Bayesian quantile regression models for heavy tailed bounded variables using the No-U-Turn sampler

Oliveira, Érika Arantes de; Castro, Mário de; Bayes, Cristian L.; Bazán, Jorge L.

doi:10.1007/s00180-022-01297-2

Comput Stat

2022

DOI: 10.1007/s00180-022-01297-2

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Bayesian quantile regression models for heavy tailed bounded variables using the No-U-Turn sampler

Érika Arantes de Oliveira

Mário de Castro

Cristian L. Bayes

et al.

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2024

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Bayesian inference for unit Gamma distribution

Rocha,

Azevedo,

Mota

et al. 2024

Cad. Pedagógico

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In this article, we develop Bayesian inference for the unit-Gamma (GU) distribution, initially introduced by Grassia (1977). This distribution is highly flexible, allowing it to take various forms within the interval (0,1), encompassing both symmetric and asymmetric shapes. Such flexibility makes it an attractive alternative to traditional distributions in this range, like the Kumaraswamy and beta models. We propose a parameterization based on quantiles, a particularly advantageous approach when dealing with datasets that include outliers, since the median, for instance, is a more robust estimator compared to the mean in such cases. Our work covers parameter estimation, model fit assessment, model comparison, and influence analysis. All procedures were implemented using Markov Chain Monte Carlo (MCMC) methods via Just Another Gibbs Sampling (JAGS) through the R2jags package in R, an open-source software. Furthermore, we demonstrate the effectiveness of this methodology by applying it to a real-world dataset, highlighting its practical utility.

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Bayesian inference for unit Gamma distribution

Rocha,

Azevedo,

Mota

et al. 2024

Cad. Pedagógico

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show abstract

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Bayesian quantile regression models for heavy tailed bounded variables using the No-U-Turn sampler

Cited by 1 publication

References 31 publications

Bayesian inference for unit Gamma distribution

Bayesian inference for unit Gamma distribution

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