Home > Research > Publications & Outputs > Two-sample smooth tests for the equality of dis...

Electronic data

  • BEJ766

    Final published version, 504 KB, PDF document

    Available under license: CC BY: Creative Commons Attribution 4.0 International License


Text available via DOI:

View graph of relations

Two-sample smooth tests for the equality of distributions

Research output: Contribution to Journal/MagazineJournal articlepeer-review

<mark>Journal publication date</mark>4/02/2017
Issue number2
Number of pages39
Pages (from-to)951-989
Publication StatusPublished
<mark>Original language</mark>English


This paper considers the problem of testing the equality of two unspecified distributions. The classical omnibus tests such as the Kolmogorov–Smirnov and Cramér–von Mises are known to suffer from low power against essentially all but location-scale alternatives. We propose a new two-sample test that modifies the Neyman’s smooth test and extend it to the multivariate case based on the idea of projection pursue. The asymptotic null property of the test and its power against local alternatives are studied. The multiplier bootstrap method is employed to compute the critical value of the multivariate test. We establish validity of the bootstrap approximation in the case where the dimension is allowed to grow with the sample size. Numerical studies show that the new testing procedures perform well even for small sample sizes and are powerful in detecting local features or high-frequency components.