2002/03/07 by B. Aslan, Aslan, B., G. Zech +1 · 2 citations
Decision Sciences · Mathematics · Physics and Astronomy · #Advanced Statistical Methods and Models #Advanced Statistical Process Monitoring #Data Analysis #FOS: Physical sciences #High Energy Physics - Experiment (hep-ex) #Statistical Methods and Inference #Statistics and Probability (physics.data-an) #hep-ex #physics.data-an
paper · pdf · doi:10.48550/arxiv.hep-ex/0203010
17 pages,8 figures, corrected version
openalex publication_date 2002/03/07 · arxiv created 2003/04/29 · arxiv updated 2009/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We present a new class of multivariate binning-free and nonparametric goodness-of-fit tests. The test quantity energy is a function of the distances of observed and simulated observations in the variate space. The simulation follows the probability distribution function f0 of the null hypothesis. The distances are weighted with a weighting function which can be adjusted to the variations of f0. We have investigated the power of the test for a uniform and a Gaussian distribution of one or two variates, respectively and compared it to that of conventional tests. The energy test with a Gaussian weighting function is closely related to the Pearson χ2 test but is more powerful in most applications and avoids arbitrary bin boundaries. The test is especially powerful in the multivariate case.