Forthcoming

Selection of Objective Priors for Gumbel Distribution Parameters with Application to Maximum Rainfall Data

Accepted - September 2024

Authors

  • Fernando A. Moala Department of Statistics, State University of Sao Paulo, Brazil
  • Adriano B. Moala Department of Statistics, State University of Sao Paulo, Brazil
  • Nixon Jerez Lillo Pontifical Catholic University of Chile
  • Pedro L. Ramos Faculty of Mathematics, Pontifical Catholic University of Chile, Chile

Keywords:

Gumbel distribution, Bayesian inference, objective priors, reference prior

Abstract

The selection of priors is a critical aspect of Bayesian analysis, although the literature lacks studies concerning the application of the Gumbel distribution using different objective priors. We derive objective priors for the two-parameter Gumbel distribution and present a fully Bayesian analysis. Our primary goal is to choose a prior that represents a state of "little knowledge" a priori for both parameters. To yield this, we implement Markov Chain Monte Carlo algorithms to sample from the posterior distribution and to calculate the Bayes estimators. This investigation is made in the context of extreme weather events, using maximum rainfall data.

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Published

2024-09-30

How to Cite

Moala, F. A., Moala, A. B., Jerez Lillo, N., & Ramos, P. L. (2024). Selection of Objective Priors for Gumbel Distribution Parameters with Application to Maximum Rainfall Data: Accepted - September 2024. REVSTAT-Statistical Journal. Retrieved from https://revstat.ine.pt/index.php/REVSTAT/article/view/648

Issue

new article

Section

Forthcoming Paper

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Copyright (c) 2024 REVSTAT-Statistical Journal

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This work is licensed under a Creative Commons Attribution 4.0 International License.