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Abstract Details
Activity Number:
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399
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Type:
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Contributed
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Date/Time:
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Tuesday, July 31, 2012 : 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 - #304565 |
Title:
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A Nonparametric Test of Missing Completely at Random for Incomplete Multivariate Data
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Author(s):
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Yao Yu*+ and Jun Li
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Companies:
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University of California at Riverside and University of California at Riverside
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Address:
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900 University Ave, Riverside, CA, 92507, United States
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Keywords:
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missing data ;
k-sample test ;
nonparametric test
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Abstract:
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Missing values occur in many data sets in the real world, for example, data from clinical trials or survey. Knowing the type of missing mechanisms is critical for adopting appropriate statistical analysis procedure. Many statistical methods assume missing completely at random (MCAR) due to its simplicity. Therefore, it is important to test whether this assumption is satisfied before applying those procedures. In the literature, most of the procedures for testing MCAR were developed under normality assumption, which is sometimes difficult to justify in practice. We propose a nonparametric test of MCAR for incomplete multivariate data, which does not require distributional assumptions. The proposed test is proved to be consistent against all the alternative hypotheses. Simulation shows that the proposed procedure has the Type I error well controlled at the nominal level and also has good power against a variety of alternative hypotheses.
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