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

Pei Geng () and Hira L. Koul ()
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Pei Geng: Illinois State University
Hira L. Koul: Michigan State University

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2019, vol. 28, issue 3, No 15, 879-899

Abstract: 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.

Keywords: Kernel smoothing; Integrated square distance; Two sample U statistic; 62G10 (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s11749-018-0610-6

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