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The comparison of standard bootstrap and robust outlier detections procedure in bilinear (1,0,1,1) model

Link to publisher's homepage at https://amci.unimap.edu.my/

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Main Authors: Mohd Isfahani, Ismail, Hazlina, Ali, Sharipah Soaad, Syed Yahaya
Other Authors: md_isfahani@yahoo.com
Format: Article
Language:English
Published: Institute of Engineering Mathematics, Universiti Malaysia Perlis 2021
Subjects:
Bilinear
Bootstrap
MOM
MADn
Tn
Variance
Online Access:http://dspace.unimap.edu.my:80/xmlui/handle/123456789/69393
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spelling my.unimap-693932021-01-26T07:49:20Z The comparison of standard bootstrap and robust outlier detections procedure in bilinear (1,0,1,1) model Mohd Isfahani, Ismail Hazlina, Ali Sharipah Soaad, Syed Yahaya md_isfahani@yahoo.com Bilinear Bootstrap MOM MADn Tn Variance Link to publisher's homepage at https://amci.unimap.edu.my/ Parameter estimation is the most important part in modelling and predicting time series. However, the existence of outliers in the data will affect the estimation, which consequently jeopardizes the validity of the model. Therefore, the existence of outliers in the data must be first detected before the next process can be performed. The best outlier detection procedure can ensure data are free of outliers and achieve the best value parameter estimation. One of the procedures is using the bootstrap method to obtain the variance of the estimated magnitude of outlier effects. The variance found directly from the bootstrap method is called the 'standard' variance. However, the bootstrap method is quite complex in obtaining the variance value. As alternatives, trimming methods involving robust estimators such as a median absolute deviation (MADn) and alternative median-based deviation called Tn in the 'robust' variance calculation are used to replace the 'standard' variance. This method involves direct calculation to obtain the value of the variance from the estimated magnitude of outlier effects. To see the effectiveness of this method, the bilinear (1,0,1,1) model and two robust detection procedures, namely, modified one-step M-estimator (MOM) with MADn and MOM with Tn were used. Later, these two procedures are evaluated and compared with the bootstrap method through simulation studies based on the probability of outlier detection. Through the findings obtained, in general, the standard bootstrap procedure performs better than the robust procedure performance in detecting the existence of outliers in the bilinear (1,0,1,1) model. 2021-01-26T07:49:20Z 2021-01-26T07:49:20Z 2020-12 Article Applied Mathematics and Computational Intelligence (AMCI), vol.9, 2020, pages 39-52 2289-1315 (print) 2289-1323 (online) http://dspace.unimap.edu.my:80/xmlui/handle/123456789/69393 en Institute of Engineering Mathematics, Universiti Malaysia Perlis
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Bilinear
Bootstrap
MOM
MADn
Tn
Variance
spellingShingle Bilinear
Bootstrap
MOM
MADn
Tn
Variance
Mohd Isfahani, Ismail
Hazlina, Ali
Sharipah Soaad, Syed Yahaya
The comparison of standard bootstrap and robust outlier detections procedure in bilinear (1,0,1,1) model
description Link to publisher's homepage at https://amci.unimap.edu.my/
author2 md_isfahani@yahoo.com
author_facet md_isfahani@yahoo.com
Mohd Isfahani, Ismail
Hazlina, Ali
Sharipah Soaad, Syed Yahaya
format Article
author Mohd Isfahani, Ismail
Hazlina, Ali
Sharipah Soaad, Syed Yahaya
author_sort Mohd Isfahani, Ismail
title The comparison of standard bootstrap and robust outlier detections procedure in bilinear (1,0,1,1) model
title_short The comparison of standard bootstrap and robust outlier detections procedure in bilinear (1,0,1,1) model
title_full The comparison of standard bootstrap and robust outlier detections procedure in bilinear (1,0,1,1) model
title_fullStr The comparison of standard bootstrap and robust outlier detections procedure in bilinear (1,0,1,1) model
title_full_unstemmed The comparison of standard bootstrap and robust outlier detections procedure in bilinear (1,0,1,1) model
title_sort comparison of standard bootstrap and robust outlier detections procedure in bilinear (1,0,1,1) model
publisher Institute of Engineering Mathematics, Universiti Malaysia Perlis
publishDate 2021
url http://dspace.unimap.edu.my:80/xmlui/handle/123456789/69393
_version_ 1698698597946621952
score 13.214268

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