The identification of outliers in wrapped normal data by using ga statistics
This paper focuses on identifying outliers in the wrapped normal distribution. It is commonly found and when it is dealing with circular data, the existing of outliers will increase several problems.We will be using the existing statistics, the G a statistics to identify a single and patch of outlie...
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| Main Authors: | , , , , |
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| Format: | Article |
| Published: |
Blue Eyes Intelligence Engineering & Sciences Publication
2019
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| Subjects: | |
| Online Access: | http://eprints.um.edu.my/23435/ https://www.ijitee.org/wp-content/uploads/papers/v8i4s/DS2857028419.pdf |
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| Summary: | This paper focuses on identifying outliers in the wrapped normal distribution. It is commonly found and when it is dealing with circular data, the existing of outliers will increase several problems.We will be using the existing statistics, the G a statistics to identify a single and patch of outliers in the wrapped normal data. A Monte Carlo simulation will be carried out to generate the cut-off point’s value. The power performance of the discordancy test in circular data has been investigated. The increment of the contamination level, λ, large value of concentration parameter, ρ and large sample size, n will increase the performance of the outlier detection procedures. In addition, the result shows that the statistics performs well in detecting a patch of outliers in the data. As an illustration a practical example is presented by using the wind direction in Kota Bharu station. As conclusion, the G a statistics successfully detect outlier presence in this data set. © BEIESP. |
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