Ranking procedures for matched pairs with missing data — Asymptotic theory and a small sample approximation

F. Konietschke, S.W. Harrar, K. Lange and E. Brunner

Computational Statistics & Data Analysis, 2012, vol. 56, issue 5, 1090-1102

Abstract: Nonparametric methods for matched pairs with data missing completely at random are considered. It is not assumed that the observations are coming from distribution functions belonging to a certain parametric or semi-parametric family. In particular, the distributions can have different shapes under the null hypothesis. Hence, the so-called nonparametric Behrens–Fisher problem for matched pairs with missing data is considered. Moreover, a new approach for confidence intervals for nonparametric effects is presented. In particular, no restriction on the ratio of the number of complete and incomplete cases is required to derive the asymptotic results. Simulations show that for arbitrary settings of complete data and missing values, the resulting confidence intervals maintain the pre-assigned coverage probability quite accurately. Regarding the power, none of the proposed tests is uniformly superior to the other. A real data set illustrates the application.

Keywords: Behrens–Fisher problem; Rank test; Nonparametric hypothesis; Ordered categorical data; Ties; Repeated measures design; Missing values (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:56:y:2012:i:5:p:1090-1102

DOI: 10.1016/j.csda.2011.03.022

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