Outlier detection in multiple circular regression model using DFFITC statistic

This paper presents the identification of outliers in multiple circular regression model (MCRM), where the model studies the relationship between two or more circular variables. To date, most of the published papers concentrating on detecting outliers in circular samples and simple circular regressi...

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Main Authors:	Najla Ahmed Alkasadi, Safwati Ibrahim, Abuzaid, Ali H.M., Mohd Irwan Yusoff, Hashibah Hamid, Leow, Wai Zhe, Amelia Abd Razak
Format:	Article
Language:	English
Published:	Penerbit Universiti Kebangsaan Malaysia 2019
Online Access:	http://journalarticle.ukm.my/13750/ http://journalarticle.ukm.my/13750/ http://journalarticle.ukm.my/13750/1/25%20Najla%20Ahmed%20Alkasadi.pdf

id	ukm-13750
recordtype	eprints
spelling	ukm-137502019-11-29T10:34:13Z http://journalarticle.ukm.my/13750/ Outlier detection in multiple circular regression model using DFFITC statistic Najla Ahmed Alkasadi, Safwati Ibrahim, Abuzaid, Ali H.M. Mohd Irwan Yusoff, Hashibah Hamid, Leow, Wai Zhe Amelia Abd Razak, This paper presents the identification of outliers in multiple circular regression model (MCRM), where the model studies the relationship between two or more circular variables. To date, most of the published papers concentrating on detecting outliers in circular samples and simple circular regression model with one independent circular variable. However, no related studies have been found for more than one independent circular variable. The existence of outliers could alert the sign and change the magnitude of regression coefficients and may lead to inaccurate model development and wrong prediction. Hence, the intention is to develop an outlier detection procedure using DFFITS statistic for circular case. This method has been successfully used in multiple linear regression model. Therefore, the DFFITc statistic for circular variable has been derived. The corresponding critical values and the performance of the procedure are studied via simulations. The results of simulation studies show that the proposed statistic perform well in detecting outliers in MCRM using DFFITc statistic. The proposed statistic was applied to a real data for illustration purposes. Penerbit Universiti Kebangsaan Malaysia 2019-07 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/13750/1/25%20Najla%20Ahmed%20Alkasadi.pdf Najla Ahmed Alkasadi, and Safwati Ibrahim, and Abuzaid, Ali H.M. and Mohd Irwan Yusoff, and Hashibah Hamid, and Leow, Wai Zhe and Amelia Abd Razak, (2019) Outlier detection in multiple circular regression model using DFFITC statistic. Sains Malaysiana, 48 (7). pp. 1557-1563. ISSN 0126-6039 http://www.ukm.my/jsm/malay_journals/jilid48bil7_2019/KandunganJilid48Bil7_2019.html
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Kebangasaan Malaysia
building	UKM Institutional Repository
collection	Online Access
language	English
description	This paper presents the identification of outliers in multiple circular regression model (MCRM), where the model studies the relationship between two or more circular variables. To date, most of the published papers concentrating on detecting outliers in circular samples and simple circular regression model with one independent circular variable. However, no related studies have been found for more than one independent circular variable. The existence of outliers could alert the sign and change the magnitude of regression coefficients and may lead to inaccurate model development and wrong prediction. Hence, the intention is to develop an outlier detection procedure using DFFITS statistic for circular case. This method has been successfully used in multiple linear regression model. Therefore, the DFFITc statistic for circular variable has been derived. The corresponding critical values and the performance of the procedure are studied via simulations. The results of simulation studies show that the proposed statistic perform well in detecting outliers in MCRM using DFFITc statistic. The proposed statistic was applied to a real data for illustration purposes.
format	Article
author	Najla Ahmed Alkasadi, Safwati Ibrahim, Abuzaid, Ali H.M. Mohd Irwan Yusoff, Hashibah Hamid, Leow, Wai Zhe Amelia Abd Razak,
spellingShingle	Najla Ahmed Alkasadi, Safwati Ibrahim, Abuzaid, Ali H.M. Mohd Irwan Yusoff, Hashibah Hamid, Leow, Wai Zhe Amelia Abd Razak, Outlier detection in multiple circular regression model using DFFITC statistic
author_facet	Najla Ahmed Alkasadi, Safwati Ibrahim, Abuzaid, Ali H.M. Mohd Irwan Yusoff, Hashibah Hamid, Leow, Wai Zhe Amelia Abd Razak,
author_sort	Najla Ahmed Alkasadi,
title	Outlier detection in multiple circular regression model using DFFITC statistic
title_short	Outlier detection in multiple circular regression model using DFFITC statistic
title_full	Outlier detection in multiple circular regression model using DFFITC statistic
title_fullStr	Outlier detection in multiple circular regression model using DFFITC statistic
title_full_unstemmed	Outlier detection in multiple circular regression model using DFFITC statistic
title_sort	outlier detection in multiple circular regression model using dffitc statistic
publisher	Penerbit Universiti Kebangsaan Malaysia
publishDate	2019
url	http://journalarticle.ukm.my/13750/ http://journalarticle.ukm.my/13750/ http://journalarticle.ukm.my/13750/1/25%20Najla%20Ahmed%20Alkasadi.pdf
first_indexed	2023-09-18T20:05:33Z
last_indexed	2023-09-18T20:05:33Z
_version_	1777407134162485248

Outlier detection in multiple circular regression model using DFFITC statistic

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