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DOI:	10.1371/journal.pone.0153074
论文题名:	Procedure for Detecting Outliers in a Circular Regression Model
作者:	Adzhar Rambli; Ali H. M. Abuzaid; Ibrahim Bin Mohamed; Abdul Ghapor Hussin
刊名:	PLOS ONE
ISSN:	1932-6203
出版年:	2016
发表日期:	2016-4-11
卷:	11, 期:4
语种:	英语
英文关键词:	Chronobiology ; Linear regression analysis ; Simulation and modeling ; Blood pressure ; Forecasting ; Fourier analysis ; Meteorology ; Software tools
英文摘要:	A number of circular regression models have been proposed in the literature. In recent years, there is a strong interest shown on the subject of outlier detection in circular regression. An outlier detection procedure can be developed by defining a new statistic in terms of the circular residuals. In this paper, we propose a new measure which transforms the circular residuals into linear measures using a trigonometric function. We then employ the row deletion approach to identify observations that affect the measure the most, a candidate of outlier. The corresponding cut-off points and the performance of the detection procedure when applied on Down and Mardia’s model are studied via simulations. For illustration, we apply the procedure on circadian data.
URL:	http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0153074&type=printable
Citation statistics:
资源类型:	期刊论文
标识符:	http://119.78.100.158/handle/2HF3EXSE/23224
Appears in Collections:	过去全球变化的重建影响、适应和脆弱性科学计划与规划气候变化与战略全球变化的国际研究计划气候减缓与适应气候变化事实与影响

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File Name/ File Size	Content Type	Version	Access	License
journal.pone.0153074.PDF（975KB）	期刊论文	作者接受稿	开放获取	CC BY-NC-SA	View Download

作者单位:

Institute of Mathematical Sciences, University of Malaya, Kuala Lumpur, Malaysia;Department of Mathematics, Faculty of Science, Al-Azhar University-Gaza, Palestine;Institute of Mathematical Sciences, University of Malaya, Kuala Lumpur, Malaysia;Centre for Defence Foundation Studies, National Defence University of Malaysia, Kuala Lumpur, Malaysia

Recommended Citation:

Adzhar Rambli,Ali H. M. Abuzaid,Ibrahim Bin Mohamed,et al. Procedure for Detecting Outliers in a Circular Regression Model[J]. PLOS ONE,2016-01-01,11(4)

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