A Bayesian nonparametric testing procedure for paired samples

Pereira, Luz; Taylor-Rodríguez, Daniel; Gutiérrez, Luis

Abstract

We propose a Bayesian hypothesis testing procedure for comparing the distributions of paired samples. The procedure is based on a flexible model for the joint distribution of both samples. The flexibility is given by a mixture of Dirichlet processes. Our proposal uses a spike-slab prior specification for the base measure of the Dirichlet process and a particular parametrization for the kernel of the mixture in order to facilitate comparisons and posterior inference. The joint model allows us to derive the marginal distributions and test whether they differ or not. The procedure exploits the correlation between samples, relaxes the parametric assumptions, and detects possible differences throughout the entire distributions. A Monte Carlo simulation study comparing the performance of this strategy to other traditional alternatives is provided. Finally, we apply the proposed approach to spirometry data collected in the United States to investigate changes in pulmonary function in children and adolescents in response to air polluting factors.

Más información

Título de la Revista: Biometrics
Volumen: 76
Número: 4
Editorial: Blackwell Publishing Inc.
Fecha de publicación: 2020
Página de inicio: 1133
Página final: 1146
URL: https://doi.org/10.1111/biom.13234
DOI:

10.1111/biom.13234

Notas: ISI