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Identification of multiple outliers in a generalized linear model with continuous variables

In the statistical analysis of data, a model might be awfully fitted with the presence of outliers. Besides, it has been well established to use residuals for identification of outliers. The asymptotic properties of residuals can be utilized to contribute diagnostic tools. However, it is now evident...

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Main Authors: Loo, Yee Peng, Midi, Habshah, Rana, Md. Sohel, Fitrianto, Anwar
格式: Article
語言:English
出版: Hindawi Publishing Corporation 2016
在線閱讀:http://psasir.upm.edu.my/id/eprint/54484/1/Identification%20of%20multiple%20outliers%20in%20a%20generalized%20linear%20model%20with%20continuous%20variables.pdf
http://psasir.upm.edu.my/id/eprint/54484/
https://www.hindawi.com/journals/mpe/2016/5840523/abs/
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spelling my.upm.eprints.544842018-03-21T01:27:12Z http://psasir.upm.edu.my/id/eprint/54484/ Identification of multiple outliers in a generalized linear model with continuous variables Loo, Yee Peng Midi, Habshah Rana, Md. Sohel Fitrianto, Anwar In the statistical analysis of data, a model might be awfully fitted with the presence of outliers. Besides, it has been well established to use residuals for identification of outliers. The asymptotic properties of residuals can be utilized to contribute diagnostic tools. However, it is now evident that most of the existing diagnostic methods have failed in identifying multiple outliers. Therefore, this paper proposed a diagnostic method for the identification of multiple outliers in GLM, where traditionally used outlier detection methods are effortless as they undergo masking or swamping dilemma. Hence, an investigation was carried out to determine the capability of the proposed GSCPR method. The findings obtained from the numerical examples indicated that the performance of the proposed method was satisfactory for the identification of multiple outliers. Meanwhile, in the simulation study, two scenarios were considered to assess the validity of the proposed method. The proposed method consistently displayed higher percentage of correct detection, as well as lower rates of swamping and masking, regardless of the sample size and the contamination levels. Hindawi Publishing Corporation 2016 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/54484/1/Identification%20of%20multiple%20outliers%20in%20a%20generalized%20linear%20model%20with%20continuous%20variables.pdf Loo, Yee Peng and Midi, Habshah and Rana, Md. Sohel and Fitrianto, Anwar (2016) Identification of multiple outliers in a generalized linear model with continuous variables. Mathematical Problems in Engineering, 2016. art. no. 5840523. pp. 1-9. ISSN 1024-123X; ESSN: 1563-5147 https://www.hindawi.com/journals/mpe/2016/5840523/abs/ 10.1155/2016/5840523
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description In the statistical analysis of data, a model might be awfully fitted with the presence of outliers. Besides, it has been well established to use residuals for identification of outliers. The asymptotic properties of residuals can be utilized to contribute diagnostic tools. However, it is now evident that most of the existing diagnostic methods have failed in identifying multiple outliers. Therefore, this paper proposed a diagnostic method for the identification of multiple outliers in GLM, where traditionally used outlier detection methods are effortless as they undergo masking or swamping dilemma. Hence, an investigation was carried out to determine the capability of the proposed GSCPR method. The findings obtained from the numerical examples indicated that the performance of the proposed method was satisfactory for the identification of multiple outliers. Meanwhile, in the simulation study, two scenarios were considered to assess the validity of the proposed method. The proposed method consistently displayed higher percentage of correct detection, as well as lower rates of swamping and masking, regardless of the sample size and the contamination levels.
format Article
author Loo, Yee Peng
Midi, Habshah
Rana, Md. Sohel
Fitrianto, Anwar
spellingShingle Loo, Yee Peng
Midi, Habshah
Rana, Md. Sohel
Fitrianto, Anwar
Identification of multiple outliers in a generalized linear model with continuous variables
author_facet Loo, Yee Peng
Midi, Habshah
Rana, Md. Sohel
Fitrianto, Anwar
author_sort Loo, Yee Peng
title Identification of multiple outliers in a generalized linear model with continuous variables
title_short Identification of multiple outliers in a generalized linear model with continuous variables
title_full Identification of multiple outliers in a generalized linear model with continuous variables
title_fullStr Identification of multiple outliers in a generalized linear model with continuous variables
title_full_unstemmed Identification of multiple outliers in a generalized linear model with continuous variables
title_sort identification of multiple outliers in a generalized linear model with continuous variables
publisher Hindawi Publishing Corporation
publishDate 2016
url http://psasir.upm.edu.my/id/eprint/54484/1/Identification%20of%20multiple%20outliers%20in%20a%20generalized%20linear%20model%20with%20continuous%20variables.pdf
http://psasir.upm.edu.my/id/eprint/54484/
https://www.hindawi.com/journals/mpe/2016/5840523/abs/
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score 13.251813

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