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

Listed author(s):
  • Caiya Zhang
  • Yanbiao Xiang
  • Xinmei Shen
Registered author(s):

    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.

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    Article provided by Taylor & Francis Journals in its journal Journal of Applied Statistics.

    Volume (Year): 39 (2012)
    Issue (Month): 2 (May)
    Pages: 385-397

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    Handle: RePEc:taf:japsta:v:39:y:2012:i:2:p:385-397
    DOI: 10.1080/02664763.2011.594033
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