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Parametric versus nonparametrics: two alternative methodologies

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  • Erich Lehmann

Abstract

This article compares parametric and nonparametric approaches to statistical inference. It considers their advantages and disadvantages, and their areas of applicability. Although there is no clear comprehensive conclusion, the article finds that in simple problems in which Wilcoxon type tests and estimators apply, they may be recommended as the methods of choice.

Suggested Citation

  • Erich Lehmann, 2009. "Parametric versus nonparametrics: two alternative methodologies," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 21(4), pages 397-405.
    • Handle: RePEc:taf:gnstxx:v:21:y:2009:i:4:p:397-405
    DOI: 10.1080/10485250902842727
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    References listed on IDEAS

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    1. W. R. van Zwet, 1964. "Convex transformations: A new approach to slcewness and kurtosis," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 18(4), pages 433-441, December.
    2. Jiang, Jiming, 1997. "Sharp upper and lower bounds for asymptotic levels of some statistical tests," Statistics & Probability Letters, Elsevier, vol. 35(4), pages 395-400, November.
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    Cited by:

    1. David C Young & Tina Delaney & B Anthony Armson & Cora Fanning, 2020. "Oral misoprostol, low dose vaginal misoprostol, and vaginal dinoprostone for labor induction: Randomized controlled trial," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-17, January.
    2. Amitava Mukherjee & Marco Marozzi, 2019. "A class of percentile modified Lepage-type tests," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 82(6), pages 657-689, August.
    3. Markus Neuh�user, 2015. "Combining the t test and Wilcoxon's rank-sum test," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(12), pages 2769-2775, December.

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