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On Bayesian approach to composite Pareto models

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  • Muhammad Hilmi Abdul Majid
  • Kamarulzaman Ibrahim

Abstract

In data modelling using the composite Pareto distribution, any observations above a particular threshold value are assumed to follow Pareto type distribution, whereas the rest of the observations are assumed to follow a different distribution. This paper proposes on the use of Bayesian approach to the composite Pareto models involving specification of the prior distribution on the proportion of data coming from the Pareto distribution, instead of assuming the prior distribution on the threshold, as often done in the literature. Based on a simulation study, it is found that the parameter estimates determined when using uniform prior on the proportion is less biased as compared to the point estimates determined when using uniform prior on the threshold. Applications on income data and finance are included for illustrative examples.

Suggested Citation

  • 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.
    • Handle: RePEc:plo:pone00:0257762
    DOI: 10.1371/journal.pone.0257762
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    References listed on IDEAS

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    Cited by:

    1. Mathias Silva & Michel Lubrano, 2023. "Bayesian correction for missing rich using a Pareto II tail with unknown threshold: Combining EU-SILC and WID data," AMSE Working Papers 2320, Aix-Marseille School of Economics, France.
    2. Mathias Silva & Michel Lubrano, 2024. "Bayesian inference for income inequality using a Pareto II tail with an uncertain threshold: Combining EU-SILC and WID data," AMSE Working Papers 2429, Aix-Marseille School of Economics, France.
    3. Marco Bee, 2024. "On discriminating between lognormal and Pareto tail: an unsupervised mixture-based approach," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 18(2), pages 251-269, June.

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