Robust feasible generalized least squares: A remedial measures of heteroscedasticity
The assumption of equal error variances (homoscedasticity) is one of the important assumptions for Least Squares (LS) method in linear regression. However, in man y practical situations equal error variances are not exist and the problem of heteroscedasticity occurs. As a consequence, although the L...
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| Format: | Article |
| Language: | English |
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Centre for Environment & Socio-Economic Research Publications
2015
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| Online Access: | http://psasir.upm.edu.my/id/eprint/46201/ http://psasir.upm.edu.my/id/eprint/46201/1/Robust%20feasible%20generalized%20least%20squares%3B%20a%20remedial%20measures%20of%20heteroscedasticity.pdf |
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| author | Rana, Sohel Fitrianto, Anwar Khor, Wen Jie Midi, Habshah Imon, A. H. M. R. |
| author_facet | Rana, Sohel Fitrianto, Anwar Khor, Wen Jie Midi, Habshah Imon, A. H. M. R. |
| author_sort | Rana, Sohel |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | The assumption of equal error variances (homoscedasticity) is one of the important assumptions for Least Squares (LS) method in linear regression. However, in man y practical situations equal error variances are not exist and the problem of heteroscedasticity occurs. As a consequence, although the LS method gives unbiased estimate of parameters but it gives biased estimates of the standard errors of the parameters. To overcome this problem of LS method, the Feasible Generalized Least Squares (FGLS) estimator is often suggested in the literature. The FLGS gives unbiased estimate of the parameters and also their standard errors. Nevertheless, there is an evidence that OLS and FLGS estimators suffer a huge set back in the presence of a few atypical observations that we often call outliers. When both outliers and heteroscedasticity exist, the FLGS gives biased estimates and biased standard errors of the parameters. In this article, we proposed to use the Robust Feasible Generalized Least Squares (RFGLS) which his modification of FLGS by incorporating the robust LTS estimator. Numerical results show that the RFLGS offers substantial improvements over the existing FLGS. |
| first_indexed | 2025-11-15T10:09:06Z |
| format | Article |
| id | upm-46201 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T10:09:06Z |
| publishDate | 2015 |
| publisher | Centre for Environment & Socio-Economic Research Publications |
| recordtype | eprints |
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| spelling | upm-462012022-05-31T20:44:44Z http://psasir.upm.edu.my/id/eprint/46201/ Robust feasible generalized least squares: A remedial measures of heteroscedasticity Rana, Sohel Fitrianto, Anwar Khor, Wen Jie Midi, Habshah Imon, A. H. M. R. The assumption of equal error variances (homoscedasticity) is one of the important assumptions for Least Squares (LS) method in linear regression. However, in man y practical situations equal error variances are not exist and the problem of heteroscedasticity occurs. As a consequence, although the LS method gives unbiased estimate of parameters but it gives biased estimates of the standard errors of the parameters. To overcome this problem of LS method, the Feasible Generalized Least Squares (FGLS) estimator is often suggested in the literature. The FLGS gives unbiased estimate of the parameters and also their standard errors. Nevertheless, there is an evidence that OLS and FLGS estimators suffer a huge set back in the presence of a few atypical observations that we often call outliers. When both outliers and heteroscedasticity exist, the FLGS gives biased estimates and biased standard errors of the parameters. In this article, we proposed to use the Robust Feasible Generalized Least Squares (RFGLS) which his modification of FLGS by incorporating the robust LTS estimator. Numerical results show that the RFLGS offers substantial improvements over the existing FLGS. Centre for Environment & Socio-Economic Research Publications 2015 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/46201/1/Robust%20feasible%20generalized%20least%20squares%3B%20a%20remedial%20measures%20of%20heteroscedasticity.pdf Rana, Sohel and Fitrianto, Anwar and Khor, Wen Jie and Midi, Habshah and Imon, A. H. M. R. (2015) Robust feasible generalized least squares: A remedial measures of heteroscedasticity. International Journal of Applied Mathematics and Statistics, 53 (5). pp. 192-199. ISSN 0973-7545; ESSN: 0973-1377 http://www.ceser.in/ceserp/index.php/ijamas/article/view/3725 |
| spellingShingle | Rana, Sohel Fitrianto, Anwar Khor, Wen Jie Midi, Habshah Imon, A. H. M. R. Robust feasible generalized least squares: A remedial measures of heteroscedasticity |
| title | Robust feasible generalized least squares: A remedial measures of heteroscedasticity |
| title_full | Robust feasible generalized least squares: A remedial measures of heteroscedasticity |
| title_fullStr | Robust feasible generalized least squares: A remedial measures of heteroscedasticity |
| title_full_unstemmed | Robust feasible generalized least squares: A remedial measures of heteroscedasticity |
| title_short | Robust feasible generalized least squares: A remedial measures of heteroscedasticity |
| title_sort | robust feasible generalized least squares: a remedial measures of heteroscedasticity |
| url | http://psasir.upm.edu.my/id/eprint/46201/ http://psasir.upm.edu.my/id/eprint/46201/ http://psasir.upm.edu.my/id/eprint/46201/1/Robust%20feasible%20generalized%20least%20squares%3B%20a%20remedial%20measures%20of%20heteroscedasticity.pdf |