Abstract
In this paper, a Bayesian nonparametric approach to the two-sample problem is proposed. Given two samples X=X1,…,Xm1~i.i.d.F and Y=Y1,…,Ym2~i.i.d.G, with F and G being unknown continuous cumulative distribution functions, we wish to test the null hypothesis H0: F = G. The method is based on computing the Kolmogorov distance between two posterior Dirichlet processes and comparing the results with a reference distance. The parameters of the Dirichlet processes are selected so that any discrepancy between the posterior distance and the reference distance is related to the difference between the two samples. Relevant theoretical properties of the procedure are also developed. Through simulated examples, the approach is compared to the frequentist Kolmogorov–Smirnov test and a Bayesian nonparametric test in which it demonstrates excellent performance.
Original language | English |
---|---|
Pages (from-to) | 212-225 |
Number of pages | 14 |
Journal | Mathematical Methods of Statistics |
Volume | 26 |
Issue number | 3 |
DOIs | |
Publication status | Published - Jul 1 2017 |
Externally published | Yes |
Keywords
- Dirichlet process
- Kolmogorov distance
- goodness-of-fit tests
- two-sample problem
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty