Graph-Based Two-Sample Tests for Data with Repeated Observations

2017/11/12 by Jingru Zhang, Hao Chen, Zhang, Jingru +1
Biochemistry, Genetics and Molecular Biology · Computer Science · Physics and Astronomy · #Advanced Clustering Algorithms Research #Bioinformatics and Genomic Networks #Complex Network Analysis Techniques #FOS: Computer and information sciences #Methodology (stat.ME)

paper · pdf · doi:10.48550/arxiv.1711.04349

openalex publication_date 2017/11/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In the regime of two-sample comparison, tests based on a graph constructed on observations by utilizing similarity information among them is gaining attention due to their flexibility and good performances for high-dimensional/non-Euclidean data. However, when there are repeated observations, these graph-based tests could be problematic as they are versatile to the choice of the similarity graph. We propose extended graph-based test statistics to resolve this problem. The analytic p-value approximations to these extended graph-based tests are derived to facilitate the application of these tests to large datasets. The new tests are illustrated in the analysis of a phone-call network dataset. All tests are implemented in an R package gTests.

Citations

• A test for the mean vector with fewer observations than the dimension

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