Minimum distance model checking in Berkson measurement error models with validation data
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Pei Geng [1] ; Hira L. Koul [2]
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[1] Illinois State University
Illinois State University
Township of Normal, Estados Unidos
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[2] Michigan State University
Michigan State University
City of East Lansing, Estados Unidos
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- Localización: Test: An Official Journal of the Spanish Society of Statistics and Operations Research, ISSN-e 1863-8260, ISSN 1133-0686, Vol. 28, Nº. 3, 2019, págs. 879-899
- Idioma: inglés
- DOI: 10.1007/s11749-018-0610-6
- Texto completo no disponible (Saber más ...)
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Resumen
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
