A Bayesian Measure of Model Accuracy

Author:

Brunello Gabriel Hideki Vatanabe1,Nakano Eduardo Yoshio1ORCID

Affiliation:

1. Department of Statistics, University of Brasília, Campus Darcy Ribeiro, Asa Norte, Brasília 70910-900, Brazil

Abstract

Ensuring that the proposed probabilistic model accurately represents the problem is a critical step in statistical modeling, as choosing a poorly fitting model can have significant repercussions on the decision-making process. The primary objective of statistical modeling often revolves around predicting new observations, highlighting the importance of assessing the model’s accuracy. However, current methods for evaluating predictive ability typically involve model comparison, which may not guarantee a good model selection. This work presents an accuracy measure designed for evaluating a model’s predictive capability. This measure, which is straightforward and easy to understand, includes a decision criterion for model rejection. The development of this proposal adopts a Bayesian perspective of inference, elucidating the underlying concepts and outlining the necessary procedures for application. To illustrate its utility, the proposed methodology was applied to real-world data, facilitating an assessment of its practicality in real-world scenarios.

Funder

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brazil

Fundação de Apoio à Pesquisa do Distrito Federal

Editais de Auxílio Financeiro DPI/DPG/UnB, DPI/DPG/BCE/UnB and PPGEST/UnB

Publisher

MDPI AG

Reference16 articles.

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3. R Core Team (2023). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing. Available online: https://www.R-project.org/.

4. D’Agostino, R.B. (1986). Goodness-of-Fit-Techniques, CRC Press.

5. Paulino, C.D.M., Turkman, M.A.A., and Murteira, B. (2003). Estatística Bayesiana, Fundação Calouste Gulbenkian.

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