Bayesian analysis of the generalized gamma distribution using non-informative priors
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Data
2017-01-01
Autores
Ramos, Pedro L.
Achcar, Jorge A.
Moala, Fernando A. [UNESP]
Ramos, Eduardo
Louzada, Francisco
Título da Revista
ISSN da Revista
Título de Volume
Editor
Taylor & Francis Ltd
Resumo
The Generalized gamma (GG) distribution plays an important role in statistical analysis. For this distribution, we derive non-informative priors using formal rules, such as Jeffreys prior, maximal data information prior and reference priors. We have shown that these most popular formal rules with natural ordering of parameters, lead to priors with improper posteriors. This problem is overcome by considering a prior averaging approach discussed in Berger et al. [Overall objective priors. Bayesian Analysis. 2015;10(1):189-221]. The obtained hybrid Jeffreys-reference prior is invariant under one-to-one transformations and yields a proper posterior distribution. We obtained good frequentist properties of the proposed prior using a detailed simulation study. Finally, an analysis of the maximum annual discharge of the river Rhine at Lobith is presented.
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Palavras-chave
Bayesian analysis, Generalized gamma distribution, Jeffreys prior, Reference prior
Como citar
Statistics. Abingdon: Taylor & Francis Ltd, v. 51, n. 4, p. 824-843, 2017.