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

Autor:	Moti L. Tiku, Aysen D. Akkaya
Rok vydání:	2008
Předmět:	Control and Systems Engineering Non-linear least squares Statistics Estimator Applied mathematics Generalized least squares Electrical and Electronic Engineering Simple linear regression M-estimator Total least squares Least squares Robust regression Mathematics
Zdroj:	Automatica. 44:407-417
ISSN:	0005-1098
DOI:	10.1016/j.automatica.2007.06.029
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::8d7aa00248c157f0a4b780d0758bb9b8 https://doi.org/10.1016/j.automatica.2007.06.029 Zobrazit plný text záznamu Full Text from ScienceDirect