Robust estimation in multiple linear regression model with non-Gaussian noise

Date

2008-02-01

Author

Akkaya, Ayşen

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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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Citation Formats

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.