Bayesian Reference Analysis for the Generalized Normal Linear Regression Model
AuthorsTomazella, Vera Lucia Damasceno; orcid: 0000-0002-6780-2089; email: email@example.com
Jesus, Sandra Rêgo; email: firstname.lastname@example.org
Gazon, Amanda Buosi; orcid: 0000-0001-8140-5496; email: email@example.com
Louzada, Francisco; orcid: 0000-0001-7815-9554; email: firstname.lastname@example.org
Nadarajah, Saralees; email: email@example.com
Nascimento, Diego Carvalho; orcid: 0000-0002-3406-4518; email: firstname.lastname@example.org
Rodrigues, Francisco Aparecido; email: email@example.com
Ramos, Pedro Luiz; orcid: 0000-0002-5387-2457; email: firstname.lastname@example.org
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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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