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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| Format: | Article |
| Language: | English |
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Penerbit Universiti Kebangsaan Malaysia
2019
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| Online Access: | http://journalarticle.ukm.my/13750/ http://journalarticle.ukm.my/13750/1/25%20Najla%20Ahmed%20Alkasadi.pdf |
| _version_ | 1848813364867760128 |
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| author | Najla Ahmed Alkasadi, Safwati Ibrahim, Abuzaid, Ali H.M. Mohd Irwan Yusoff, Hashibah Hamid, Leow, Wai Zhe Amelia Abd Razak, |
| 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, |
| building | UKM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T00:17:01Z |
| format | Article |
| id | oai:generic.eprints.org:13750 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T00:17:01Z |
| publishDate | 2019 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org: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 |
| 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 |
| title | 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_short | Outlier detection in multiple circular regression model using DFFITC statistic |
| title_sort | outlier detection in multiple circular regression model using dffitc statistic |
| url | http://journalarticle.ukm.my/13750/ http://journalarticle.ukm.my/13750/ http://journalarticle.ukm.my/13750/1/25%20Najla%20Ahmed%20Alkasadi.pdf |