What Does Objective Mean in a Dirichlet‐multinomial Process?. (17th August 2017)

• Record Type: Journal Article
• Authors: Alvares, Danilo; Armero, Carmen; Forte, Anabel
• Is Part Of: International statistical review. Volume 86:Number 1(2018)
• Page Start: 106
• Page End: 118
• Publication Date: 2017-08-17
• Subjects: Bayesian inference -- non‐informative priors -- posterior mean; Statistics -- Periodicals; Statistics -- Bibliography -- Periodicals; Statistics -- Bibliography; Statistics -- Periodicals; Statistique; Statistique -- Périodiques; Statistique -- Bibliographie -- Périodiques; Statistique -- Étude et enseignement -- Périodiques; Statistique -- Étude et enseignement -- Bibliographie -- Périodiques; Ressource Internet (Descripteur de forme); Périodique électronique (Descripteur de forme); 519.2
• DOI: 10.1111/insr.12231
• Languages: English
• ISSNs: 0306-7734

Abstract

Summary: The Dirichlet‐multinomial process can be seen as the generalisation of the binomial model with beta prior distribution when the number of categories is larger than two. In such a scenario, setting informative prior distributions when the number of categories is great becomes difficult, so the need for an objective approach arises. However, what does objective mean in the Dirichlet‐multinomial process? To deal with this question, we study the sensitivity of the posterior distribution to the choice of an objective Dirichlet prior from those presented in the available literature. We illustrate the impact of the selection of the prior distribution in several scenarios and discuss the most sensible ones.