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A test for the two-sample problem based on empirical characteristic functions

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  • Alba Fernández, V.
  • Jiménez Gamero, M.D.
  • Muñoz Garcia, J.

Abstract

A class of tests for the two sample problem that is based on the empirical characteristic function is investigated. They can be applied to continuous as well as to discrete data of any arbitrary fixed dimension. The tests are consistent against any fixed alternatives for adequate choices of the weight function involved in the definition of the test statistic. Both the bootstrap and the permutation procedures can be employed to estimate consistently the null distribution. The goodness of these approximations and the power of some tests in this class for finite sample sizes are investigated by simulation.

Suggested Citation

  • Alba Fernández, V. & Jiménez Gamero, M.D. & Muñoz Garcia, J., 2008. "A test for the two-sample problem based on empirical characteristic functions," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3730-3748, March.
    • Handle: RePEc:eee:csdana:v:52:y:2008:i:7:p:3730-3748
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    References listed on IDEAS

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    Cited by:

    1. Sangyeol Lee & Simos G. Meintanis & Minyoung Jo, 2019. "Inferential procedures based on the integrated empirical characteristic function," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 103(3), pages 357-386, September.
    2. Meintanis, Simos G. & Ushakov, Nikolai G., 2016. "Nonparametric probability weighted empirical characteristic function and applications," Statistics & Probability Letters, Elsevier, vol. 108(C), pages 52-61.
    3. Xu Li & Wenjuan Hu & Baoxue Zhang, 2023. "Measuring and testing homogeneity of distributions by characteristic distance," Statistical Papers, Springer, vol. 64(2), pages 529-556, April.
    4. Martínez-Camblor, Pablo & de Uña-Álvarez, Jacobo, 2009. "Non-parametric k-sample tests: Density functions vs distribution functions," Computational Statistics & Data Analysis, Elsevier, vol. 53(9), pages 3344-3357, July.
    5. Pablo Martínez-Camblor, 2010. "Comparing k-independent and right censored samples based on the likelihood ratio," Computational Statistics, Springer, vol. 25(3), pages 363-374, September.

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