for Inverse Gamma-Pareto composite distribution," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 48(8), pages 1938-1954, April.
    • Handle: RePEc:taf:lstaxx:v:48:y:2019:i:8:p:1938-1954
    DOI: 10.1080/03610926.2018.1440595
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    Cited by:

    1. Min Deng & Mostafa S. Aminzadeh, 2023. "Bayesian Inference for the Loss Models via Mixture Priors," Risks, MDPI, vol. 11(9), pages 1-27, August.
    2. Muhammad Hilmi Abdul Majid & Kamarulzaman Ibrahim, 2021. "On Bayesian approach to composite Pareto models," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-22, September.
    3. Bowen Liu & Malwane M. A. Ananda, 2022. "A Generalized Family of Exponentiated Composite Distributions," Mathematics, MDPI, vol. 10(11), pages 1-18, June.
    4. Muhammad Hilmi Abdul Majid & Kamarulzaman Ibrahim & Nurulkamal Masseran, 2023. "Three-Part Composite Pareto Modelling for Income Distribution in Malaysia," Mathematics, MDPI, vol. 11(13), pages 1-15, June.
    5. Min Deng & Mostafa Aminzadeh & Min Ji, 2021. "Bayesian Predictive Analysis of Natural Disaster Losses," Risks, MDPI, vol. 9(1), pages 1-23, January.

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