The log-linear model with a generalized gamma distribution for the error: A Bayesian approach
type: Item
instance of: scholarly article
title: The log-linear model with a generalized gamma distribution for the error: A Bayesian approach
zbMATH Open document ID: 0616.62038
DOI: 10.1016/0167-7152(86)90053-2
author: Jorge Alberto Achcar
author: Heleno Bolfarine
published in: Statistics \& Probability Letters
publication date: 1986-01-01T00:00:00Z
review text: Considering a log-linear model with one covariate and a generalized gamma distribution for the error, we find the posterior densities for the parameters of interest. Since many standard survival distributions are particular cases of the generalized gamma model, the proposed Bayesian method is very useful to discriminate between possible models to be used in the data analysis. The Laplace approximation for integrals [see \textit{L. Tierney} and \textit{J. B. Kadane}, J. Am. Stat. Assoc. 81, 82-86 (1986; Zbl 0587.62067)] is used to find the posterior distributions of the parameters involved when they cannot be obtained explicitly.
Mathematics Subject Classification ID: 62F15
Mathematics Subject Classification ID: 62J99
Mathematics Subject Classification ID: 62H17
zbMATH DE Number: 3998985
zbMATH Keywords: log-likelihood
zbMATH Keywords: log-linear model
zbMATH Keywords: generalized gamma distribution
zbMATH Keywords: posterior densities
zbMATH Keywords: survival distributions
zbMATH Keywords: Laplace approximation for integrals
MaRDI profile type: Publication
full work available at URL: 0167-7152(86)90053-2
OpenAlex ID: W2020830344
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description: scientific article; zbMATH DE number 3998985