Data augmentation based methods for estimating the parameters of the Feller-Pareto Distribution: Theory and applications
- UNCW Author/Contributor (non-UNCW co-authors, if there are any, appear on document)
- Indranil Ghosh (Creator)
- Institution
- The University of North Carolina Wilmington (UNCW )
- Web Site: http://library.uncw.edu/
Abstract: In income and wealth data modeling Pareto distribution and its several variants play an important role. Both univariate and multivariate variations of this model have been extensively used as a suitable model for various non-negative socio-economic variables, for pertinent details, see Arnold (2015). In this article, weconsider the most general Feller-Pareto (FP, in short) distribution, which subsumes all four types of Pareto distributions and show that it can be represented as a mixture of a conditional generalized gamma and an unconditional gamma distribution. Using this strategy, we consider a data augmentation based method (under the envelope of Bayesian paradigm) to estimate the parameters of the FP distribution. This mixture representation allows us to easily derive conditional Jeffery’s type non informative priors. For illustrative purposes, one data set is considered to exhibit the utility of the proposed method.
Data augmentation based methods for estimating the parameters of the Feller-Pareto Distribution: Theory and applications
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Created on 4/1/2024
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Additional Information
- Publication
- Ghosh, Indranil. (2024, March). Data augmentation-based methods for estimating the parameters of the Feller-Pareto distribution: Theory and applications. Research in Statistics, 2 (1), 2318387. DOI: 10.1080/27684520.2024.2318387
- Language: English
- Date: 2024
- Keywords
- Pareto distribution, Feller-Pareto distribution, data augmentation, Bayesian estimation, Convergence of MCMC procedure