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1860799649962524672
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| building |
INTELEK Repository
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Online Access
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| collectionurl |
https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072
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| date |
2017-12-27 10:31:16
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| eventvenue |
Universiti Utara Malaysia, Kedah
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| format |
Restricted Document
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| id |
6849
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UniSZA
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1359-01-FH03-FESP-17-11875.jpg
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| person |
norman
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| recordtype |
oai_dc
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https://intelek.unisza.edu.my/intelek/pages/view.php?ref=6849
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| spelling |
6849 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=6849 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Conference Conference Paper image/jpeg inches 96 96 norman 763 1439 1439x763 2017-12-27 10:31:16 45 45 1359-01-FH03-FESP-17-11875.jpg UniSZA Private Access Robust weights of generalized M-estimator for panel data ABSTRACT Ordinary Least Square estimation for panel data suffers biasness in the presence of high leverage points. Robust alternatives are proposed by incorporating new robust weights in Generalized M-estimator; determined by superior outlier detection methods. In this study, Diagnostic Robust Generalized Potential (DRGP) and Robust Diagnostic-F (RDF) are considered to form new weighting schemes for Robust Within GM-estimator. The performance of the newly proposed methods are called RWGM-DRGP and RWGM-RDF and investigated using real and simulated data sets. The ratios of root mean square error are evaluated and compared with the existing RWGM under robust centering procedures. The newly proposed estimators are found to be more efficient and resilient towards high leverage points due to the success of the new robust weights. The results are confirmed through reanalyzing numerical examples. AIP Conference Proceedings Universiti Utara Malaysia, Kedah
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| spellingShingle |
Robust weights of generalized M-estimator for panel data
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| summary |
ABSTRACT Ordinary Least Square estimation for panel data suffers biasness in the presence of high leverage points. Robust alternatives are proposed by incorporating new robust weights in Generalized M-estimator; determined by superior outlier detection methods. In this study, Diagnostic Robust Generalized Potential (DRGP) and Robust Diagnostic-F (RDF) are considered to form new weighting schemes for Robust Within GM-estimator. The performance of the newly proposed methods are called RWGM-DRGP and RWGM-RDF and investigated using real and simulated data sets. The ratios of root mean square error are evaluated and compared with the existing RWGM under robust centering procedures. The newly proposed estimators are found to be more efficient and resilient towards high leverage points due to the success of the new robust weights. The results are confirmed through reanalyzing numerical examples.
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| title |
Robust weights of generalized M-estimator for panel data
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| title_full |
Robust weights of generalized M-estimator for panel data
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| title_fullStr |
Robust weights of generalized M-estimator for panel data
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| title_full_unstemmed |
Robust weights of generalized M-estimator for panel data
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| title_short |
Robust weights of generalized M-estimator for panel data
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| title_sort |
robust weights of generalized m-estimator for panel data
|