Bayesian Reference Analysis for the Generalized Normal Linear Regression Model
AuthorsTomazella, Vera Lucia Damasceno; orcid: 0000-0002-6780-2089; email: firstname.lastname@example.org
Jesus, Sandra Rêgo; email: email@example.com
Gazon, Amanda Buosi; orcid: 0000-0001-8140-5496; email: firstname.lastname@example.org
Louzada, Francisco; orcid: 0000-0001-7815-9554; email: email@example.com
Nadarajah, Saralees; email: firstname.lastname@example.org
Nascimento, Diego Carvalho; orcid: 0000-0002-3406-4518; email: email@example.com
Rodrigues, Francisco Aparecido; email: firstname.lastname@example.org
Ramos, Pedro Luiz; orcid: 0000-0002-5387-2457; email: email@example.com
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AbstractThis article proposes the use of the Bayesian reference analysis to estimate the parameters of the generalized normal linear regression model. It is shown that the reference prior led to a proper posterior distribution, while the Jeffreys prior returned an improper one. The inferential purposes were obtained via Markov Chain Monte Carlo (MCMC). Furthermore, diagnostic techniques based on the Kullback–Leibler divergence were used. The proposed method was illustrated using artificial data and real data on the height and diameter of Eucalyptus clones from Brazil.
CitationSymmetry, volume 13, issue 5, page e856
DescriptionFrom MDPI via Jisc Publications Router
History: accepted 2021-04-29, pub-electronic 2021-05-12
Publication status: Published
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