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Activity Number:
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203
- Advances in Nonparametric Testing
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Type:
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Contributed
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Date/Time:
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Monday, August 8, 2022 : 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 #323502
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Title:
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Exact Multivariate Two-Sample Density-Based Empirical Likelihood Ratio Tests Applicable to Retrospective and Group Sequential Studies
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Author(s):
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Li Zou* and Albert Vexler and Gregory Gurevich
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Companies:
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California State University, East Bay and The State University of New York at Buffalo and Shannon College of Engineering
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Keywords:
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Density-based empirical likelihood;
Exact test;
Multivariate two-sample test
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Abstract:
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Nonparametric tests for equality of multivariate distributions are frequently desired in research. It is commonly required that test-procedures based on relatively small samples of vectors accurately control the corresponding Type I Error (TIE) rates. The present paper extends the density-based empirical likelihood technique in order to nonparametrically approximate the most powerful test for the multivariate two-sample (MTS) problem, yielding an exact finite-sample test statistic. We rigorously establish and apply one-to-one-mapping between the equality of vectors distributions and the equality of distributions of relevant univariate linear projections. In this framework, we prove an algorithm that simplifies the use of projection pursuit, employing only a few of the infinitely many linear combinations of observed vectors components. The displayed distribution-free strategy is employed in retrospective and group sequential manners. The asymptotic consistency of the proposed technique is shown. Monte Carlo studies demonstrate that the proposed procedures exhibit extremely high and stable power characteristics across a variety of settings.
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Authors who are presenting talks have a * after their name.
