| Full text | |
| Author(s): |
Ceregatti, Rafael de C.
;
Izbicki, Rafael
;
Salasar, Luis Ernesto B.
;
Polpo, A
;
Stern, J
;
Louzada, F
;
Izbicki, R
;
Takada, H
Total Authors: 8
|
| Document type: | Journal article |
| Source: | BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING, MAXENT 37; v. 239, p. 11-pg., 2018-01-01. |
| Abstract | |
In this work, we propose a novel nonparametric Bayesian approach to the so-called two-sample problem. Let X-1, . . . , X-n and Y-1, . . . , Y-m be two independent i.i.d samples generated from P-1 and P-2, respectively. Using a nonparametric prior distribution for (P-1, P-2), we propose a new evidence index for the null hypothesis H-0 : P-1 = P-2 based on the posterior distribution of the distance d(P-1, P-2) between P-1 and P-2. This evidence index is easy to compute, has an intuitive interpretation, and can also be justified from a Bayesian decision-theoretic framework. We provide a simulation study to show that our method achieves greater power than the Kolmogorov-Smirnov and the Wilcoxon tests in several settings. Finally, we apply the method to a dataset on Alzheimer's disease. (AU) | |
| FAPESP's process: | 17/03363-8 - Interpretability and efficiency in hypothesis tests |
| Grantee: | Rafael Izbicki |
| Support Opportunities: | Regular Research Grants |