Mann-Whitney Test with Adjustments to Pre-treatment Variables for Missing Values and Observational Study
AbstractThe conventional Wilcoxon/Mann-Whitney test can be invalid for comparing treatment effects in the presence of missing values or in observational studies. This is because the missingness of the outcomes or the participation in the treatments may depend on certain pre-treatment variables. We propose an approach to adjust the Mann-Whitney test by correcting the potential bias via consistently estimating the conditional distributions of the outcomes given the pre-treatment variables. We also propose semiparametric extensions of the adjusted Mann-Whitney test which leads to dimension reduction for high dimensional covariate. A novel bootstrap procedure is devised to approximate the null distribution of the test statistics for practical implementations. Results from simulation studies and an economic observational study data analysis are presented to demonstrate the performance of the proposed approach.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 46239.
Date of creation: Jan 2013
Date of revision:
Dimension reduction; Kernel smoothing; Mann-Whitney statistic; Missing outcomes; Observational studies; Selection bias.;
Find related papers by JEL classification:
- C0 - Mathematical and Quantitative Methods - - General
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- C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
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This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-04-20 (All new papers)
- NEP-CWA-2013-04-20 (Central & Western Asia)
- NEP-ECM-2013-04-20 (Econometrics)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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