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Non-Uniform Bounds For Nonparametric T Tests

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  • DUFOUR, J-M.

Abstract

This Paper Gives Non-Uniform Bounds on the Tail Areas of the Permutation Distribution of the Usual Student's T Statistic When the Observations Are Independent with Symmetric Distributions. As Opposed to Uniform Bounds, Non-Uniform Bounds Depend on the Observed Sample. It Is Shown That the Non-Uniform Bounds Proposed Are Always Tighter Than Uniform Exponential Bounds Previously Suggested. the Use of the Bounds to Perform Nonparametric T Tests Is Discussed and Numerical Examples Are Presented.
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(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Dufour, J-M., 1988. "Non-Uniform Bounds For Nonparametric T Tests," Cahiers de recherche 8820, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
  • Handle: RePEc:mtl:montec:8820
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    Cited by:

    1. Dufour, Jean-Marie & Farhat, Abdeljelil & Hallin, Marc, 2006. "Distribution-free bounds for serial correlation coefficients in heteroskedastic symmetric time series," Journal of Econometrics, Elsevier, vol. 130(1), pages 123-142, January.
    2. Flores, Renato G, Jr & Szafarz, Ariane, 1997. "Testing the Information Structure of Eastern European Markets: The Warsaw Stock Exchange," Economic Change and Restructuring, Springer, vol. 30(2-3), pages 91-105.
    3. Bryan Campbell & Eric Ghysels, 1997. "An Empirical Analysis of the Canadian Budget Process," Canadian Journal of Economics, Canadian Economics Association, vol. 30(3), pages 553-576, August.
    4. Dufour, Jean-Marie & Taamouti, Abderrahim, 2010. "Exact optimal inference in regression models under heteroskedasticity and non-normality of unknown form," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2532-2553, November.

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    Keywords

    tests ; distribution ; statistics;

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