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Some multivariate goodness-of-fit tests based on data depth

Author

Listed:
  • Caiya Zhang
  • Yanbiao Xiang
  • Xinmei Shen

Abstract

Based on data depth, three types of nonparametric goodness-of-fit tests for multivariate distribution are proposed in this paper. They are Pearson’s chi-square test, tests based on EDF and tests based on spacings, respectively. The Anderson--Darling (AD) test and the Greenwood test for bivariate normal distribution and uniform distribution are simulated. The results of simulation show that these two tests have low type I error rates and become more efficient with the increase in sample size. The AD-type test performs more powerfully than the Greenwood type test.

Suggested Citation

  • Caiya Zhang & Yanbiao Xiang & Xinmei Shen, 2012. "Some multivariate goodness-of-fit tests based on data depth," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(2), pages 385-397, May.
  • Handle: RePEc:taf:japsta:v:39:y:2012:i:2:p:385-397
    DOI: 10.1080/02664763.2011.594033
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