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An empirical study of tests for uniformity in multidimensional data

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  • Petrie, Adam
  • Willemain, Thomas R.

Abstract

An important problem in high-dimensional data analysis is determining whether sample points are uniformly distributed (i.e., exhibit complete spatial randomness) over some compact support, or rather possess some underlying structure (e.g., clusters or other nonhomogeneities). We propose two new graph-theoretic tests of uniformity which utilize the minimum spanning tree and a snake (a short non-branching acyclic path connecting each data point). We compare the powers of statistics based on these graphs with other statistics from the literature on an array of non-uniform alternatives in a variety of supports. For data in a hypercube, we find that test statistics based on the minimum spanning tree have superior power when the data displays regularity (e.g., results from an inhibition process). For arbitrarily shaped or unknown supports, we use run length statistics of the sequence of segment lengths along the snake’s path to test uniformity. The snake is particularly useful because no knowledge or estimation of the support is required to compute the test statistic, it can be computed quickly for any dimension, and it shows what kinds of non-uniformities are present. These properties make the snake unique among multivariate tests of uniformity since others only function on specific and known supports, have computational difficulties in high dimension, or have inconsistent type I error rates.

Suggested Citation

  • Petrie, Adam & Willemain, Thomas R., 2013. "An empirical study of tests for uniformity in multidimensional data," Computational Statistics & Data Analysis, Elsevier, vol. 64(C), pages 253-268.
    • Handle: RePEc:eee:csdana:v:64:y:2013:i:c:p:253-268
    DOI: 10.1016/j.csda.2013.02.013
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    References listed on IDEAS

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    1. Justel, Ana & Peña, Daniel & Zamar, Rubén, 1997. "A multivariate Kolmogorov-Smirnov test of goodness of fit," Statistics & Probability Letters, Elsevier, vol. 35(3), pages 251-259, October.
    2. Dale L. Zimmerman, 1993. "A Bivariate Cramér–Von Mises Type of Test for Spatial Randomness," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 42(1), pages 43-54, March.
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    Cited by:

    1. Ebner, Bruno & Henze, Norbert & Yukich, Joseph E., 2018. "Multivariate goodness-of-fit on flat and curved spaces via nearest neighbor distances," Journal of Multivariate Analysis, Elsevier, vol. 165(C), pages 231-242.
    2. Mengta Yang & Reza Modarres, 2017. "Multivariate tests of uniformity," Statistical Papers, Springer, vol. 58(3), pages 627-639, September.

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