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Nonparametric k-sample test based on kernel density estimator for paired design

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  • Martínez-Camblor, Pablo

Abstract

Comparing whether the marginal distribution functions of a k-dimensional random variable are equal or not is a classical problem in statistical inference. Usually, the parametric ANOVA repeat measures analysis or the nonparametric Friedman test are used. Both procedures allow us to detect differences among the location parameters but not among shapes or spreads of the involved distributions. The statistic which is based on the measure of the common area under the respective kernel density estimators is used in order to compare the equality among the marginal densities of a k-dimensional random variable. The BM algorithm is employed to select, automatically, the final bandwidth parameter. Its statistical power is studied from Monte Carlo simulations and a real data analysis is also considered.

Suggested Citation

  • Martínez-Camblor, Pablo, 2010. "Nonparametric k-sample test based on kernel density estimator for paired design," Computational Statistics & Data Analysis, Elsevier, vol. 54(8), pages 2035-2045, August.
    • Handle: RePEc:eee:csdana:v:54:y:2010:i:8:p:2035-2045
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    References listed on IDEAS

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    1. Li, Ju & Zelterman, Daniel, 2002. "Rank tests of association for exchangeable paired data," Computational Statistics & Data Analysis, Elsevier, vol. 40(1), pages 111-129, July.
    2. U. Munzel, 1999. "Nonparametric methods for paired samples," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 53(3), pages 277-286, November.
    3. Pedro Delicado, 2007. "Functional k-sample problem when data are density functions," Computational Statistics, Springer, vol. 22(3), pages 391-410, September.
    4. Munzel, Ullrich, 1999. "Linear rank score statistics when ties are present," Statistics & Probability Letters, Elsevier, vol. 41(4), pages 389-395, February.
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