Bayesian analysis of mixtures of lognormal distribution with an unknown number of components from grouped data

Kazuhiko Kakamu

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arXiv · 2210.05115

Bayesian analysis of mixtures of lognormal distribution with an unknown number of components from grouped data

Kazuhiko Kakamu

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Abstract

This study proposes a reversible jump Markov chain Monte Carlo method for estimating parameters of lognormal distribution mixtures for income. Using simulated data examples, we examined the proposed algorithm's performance and the accuracy of posterior distributions of the Gini coefficients. Results suggest that the parameters were estimated accurately. Therefore, the posterior distributions are close to the true distributions even when the different data generating process is accounted for. Moreover, promising results for Gini coefficients encouraged us to apply our method to real data from Japan. The empirical examples indicate two subgroups in Japan (2020) and the Gini coefficients' integrity.

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BibTeX RIS

Kazuhiko Kakamu. 2022-10-11. Bayesian analysis of mixtures of lognormal distribution with an unknown number of components from grouped data. https://arxiv.org/abs/2210.05115

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