Regressions with Berkson errors in covariates—A nonparametric approach

Regressions with Berkson errors in covariates – A nonparametric approach

This paper establishes that so-called instrumental variables enable the identification and the estimation of a fully nonparametric regression model with Berkson-type measurement error in the regressors. An estimator is proposed and proven to be consistent. Its practical performance and feasibility are investigated via Monte Carlo simulations as well as through an epidemiological application inv...

Non-Gaussian Berkson errors in bioassay.

The experimental design plays an important role in every experimental study. However, if errors in the settings of the studied factors cannot be avoided, i.e. Berkson errors occur, the estimates of the model parameters may be biased and the variability in the study increased. Correction methods for the effect of Berkson errors are compared. The emphasis is on the study of correlated Berkson err...

Minimum Distance Regression Model Checking with Berkson Measurement Errors

Lack-of-fit testing of a regression model with Berkson measurement error has not been discussed in the literature to date. To fill this void, we propose a class of tests based on minimized integrated square distances between a nonparametric regression function estimator and the parametric model being fitted. We prove asymptotic normality of these test statistics under the null hypothesis and th...

A Comparative Study Of Parametric And Nonparametric Regressions

Measurement Errors Regression: a Nonparametric Approach

Nonparametric regression provides a useful tool for exploring the association between the responses and covariates. In many applications, the actual values of the covariates are not known, but are measured with errors or through surrogates. In this paper, we present nonparametric regression techniques to study the association between the response and the underlying unobserved covariate. Deconvo...