Outlier detection in multiple circular regression model using DFFITC statistic

• 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/13750/1/25%20Najla%20Ahmed%20Alkasadi.pdf; http://journalarticle.ukm.my/13750/; http://www.ukm.my/jsm/malay_journals/jilid48bil7_2019/KandunganJilid48Bil7_2019.html

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.