The identification of outliers in wrapped normal data by using ga statistics
Document Type
Article
Publication Date
1-1-2019
Abstract
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.
Keywords
Circular data, G a statistics, Monte carlo simulation, Outliers, Wind direction, Wrapped normal distribution
Divisions
mathematics
Funders
Universiti Teknologi MARA Research Grants (600-IRMI/PERDANA 5/3 BESTARI (P)(042/2018))
Publication Title
International Journal of Innovative Technology and Exploring Engineering
Volume
8
Issue
4S
Publisher
Blue Eyes Intelligence Engineering & Sciences Publication