Bivariate Bayesian hypothesis testing with missing data in components

2021 ◽  
Author(s):  
Zhixing Xu ◽  
Hui Quan
Keyword(s):  
Missing Data ◽  
Hypothesis Testing ◽  
Bayesian Hypothesis Testing

scholarly journals A Critical Evaluation of the FBST ev for Bayesian Hypothesis Testing

2021 ◽  
Author(s):  
Alexander Ly ◽  
Eric-Jan Wagenmakers
Keyword(s):  
Hypothesis Testing ◽  
Null Hypothesis ◽  
Alternative Hypothesis ◽  
Critical Evaluation ◽  
Significance Test ◽  
Bayesian Hypothesis Testing ◽  
Absence Of Evidence ◽  
Evidence Of Absence ◽  
Foregone Conclusion

AbstractThe “Full Bayesian Significance Test e-value”, henceforth FBST ev, has received increasing attention across a range of disciplines including psychology. We show that the FBST ev leads to four problems: (1) the FBST ev cannot quantify evidence in favor of a null hypothesis and therefore also cannot discriminate “evidence of absence” from “absence of evidence”; (2) the FBST ev is susceptible to sampling to a foregone conclusion; (3) the FBST ev violates the principle of predictive irrelevance, such that it is affected by data that are equally likely to occur under the null hypothesis and the alternative hypothesis; (4) the FBST ev suffers from the Jeffreys-Lindley paradox in that it does not include a correction for selection. These problems also plague the frequentist p-value. We conclude that although the FBST ev may be an improvement over the p-value, it does not provide a reasonable measure of evidence against the null hypothesis.

scholarly journals Bayesian Hypothesis Testing: A Reference Approach

2002 ◽  
Vol 70 (3) ◽  
pp. 351 ◽  
Author(s):  
Jose M. Bernardo ◽  
Raul Rueda
Keyword(s):  
Hypothesis Testing ◽  
Bayesian Hypothesis Testing
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