Estimation of generalized partially linear models with measurement error using sufficiency scores

We study the partially linear model in logistic and other types of canonical exponential family regression when the explanatory variable is measured with independent normal error. We develop a backfitting estimation procedure to this model based upon the parametric idea of sufficiency scores so that no assumptions are made about the latent variable measured with error. We derive the method's asymptotic properties and present a numerical example and a simulation study.