A regularization corrected score method for nonlinear regression models with covariate error.

ABSTRACT: 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

SUBMITTER: Zucker DM 

PROVIDER: S-EPMC3622191 | biostudies-literature | 2013 Mar

REPOSITORIES: biostudies-literature