On Errors-in-Variables in Binary Regression-Berkson Case

DOI10.2307/2289299zbMath0701.62102OpenAlexW4256262339MaRDI QIDQ3479427

Deborah Burr

Publication date: 1988

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.2307/2289299

zbMATH Keywords

maximum likelihood estimatorsimulation studyslopeprobit regressiontoxicityintercepterrors-in-variables problembinary regression modelcarcinogenicityBerkson casenormally distributed errors of observationQuantal bioassay

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Applications of statistics to biology and medical sciences; meta analysis (62P10) Linear inference, regression (62J99)

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Identifiability of logistic regression with homoscedastic error: Berkson model ⋮ Binary Regression Analysis with Pooled Exposure Measurements: A Regression Calibration Approach ⋮ Improved regression calibration ⋮ Spatial statistics in the presence of location error with an application to remote sensing of the environment ⋮ Reverse attenuation in interaction terms due to covariate measurement error ⋮ Instrumental variable estimation in a probit measurement error model ⋮ Orthogonal regression: a teaching perspective ⋮ Logistic regression with homoscedastic errors—A Berkson model ⋮ Applied regression analysis bibliography update 1988-89 ⋮ Berkson measurement error in designed repeated measures studies with random coefficients

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