Some multivariate goodness-of-fit tests based on data depth |
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| Authors: | Caiya Zhang Yanbiao Xiang Xinmei Shen |
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| Institution: | 1. School of Computer and Computing Science , Zhejiang University City College , Hangzhou, People’s Republic of China;2. Department of Mathematics , Zhejiang University , Hangzhou, People’s Republic of China;3. School of Mathematical Sciences , Dalin University of Technology , Dalin, People’s Republic of China |
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| 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. |
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| Keywords: | multivariate goodness-of-fit test data depth Kolmogrov–Sminorv test Anderson–Darling statistic multivariate spacings |
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