Adaptive significance levels in linear regression models

• Ano de defesa: 2019
• Autor(a) principal: Hoyos, Alejandra Estefanía Patiño
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Adaptive significance levels; Bayes Factor; Bayesian test; Distribuição preditiva; E-value; Fator de Bayes; Linear regression; Níveis de significância adaptativos; P-value; Predictive distribution; Regressão linear; Significance test; Teste Bayesiano; Teste de significância
• Link de acesso: https://www.teses.usp.br/teses/disponiveis/45/45133/tde-07022020-202851/

The Full Bayesian Significance Test (FBST) for precise hypotheses is presented by Pereira and Stern (1999) as a Bayesian alternative to the traditional significance tests based on p-values. With the FBST the authors introduce the e-value as an evidence index in favor of the null hypothesis (H). An important practical issue for the implementation of the FBST is to establish how small the evidence against H must be in order to decide for its rejection. In this work we present a method to find a cutoff value for the evidence in the FBST by minimizing the linear combination of the averaged type-I and type-II error probabilities for a given sample size and also for a given dimensionality of the parameter space. Furthermore, we compare our methodology with the results obtained from the test proposed by Pereira et al. (2017) and Gannon et al. (2019) which presents the P-value as a decision-making evidence measure and includes an adaptive significance level. For that purpose, the scenario of linear regression models under the Bayesian approach is considered.