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Activity Number:
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582
- Nonparametric Methods for Statistical Inference
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Type:
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Contributed
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Date/Time:
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Wednesday, July 31, 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 #306418
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Title:
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A Study of Performances of Some Algorithms for Multivariate Data
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Author(s):
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Jin Wang*
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Companies:
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Northern Arizona University
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Keywords:
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Algorithm;
Multivariate data;
Dimension;
Multivariate kurtosis;
Descriptive measure;
Nonparametric multivariate method
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Abstract:
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I modern statistics, almost all statistical methods are implemented through algorithms. Thus performance of a statistical method is directly affected by the algorithm for the method. Here we study two well-known algorithms for multivariate data. It is found that the performances of both algorithms decline as dimension increases. The effect of data shape on the algorithms is also studied. Our finding is that the performances of both algorithms decrease as kurtosis increases. Some adjustments for those algorithms will be discussed as well, along with some new descriptive measures for multivariate data.
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Authors who are presenting talks have a * after their name.
