We present a backfitting algorithm to estimate linear regression mod- els having both error-prone and error-free covariates as predictors. The algorithm assumes that the variance-ratios are known, and it is particulary efficient when several explanatory variables are included. The resulting estimators are shown to be unbiased and to perform well as compared to method-of-moments estimators which are usually employed when the variance ratio is known.
MUGGEO VM (2008). Estimation of linear errors-in-variable models with error free covariates: a backfitting approach. In Proceedings of the 23nd International Workshop on Statistical Modelling (pp.364-367). ipskamp partners, enschede.
Estimation of linear errors-in-variable models with error free covariates: a backfitting approach
MUGGEO, Vito Michele Rosario
2008-01-01
Abstract
We present a backfitting algorithm to estimate linear regression mod- els having both error-prone and error-free covariates as predictors. The algorithm assumes that the variance-ratios are known, and it is particulary efficient when several explanatory variables are included. The resulting estimators are shown to be unbiased and to perform well as compared to method-of-moments estimators which are usually employed when the variance ratio is known.
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