A note on corrected scores for logistic regression

Buzas, Jeffrey S.

doi:10.1016/j.spl.2009.08.005

Cited by 3 publications

(1 citation statement)

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“…Moreover, the method of Huang and Wang is designed only for the case of independent additive measurement error. Buzas () presents an approximate corrected score method for the logistic regression model with high efficiency when the covariate effect is moderate, but this method is designed only for the case of independent additive normal error.…”

Section: Introductionmentioning

confidence: 99%

A Regularization Corrected Score Method for Nonlinear Regression Models with Covariate Error

Zucker

Gorfine

et al. 2013

Biometrics

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Summary Many regression analyses involve explanatory variables that are measured with error, and failing to account for this error is well known to lead to biased point and interval estimates of the regression coefficients. We present here a new general method for adjusting for covariate error. Our method consists of an approximate version of the Stefanski-Nakamura corrected score approach, using the method of regularization to obtain an approximate solution of the relevant integral equation. We develop the theory in the setting of classical likelihood models; this setting covers, for example, linear regression, nonlinear regression, logistic regression, and Poisson regression. The method is extremely general in terms of the types of measurement error models covered, and is a functional method in the sense of not involving assumptions on the distribution of the true covariate. We discuss the theoretical properties of the method and present simulation results in the logistic regression setting (univariate and multivariate). For illustration, we apply the method to data from the Harvard Nurses’ Health Study concerning the relationship between physical activity and breast cancer mortality in the period following a diagnosis of breast cancer.

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Section: Introductionmentioning

confidence: 99%