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A Weighted Edge-Count Two-Sample Test for Multivariate and Object Data

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  • Hao Chen
  • Xu Chen
  • Yi Su

Abstract

Two-sample tests for multivariate data and non-Euclidean data are widely used in many fields. Parametric tests are mostly restrained to certain types of data that meets the assumptions of the parametric models. In this article, we study a nonparametric testing procedure that uses graphs representing the similarity among observations. It can be applied to any data types as long as an informative similarity measure on the sample space can be defined. The classic test based on a similarity graph has a problem when the two sample sizes are different. We solve the problem by applying appropriate weights to different components of the classic test statistic. The new test exhibits substantial power gains in simulation studies. Its asymptotic permutation null distribution is derived and shown to work well under finite samples, facilitating its application to large datasets. The new test is illustrated through an analysis on a real dataset of network data.

Suggested Citation

  • Hao Chen & Xu Chen & Yi Su, 2018. "A Weighted Edge-Count Two-Sample Test for Multivariate and Object Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1146-1155, July.
    • Handle: RePEc:taf:jnlasa:v:113:y:2018:i:523:p:1146-1155
    DOI: 10.1080/01621459.2017.1307757
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    Cited by:

    1. Paul, Biplab & De, Shyamal K. & Ghosh, Anil K., 2022. "Some clustering-based exact distribution-free k-sample tests applicable to high dimension, low sample size data," Journal of Multivariate Analysis, Elsevier, vol. 190(C).

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