Outlier detection in circular regression model using minimum spanning tree method

The existence of outliers in a circular regression model can lead to many errors, for example in inferences and parameter estimations. Therefore, this study aims to develop new algorithms that can detect outliers by using the minimum spanning tree method. The proposed algorithms are extended from Sa...

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Main Authors:	Nur Faraidah, Muhammad Di, Siti Zanariah, Satari, Roslinazairimah, Zakaria
Format:	Conference or Workshop Item
Language:	English
Published:	Universiti Malaysia Pahang 2019
Subjects:	Q Science (General)
Online Access:	http://umpir.ump.edu.my/id/eprint/24692/ http://umpir.ump.edu.my/id/eprint/24692/1/29.1%20Outlier%20detection%20in%20circular%20regression%20model%20using%20minimum%20spanning%20tree%20method.pdf

id	ump-24692
recordtype	eprints
spelling	ump-246922019-06-27T02:48:40Z http://umpir.ump.edu.my/id/eprint/24692/ Outlier detection in circular regression model using minimum spanning tree method Nur Faraidah, Muhammad Di Siti Zanariah, Satari Roslinazairimah, Zakaria Q Science (General) The existence of outliers in a circular regression model can lead to many errors, for example in inferences and parameter estimations. Therefore, this study aims to develop new algorithms that can detect outliers by using the minimum spanning tree method. The proposed algorithms are extended from Satari’s single-linkage algorithm. The algorithms were examined via simulation studies with different number of sample sizes and level of contaminations. Then, the performances of both algorithms were measured using “success” probability. The results revealed that the proposed methods were performed well and able to detect all the outliers planted in the study. Universiti Malaysia Pahang 2019-01 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/24692/1/29.1%20Outlier%20detection%20in%20circular%20regression%20model%20using%20minimum%20spanning%20tree%20method.pdf Nur Faraidah, Muhammad Di and Siti Zanariah, Satari and Roslinazairimah, Zakaria (2019) Outlier detection in circular regression model using minimum spanning tree method. In: ICOAIMS 2019: 2nd International Conference On Applied & Industrial Mathematics And Statistics 2019, 23 - 25 Julai 2019 , The Zenith Hotel Kuantan. p. 1.. (Unpublished)
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	Q Science (General)
spellingShingle	Q Science (General) Nur Faraidah, Muhammad Di Siti Zanariah, Satari Roslinazairimah, Zakaria Outlier detection in circular regression model using minimum spanning tree method
description	The existence of outliers in a circular regression model can lead to many errors, for example in inferences and parameter estimations. Therefore, this study aims to develop new algorithms that can detect outliers by using the minimum spanning tree method. The proposed algorithms are extended from Satari’s single-linkage algorithm. The algorithms were examined via simulation studies with different number of sample sizes and level of contaminations. Then, the performances of both algorithms were measured using “success” probability. The results revealed that the proposed methods were performed well and able to detect all the outliers planted in the study.
format	Conference or Workshop Item
author	Nur Faraidah, Muhammad Di Siti Zanariah, Satari Roslinazairimah, Zakaria
author_facet	Nur Faraidah, Muhammad Di Siti Zanariah, Satari Roslinazairimah, Zakaria
author_sort	Nur Faraidah, Muhammad Di
title	Outlier detection in circular regression model using minimum spanning tree method
title_short	Outlier detection in circular regression model using minimum spanning tree method
title_full	Outlier detection in circular regression model using minimum spanning tree method
title_fullStr	Outlier detection in circular regression model using minimum spanning tree method
title_full_unstemmed	Outlier detection in circular regression model using minimum spanning tree method
title_sort	outlier detection in circular regression model using minimum spanning tree method
publisher	Universiti Malaysia Pahang
publishDate	2019
url	http://umpir.ump.edu.my/id/eprint/24692/ http://umpir.ump.edu.my/id/eprint/24692/1/29.1%20Outlier%20detection%20in%20circular%20regression%20model%20using%20minimum%20spanning%20tree%20method.pdf
first_indexed	2023-09-18T22:37:31Z
last_indexed	2023-09-18T22:37:31Z
_version_	1777416695148707840

Outlier detection in circular regression model using minimum spanning tree method

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