Simple and fast generalized - M (GM) estimator and its application to real data set

It is now evident that some robust methods such as MM-estimator do not address the concept of bounded influence function, which means that their estimates still be affected by outliers in the X directions or high leverage points (HLPs), even though they have high efficiency and high breakdown po...

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Main Authors: Habshah Midi, Jayanthi Arasan, Ismaeel, Shelan Saied, Mohammed, Mohammed A
Format: Article
Language:English
Published: Penerbit Universiti Kebangsaan Malaysia 2021
Online Access:http://journalarticle.ukm.my/16924/
http://journalarticle.ukm.my/16924/1/26.pdf
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author Habshah Midi,
Jayanthi Arasan,
Ismaeel, Shelan Saied
Mohammed, Mohammed A
author_facet Habshah Midi,
Jayanthi Arasan,
Ismaeel, Shelan Saied
Mohammed, Mohammed A
author_sort Habshah Midi,
building UKM Institutional Repository
collection Online Access
description It is now evident that some robust methods such as MM-estimator do not address the concept of bounded influence function, which means that their estimates still be affected by outliers in the X directions or high leverage points (HLPs), even though they have high efficiency and high breakdown point (BDP). The Generalized M(GM) estimator, such as the GM6 estimator is put forward with the main aim of making a bound for the influence of HLPs by some weight function. The limitation of GM6 is that it gives lower weight to both bad leverage points (BLPs) and good leverage points (GLPs) which make its efficiency decreases when more GLPs are present in a data set. Moreover, the GM6 takes longer computational time. In this paper, we develop a new version of GM-estimator which is based on simple and fast algorithm. The attractive feature of this method is that it only downs weights BLPs and vertical outliers (VOs) and increases its efficiency. The merit of our proposed GM estimator is studied by simulation study and well-known aircraft data set.
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spelling oai:generic.eprints.org:169242021-06-28T15:55:29Z http://journalarticle.ukm.my/16924/ Simple and fast generalized - M (GM) estimator and its application to real data set Habshah Midi, Jayanthi Arasan, Ismaeel, Shelan Saied Mohammed, Mohammed A It is now evident that some robust methods such as MM-estimator do not address the concept of bounded influence function, which means that their estimates still be affected by outliers in the X directions or high leverage points (HLPs), even though they have high efficiency and high breakdown point (BDP). The Generalized M(GM) estimator, such as the GM6 estimator is put forward with the main aim of making a bound for the influence of HLPs by some weight function. The limitation of GM6 is that it gives lower weight to both bad leverage points (BLPs) and good leverage points (GLPs) which make its efficiency decreases when more GLPs are present in a data set. Moreover, the GM6 takes longer computational time. In this paper, we develop a new version of GM-estimator which is based on simple and fast algorithm. The attractive feature of this method is that it only downs weights BLPs and vertical outliers (VOs) and increases its efficiency. The merit of our proposed GM estimator is studied by simulation study and well-known aircraft data set. Penerbit Universiti Kebangsaan Malaysia 2021-03 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/16924/1/26.pdf Habshah Midi, and Jayanthi Arasan, and Ismaeel, Shelan Saied and Mohammed, Mohammed A (2021) Simple and fast generalized - M (GM) estimator and its application to real data set. Sains Malaysiana, 50 (3). pp. 859-867. ISSN 0126-6039 https://www.ukm.my/jsm/malay_journals/jilid50bil3_2021/KandunganJilid50Bil3_2021.html
spellingShingle Habshah Midi,
Jayanthi Arasan,
Ismaeel, Shelan Saied
Mohammed, Mohammed A
Simple and fast generalized - M (GM) estimator and its application to real data set
title Simple and fast generalized - M (GM) estimator and its application to real data set
title_full Simple and fast generalized - M (GM) estimator and its application to real data set
title_fullStr Simple and fast generalized - M (GM) estimator and its application to real data set
title_full_unstemmed Simple and fast generalized - M (GM) estimator and its application to real data set
title_short Simple and fast generalized - M (GM) estimator and its application to real data set
title_sort simple and fast generalized - m (gm) estimator and its application to real data set
url http://journalarticle.ukm.my/16924/
http://journalarticle.ukm.my/16924/
http://journalarticle.ukm.my/16924/1/26.pdf