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Robust estimation in multiple linear regression model with non-Gaussian noise
Date
2008-02-01
Author
Akkaya, Ayşen
Metadata
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Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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The traditional least squares estimators used in multiple linear regression model are very sensitive to design anomalies. To rectify the situation we propose a reparametrization of the model. We derive modified maximum likelihood estimators and show that they are robust and considerably more efficient than the least squares estimators besides being insensitive to moderate design anomalies.
Subject Keywords
Linear regression
,
Robustness
,
Data anomaly
,
Modified maximum likelihood
,
Outliers
URI
https://hdl.handle.net/11511/32710
Journal
AUTOMATICA
DOI
https://doi.org/10.1016/j.automatica.2007.06.029
Collections
Department of Statistics, Article
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BibTeX
A. Akkaya, “Robust estimation in multiple linear regression model with non-Gaussian noise,”
AUTOMATICA
, pp. 407–417, 2008, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/32710.