Minimum distance model checking in Berkson measurement error models with validation data

Abstract

This article studies a minimum distance regression model checking approach in the presence of Berkson measurement errors in covariates without specifying the measurement error density but when external validation data are available. The proposed tests are based on a class of minimized integrated square distances between a nonparametric estimate of the calibrated regression function and the parametric null model being fitted. The asymptotic distributions of these tests under the null hypothesis and against certain alternatives are established. Surprisingly, these asymptotic distributions are the same as in the case of known measurement error density. In comparison, the asymptotic distributions of the corresponding minimum distance estimators of the null model parameters are affected by the estimation of the calibrated regression function. A simulation study shows desirable performance of a member of the proposed class of estimators and tests.

Density Deconvolution with Small Berkson Errors

“…In economics, the household income is usually not precisely collected due to the survey design or data sensitivity. It was described by Kim et al (2016) (see also Geng and Koul (2018)) that when the income data were collected by asking individuals which salary range categories they belong to, then the midpoint of the range interval was used in analysis. In this case, it is wise to assume that the true income fluctuates around the midpoint observation subject to errors.…”