Mallick, B., Hoffman, F.O. and Carroll, R.J. (2002) Semiparametric Regression Modeling with Mixtures of Berkson and Classical Error, with Application to Fallout from the Nevada Test Site. Biometrics, 58, 13-20.
https://doi.org/10.1111/j.0006-341X.2002.00013.x

has been cited by the following article:

• TITLE: Nonparametric Regression Estimation with Mixed Measurement Errors

  AUTHORS: Zanhua Yin, Fang Liu, Yuanfu Xie

  KEYWORDS: Berkson Error, Classical Error, Deconvolution, Kernel Method, Mixed Measurement Errors

  JOURNAL NAME: Applied Mathematics, Vol.7 No.17, November 30, 2016

  ABSTRACT: We consider the estimation of nonparametric regression models with predictors being measured with a mixture of Berkson and classical errors. In practice, the Berkson error arises when the variable X of interest is unobservable and only a proxy of X can be measured while the inaccuracy related to the observation of the proxy causes an error of classical type. In this paper, we propose two nonparametric estimators of the regression function in the presence of either or both types of errors. We prove the asymptotic normality of our estimators and derive their rates of convergence. The finite-sample properties of the estimators are investigated through simulation studies.