Robust Estimation of a Linearized Nonlinear Regression Model with Heteroscedastic Errors:A Simulation Study

A simulation study is used to examine the robustness of some estimators on a linearized nonlinear regression model with heteroscedastic errors, namely the Linearized Ordinary Least Squares (LOLS), Transformed Generalized Least Squares (TGLS) , Linearized Reweighted Least Squares (LRLS) and Transfor...

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Main Author: Midi, Habshah
Format: Article
Language:English
English
Published: Universiti Putra Malaysia Press 1998
Online Access:http://psasir.upm.edu.my/id/eprint/3440/1/Robust_Estimation_of_a_Linearized_Nonlinear_Regression_Model.pdf
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author Midi, Habshah
author_facet Midi, Habshah
author_sort Midi, Habshah
collection UPM
description A simulation study is used to examine the robustness of some estimators on a linearized nonlinear regression model with heteroscedastic errors, namely the Linearized Ordinary Least Squares (LOLS), Transformed Generalized Least Squares (TGLS) , Linearized Reweighted Least Squares (LRLS) and Transformed Linearized Reweighted Least Squares (TLRLS). The latter is a modification of Reweighted Least Squares (RLS) based on Least Median of Squares (LMS). The empirical evidence shows that the first three estimators are not sufficiently robust when the percentage of outliers in the data increases. That is, they do not have a high breakdown point. On the other hand, the modified estimator (TLRLS) has a higher breakdown point than the other three estimators.
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spelling upm.eprints-34402013-05-27T07:08:32Z http://psasir.upm.edu.my/id/eprint/3440/ Robust Estimation of a Linearized Nonlinear Regression Model with Heteroscedastic Errors:A Simulation Study Midi, Habshah A simulation study is used to examine the robustness of some estimators on a linearized nonlinear regression model with heteroscedastic errors, namely the Linearized Ordinary Least Squares (LOLS), Transformed Generalized Least Squares (TGLS) , Linearized Reweighted Least Squares (LRLS) and Transformed Linearized Reweighted Least Squares (TLRLS). The latter is a modification of Reweighted Least Squares (RLS) based on Least Median of Squares (LMS). The empirical evidence shows that the first three estimators are not sufficiently robust when the percentage of outliers in the data increases. That is, they do not have a high breakdown point. On the other hand, the modified estimator (TLRLS) has a higher breakdown point than the other three estimators. Universiti Putra Malaysia Press 1998 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/3440/1/Robust_Estimation_of_a_Linearized_Nonlinear_Regression_Model.pdf Midi, Habshah (1998) Robust Estimation of a Linearized Nonlinear Regression Model with Heteroscedastic Errors:A Simulation Study. Pertanika Journal of Science & Technology, 6 (1). pp. 23-35. ISSN 0128-7680 English
spellingShingle Midi, Habshah
Robust Estimation of a Linearized Nonlinear Regression Model with Heteroscedastic Errors:A Simulation Study
title Robust Estimation of a Linearized Nonlinear Regression Model with Heteroscedastic Errors:A Simulation Study
title_full Robust Estimation of a Linearized Nonlinear Regression Model with Heteroscedastic Errors:A Simulation Study
title_fullStr Robust Estimation of a Linearized Nonlinear Regression Model with Heteroscedastic Errors:A Simulation Study
title_full_unstemmed Robust Estimation of a Linearized Nonlinear Regression Model with Heteroscedastic Errors:A Simulation Study
title_short Robust Estimation of a Linearized Nonlinear Regression Model with Heteroscedastic Errors:A Simulation Study
title_sort robust estimation of a linearized nonlinear regression model with heteroscedastic errors a simulation study
url http://psasir.upm.edu.my/id/eprint/3440/1/Robust_Estimation_of_a_Linearized_Nonlinear_Regression_Model.pdf
work_keys_str_mv AT midihabshah robustestimationofalinearizednonlinearregressionmodelwithheteroscedasticerrorsasimulationstudy