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R. J. Carroll, J. D. Maca and D. Ruppert, “Nonparametric Regression in the Presence of Measurement Error,” Biometrika, Vol. 86, No. 3, 1999, pp. 541-554.
http://dx.doi.org/10.1093/biomet/86.3.541

has been cited by the following article:

• TITLE: Estimating the Parameters Geographically Weighted Regression (GWR) with Measurement Error

  AUTHORS: Ida Mariati Hutabarat, Asep Saefuddin, Anik Djuraidah, I Wayan Mangku

  KEYWORDS: Geographical Weighted Regression; Measurement Error; Instrumental Variable; Weighted Least Squares

  JOURNAL NAME: Open Journal of Statistics, Vol.3 No.6, December 31, 2013

  ABSTRACT: Geographically weighted regression models with the measurement error are a modeling method that combines the global regression models with the measurement error and the weighted regression model. The assumptions used in this model are a normally distributed error with that the expectation value is zero and the variance is constant. The purpose of this study is to estimate the parameters of the model and find the properties of these estimators. Estimation is done by using the Weighted Least Squares (WLS) which gives different weighting to each location. The variance of the measurement error is known. Estimators obtained are . The properties of the estimator are unbiased and have a minimum variance.