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Nonparametric Location Estimators in the Randomized Complete Block Design

In: Modern Nonparametric, Robust and Multivariate Methods

Author

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  • Stefanie Hayoz

    (University of Bern, Institute of Mathematical Statistics and Actuarial Science
    Swiss Group for Clinical Cancer Research (SAKK), Now at Statistics Unit)

  • Jürg Hüsler

    (University of Bern, Institute of Mathematical Statistics and Actuarial Science)

Abstract

Several tests for the comparison of different groups in the randomized complete block design exist. However, there is a lack of robust estimators for the location difference between one group and all the others on the original scale. The relative marginal effects are commonly used in this situation, but they are more difficult to interpret and use by less experienced people because of the different scale. In this paper two nonparametric estimators for the comparison of one group against the others in the randomized complete block design will be presented. Theoretical results such as asymptotic normality, consistency, translation invariance, scale preservation, unbiasedness, and median unbiasedness are derived. The finite sample behavior of these estimators is derived by simulations of different scenarios. In addition, possible confidence intervals with these estimators are discussed and their behavior derived also by simulations.

Suggested Citation

  • Stefanie Hayoz & Jürg Hüsler, 2015. "Nonparametric Location Estimators in the Randomized Complete Block Design," Springer Books, in: Klaus Nordhausen & Sara Taskinen (ed.), Modern Nonparametric, Robust and Multivariate Methods, edition 1, chapter 0, pages 47-68, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-22404-6_4
    DOI: 10.1007/978-3-319-22404-6_4
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