Page 378 - Contributed Paper Session (CPS) - Volume 7

CPS2137 Intan Mastura R. et al.
Model (MCRM)
Rambli et al., 2016 [17] DMCEs statistic Circular Regression DM Circular
Model Regression
Model
Kim and SenGupta, [18] Least circular mean – Multivariate – Arc – tangent
2016 square estimation Multiple Circular link model 1
(LCMSE) and Regression and Arc –
asymptotic tangent link
properties of the model 2
LCMSE estimation
Abuzaid and Allahham, [19] Wrapped Cauchy Circular Regression JS Circular
2015 Error Regression
Model
Rambli et al., 2015 [20] COVRATIO statistic Circular Regression DM Circular
Regression
Model
Ibrahim et al., 2013 [7] COVRATIO statistic Circular Regression JS Circular
Regression
Model
Abuzaid et al., 2013 [21] Mean circular error Circular Regression DM Circular
(MCE) statistic Regression
Model
Jurgen A. Doornik, [22] Robust estimation Circular Regression Least trimmed
2011 Square

4. Discussion and Conclusion
At this instant, the studies of circular regression model have become a
familiar area among authors to be explore. This study looks at outlier detection
methodologies in circular regression model. There are many outlier detection
methods have been used to detect and remove the rest of data in the literature
and in practice. Also, most of them are attracted to solve this problem and
intentions to achieve some objectives in their studies. This paper presents a
survey of research method to detect outlier in circular regression model that
cover up 11 paper from 2011 until 2018. The finding displays this study has
contribute to the survey methodology development of statistic to detect
outlier in circular regression model.

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