Author's: Cuiping Guo, Junhuan Peng and Chuantao Li
Pages: [27] - [34]
Received Date: February 10, 2017
Submitted by: Jianqiang Gao.
DOI: http://dx.doi.org/10.18642/ijamml_7100121817
Linear errors-in-variables (EIV) models with heteroscedastic measurement errors are widely adopted in applied sciences. The linear EIV model estimators, however, can be highly biased by gross errors. This paper focuses on robust estimation in linear homoscedastic EIV models. Robust estimators of the linear homoscedastic EIV models are derived from M-estimators and Lagrange multiplier method.
errors-in-variables, homoscedastic measurement errors, robust estimation, total least squared estimators, M-estimators.