ON OUTLIERS DETECTION IN CIRCULAR LOGISTIC REGRESSION

Abuzaid, Ali; El Shekh Ahmed, H

dc.contributor.author	Abuzaid, Ali
dc.contributor.author	El Shekh Ahmed, H
dc.date.accessioned	2021-06-16T09:31:38Z
dc.date.available	2021-06-16T09:31:38Z
dc.date.issued	2021-04-26
dc.identifier.citation	2. Abuzaid, A. H. and El Shekh Ahmed, H. I. (2021). On Outliers Detection in Circular Logistic Regression. Journal of Applied Probability and Statistics, 16 (1), 95-110.	en_US
dc.identifier.uri	http://dspace.alazhar.edu.ps/xmlui/handle/123456789/2703
dc.description.abstract	The modeling of the relationship between a binary variable and circular variables has not been well investigated yet. This article considers the problem of outliers’ detection in the circular logistic regression model; by extending some outliers’ detection methods from the linear to the circular case. A special interest is given to develop the penalized maximum likelihood method as an outlier detection procedure in the context of circular regression modelling. The performance of considered procedures is investigated via simulation. The results show that the performance of detection procedures has a direct relationship with concentration parameter. For illustration, two real meteorological and ecological data sets with small and large concentration parameters are fitted by the circular logistic regression model, where the detection procedures are applied.	en_US
dc.language.iso	en	en_US
dc.publisher	ISOSS Publications	en_US
dc.subject	Coordinate descent algorithm	en_US
dc.subject	SCAD penalty function	en_US
dc.subject	residuals	en_US
dc.subject	row deletion	en_US
dc.subject	wind directions	en_US
dc.title	ON OUTLIERS DETECTION IN CIRCULAR LOGISTIC REGRESSION	en_US