A new test of discordancy in cylindrical data
Cylindrical data are bivariate data from the combination of circular and linear variables. However, up to now no work has been done on the detection of outlier in cylindrical data. We introduce a definition of outlier for cylindrical data and present a new test of discordancy to detect outlier in th...
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| Main Authors: | , , , |
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| Format: | Article |
| Published: |
Taylor & Francis
2019
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| Subjects: | |
| Online Access: | http://eprints.um.edu.my/23604/ https://doi.org/10.1080/03610918.2018.1458131 |
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| Summary: | Cylindrical data are bivariate data from the combination of circular and linear variables. However, up to now no work has been done on the detection of outlier in cylindrical data. We introduce a definition of outlier for cylindrical data and present a new test of discordancy to detect outlier in this type of data, based on the k-nearest neighbor’s distance. Cut-off points of the new test statistic based on the Johnson-Wehrly distribution are calculated and its performance is examined using simulation. A practical example is presented using wind speed and wind direction data obtained from the Malaysian Meteorological Department. © 2018, © 2018 Taylor & Francis Group, LLC. |
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