Two-step robust estimator in heteroscedastic regression model in the presence of outliers

Although the ordinary least squares (OLS) estimates are unbiased in the presence of heteroscedasticity, these are no longer efficient. This problem becomes more complicated when the violation of constant error variances comes together with the existence of outliers. The weighted least squares (WLS)...

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主要な著者: Midi, Habshah, Rana, Md. Sohel, Imon, A. H. M. Ramatullah
フォーマット: 論文
言語:English
出版事項: Academy of Economic Studies 2014
オンライン・アクセス:http://psasir.upm.edu.my/id/eprint/35908/1/Two-step%20robust%20estimator%20in%20heteroscedastic%20regression%20model%20in%20the%20presence%20of%20outliers.pdf
http://psasir.upm.edu.my/id/eprint/35908/
http://www.ecocyb.ase.ro/Articles2014_3.htm
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spelling my.upm.eprints.359082018-09-19T01:32:57Z http://psasir.upm.edu.my/id/eprint/35908/ Two-step robust estimator in heteroscedastic regression model in the presence of outliers Midi, Habshah Rana, Md. Sohel Imon, A. H. M. Ramatullah Although the ordinary least squares (OLS) estimates are unbiased in the presence of heteroscedasticity, these are no longer efficient. This problem becomes more complicated when the violation of constant error variances comes together with the existence of outliers. The weighted least squares (WLS) procedure is often used to estimate the regression parameters when heteroscedasticity occurs in the data. But there is evidence that the WLS estimators suffer a huge set back in the presence of outliers. Moreover, the use of the WLS requires a known form of the heteroscedastic errors structures. To rectify this problem, we proposed a new method that we call two step robust weighted least squares (TSRWLS) method where prior information on the structure of the heteroscedastic errors is not required. In the proposed procedure, the robust technique is used twice. Firstly, the robust weights are used for solving the heteroscedasic error and secondly, the robust weighting function is used for eliminating the effect of outliers. The performance of the newly proposed estimator is investigated extensively by real data sets and Monte Carlo simulations. Academy of Economic Studies 2014 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/35908/1/Two-step%20robust%20estimator%20in%20heteroscedastic%20regression%20model%20in%20the%20presence%20of%20outliers.pdf Midi, Habshah and Rana, Md. Sohel and Imon, A. H. M. Ramatullah (2014) Two-step robust estimator in heteroscedastic regression model in the presence of outliers. Economic Computation and Economic Cybernetics Studies and Research, 48 (3). pp. 255-272. ISSN 0424-267X; ESSN: 1842-3264 http://www.ecocyb.ase.ro/Articles2014_3.htm
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 Although the ordinary least squares (OLS) estimates are unbiased in the presence of heteroscedasticity, these are no longer efficient. This problem becomes more complicated when the violation of constant error variances comes together with the existence of outliers. The weighted least squares (WLS) procedure is often used to estimate the regression parameters when heteroscedasticity occurs in the data. But there is evidence that the WLS estimators suffer a huge set back in the presence of outliers. Moreover, the use of the WLS requires a known form of the heteroscedastic errors structures. To rectify this problem, we proposed a new method that we call two step robust weighted least squares (TSRWLS) method where prior information on the structure of the heteroscedastic errors is not required. In the proposed procedure, the robust technique is used twice. Firstly, the robust weights are used for solving the heteroscedasic error and secondly, the robust weighting function is used for eliminating the effect of outliers. The performance of the newly proposed estimator is investigated extensively by real data sets and Monte Carlo simulations.
format Article
author Midi, Habshah
Rana, Md. Sohel
Imon, A. H. M. Ramatullah
spellingShingle Midi, Habshah
Rana, Md. Sohel
Imon, A. H. M. Ramatullah
Two-step robust estimator in heteroscedastic regression model in the presence of outliers
author_facet Midi, Habshah
Rana, Md. Sohel
Imon, A. H. M. Ramatullah
author_sort Midi, Habshah
title Two-step robust estimator in heteroscedastic regression model in the presence of outliers
title_short Two-step robust estimator in heteroscedastic regression model in the presence of outliers
title_full Two-step robust estimator in heteroscedastic regression model in the presence of outliers
title_fullStr Two-step robust estimator in heteroscedastic regression model in the presence of outliers
title_full_unstemmed Two-step robust estimator in heteroscedastic regression model in the presence of outliers
title_sort two-step robust estimator in heteroscedastic regression model in the presence of outliers
publisher Academy of Economic Studies
publishDate 2014
url http://psasir.upm.edu.my/id/eprint/35908/1/Two-step%20robust%20estimator%20in%20heteroscedastic%20regression%20model%20in%20the%20presence%20of%20outliers.pdf
http://psasir.upm.edu.my/id/eprint/35908/
http://www.ecocyb.ase.ro/Articles2014_3.htm
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