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mmr 2020, 6(1): 99-108 |
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Azimi S S, Farid Rohani M R. Identifying Outlier Observations in Linear - Circular Regression Model. mmr 2020; 6 (1) :99-108
URL: http://mmr.khu.ac.ir/article-1-2790-en.html
URL: http://mmr.khu.ac.ir/article-1-2790-en.html
Abstract: (1964 Views)
One way to identify outlier observations in regression models, is to measure the difference between the observations and their expected values under fitted model. This identification in circular regression, is possible by using of a circular distance. In this paper, the Difference of Means Circular Error statistic that was introduced by Abuzaid et al. [1] for outlier detection in simple circular regression, is applied in linear-circular regression model and the cut-off points of this statistic are obtained by Monte Carlo simulations. In addition, the performance of this statistic is investigated with some simulation studies. Finally, this statistic is applied to identify outlier observations in speed and direction wind data set recorded at Mehrabad weather station in Tehran with parametric Bootstrap simulation method../files/site1/files/61/10.pdf
Keywords: Linear - Circular regression model, Outlier observation, Difference of means circular Error
Type of Study: Research Paper |
Subject:
stat
Received: 2018/06/3 | Revised: 2020/07/8 | Accepted: 2018/12/3 | Published: 2020/04/14 | ePublished: 2020/04/14
Received: 2018/06/3 | Revised: 2020/07/8 | Accepted: 2018/12/3 | Published: 2020/04/14 | ePublished: 2020/04/14
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