title: Instrument Assisted Regression for Errors in Variables Models with Binary Response.
abstract: We study errors-in-variables problems when the response is binary and instrumental variables are available. We construct consistent estimators through taking advantage of the prediction relation between the unobservable variables and the instruments. The asymptotic properties of the new estimator are established, and illustrated through simulation studies. We also demonstrate that the method can be readily generalized to generalized linear models and beyond. The usefulness of the method is illustrated through a real data example.
authors.affiliation: Department of Statistics, Texas A&M University.
authors.forename: Kun
authors.initials: K
authors.lastname: Xu
authors.affiliation: Department of Statistics, Texas A&M University.
authors.forename: Yanyuan
authors.initials: Y
authors.lastname: Ma
authors.affiliation: Department of Statistics, University of Manitoba.
authors.forename: Liqun
authors.initials: L
authors.lastname: Wang
keywords: binary response; conditional scores; consistency; errors in variables; generalized linear models; instrumental variables; logistic regression; measurement error; semiparametric efficiency