A comparison between two discordancy tests to identify outlier in Wrapped Normal (WN) samples

This study focuses on comparing the performance of the Robust Circular Distance (RCDU*) (simplified version) and A statistics in detecting a single outlier in the Wrapped Normal (WN) samples. Firstly, this study proposes a simplified version of RCDU statistic. Then, the paper generates the cut-off p...

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Main Authors: Nurisha Mohd Zulkefli, Adzhar Rambli, Mohamad Ismeth Khan Azhar Suhaimi, Ibrahim Mohamed, Raiha Shazween Redzuan
Format: Article
Language:English
Published: Penerbit Universiti Kebangsaan Malaysia 2023
Online Access:http://journalarticle.ukm.my/22599/
http://journalarticle.ukm.my/22599/1/STT%2019.pdf
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author Nurisha Mohd Zulkefli,
Adzhar Rambli,
Mohamad Ismeth Khan Azhar Suhaimi,
Ibrahim Mohamed,
Raiha Shazween Redzuan,
author_facet Nurisha Mohd Zulkefli,
Adzhar Rambli,
Mohamad Ismeth Khan Azhar Suhaimi,
Ibrahim Mohamed,
Raiha Shazween Redzuan,
author_sort Nurisha Mohd Zulkefli,
building UKM Institutional Repository
collection Online Access
description This study focuses on comparing the performance of the Robust Circular Distance (RCDU*) (simplified version) and A statistics in detecting a single outlier in the Wrapped Normal (WN) samples. Firstly, this study proposes a simplified version of RCDU statistic. Then, the paper generates the cut-off points for both statistics taken from WN samples via a simulation study. This study also evaluates the performance of both statistics using the proportion of a correct outlier detection. As a result, for a small sample size, the performance of RCDU* and A statistics do not have a huge difference. However, for a large sample size of n=250, A statistic performs slightly better than RCDU* statistic. As an illustration of a practical example, both statistics successfully detected one outlier present in the wind direction data at Kota Bharu station.
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institution Universiti Kebangasaan Malaysia
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spelling oai:generic.eprints.org:225992023-11-27T01:59:32Z http://journalarticle.ukm.my/22599/ A comparison between two discordancy tests to identify outlier in Wrapped Normal (WN) samples Nurisha Mohd Zulkefli, Adzhar Rambli, Mohamad Ismeth Khan Azhar Suhaimi, Ibrahim Mohamed, Raiha Shazween Redzuan, This study focuses on comparing the performance of the Robust Circular Distance (RCDU*) (simplified version) and A statistics in detecting a single outlier in the Wrapped Normal (WN) samples. Firstly, this study proposes a simplified version of RCDU statistic. Then, the paper generates the cut-off points for both statistics taken from WN samples via a simulation study. This study also evaluates the performance of both statistics using the proportion of a correct outlier detection. As a result, for a small sample size, the performance of RCDU* and A statistics do not have a huge difference. However, for a large sample size of n=250, A statistic performs slightly better than RCDU* statistic. As an illustration of a practical example, both statistics successfully detected one outlier present in the wind direction data at Kota Bharu station. Penerbit Universiti Kebangsaan Malaysia 2023 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/22599/1/STT%2019.pdf Nurisha Mohd Zulkefli, and Adzhar Rambli, and Mohamad Ismeth Khan Azhar Suhaimi, and Ibrahim Mohamed, and Raiha Shazween Redzuan, (2023) A comparison between two discordancy tests to identify outlier in Wrapped Normal (WN) samples. Sains Malaysiana, 52 (7). pp. 2139-2148. ISSN 0126-6039 https://www.ukm.my/jsm/
spellingShingle Nurisha Mohd Zulkefli,
Adzhar Rambli,
Mohamad Ismeth Khan Azhar Suhaimi,
Ibrahim Mohamed,
Raiha Shazween Redzuan,
A comparison between two discordancy tests to identify outlier in Wrapped Normal (WN) samples
title A comparison between two discordancy tests to identify outlier in Wrapped Normal (WN) samples
title_full A comparison between two discordancy tests to identify outlier in Wrapped Normal (WN) samples
title_fullStr A comparison between two discordancy tests to identify outlier in Wrapped Normal (WN) samples
title_full_unstemmed A comparison between two discordancy tests to identify outlier in Wrapped Normal (WN) samples
title_short A comparison between two discordancy tests to identify outlier in Wrapped Normal (WN) samples
title_sort comparison between two discordancy tests to identify outlier in wrapped normal (wn) samples
url http://journalarticle.ukm.my/22599/
http://journalarticle.ukm.my/22599/
http://journalarticle.ukm.my/22599/1/STT%2019.pdf