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Activity Number:
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403
- SPAAC Poster Competition
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, July 30, 2019 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract #304417
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Title:
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The Nonparametric Behrens-Fisher Problem with Dependent Replicates
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Author(s):
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Akash Roy* and FRANK KONIETSCHKE and Solomon W. Harrar
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Companies:
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University of Texas At Dallas and Institut für Biometrie und Klinische Epidemiologie , Charité – Universitätsmedizin Berlin and University of Kentucky
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Keywords:
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CLUSTERED DATA;
NONPARAMETRIC EFFECTS;
RANKS;
TWO SAMPLE PROBLEM;
ASYMPTOTIC;
EMPIRICAL DISTRIBUTION
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Abstract:
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Statistical comparison of two independent groups are one of the most frequently occurring inference problems in scientific research. Most of the existing methods available in the literature are not applicable when measurements are taken with dependent replicates, for example when visual acuity or any blood parameters of mice sharing the same cage are measured. In all of these scenarios the replicates should neither be assumed to be independent nor be seen as observations coming from different subjects. Furthermore, using a summary measure of the replicates as a single observation would decrease precision of the effect estimates and thus decrease the powers of the test procedures. So, a solution is proposed for these two sample problems with correlated replicates. Weighted as well as unweighted versions of the estimators of the treatment effects are investigated and their asymptotic distributions are derived in a closed form. Furthermore, major attention will be given to the accuracy of the tests in terms of controlling the nominal type-I error level as well as their powers when sample size are rather small. Extensive simulation studies show favorable performance of the new methods.
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Authors who are presenting talks have a * after their name.
