title: Functional and Structural Methods with Mixed Measurement Error and Misclassification in Covariates.
abstract: Covariate measurement imprecision or errors arise frequently in many areas. It is well known that ignoring such errors can substantially degrade the quality of inference or even yield erroneous results. Although in practice both covariates subject to measurement error and covariates subject to misclassification can occur, research attention in the literature has mainly focused on addressing either one of these problems separately. To fill this gap, we develop estimation and inference methods that accommodate both characteristics simultaneously. Specifically, we consider measurement error and misclassification in generalized linear models under the scenario that an external validation study is available, and systematically develop a number of effective functional and structural methods. Our methods can be applied to different situations to meet various objectives.
authors.affiliation: Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1.
authors.forename: Grace Y
authors.initials: GY
authors.lastname: Yi
authors.affiliation: Department of Statistics, Texas A&M University, TAMU 3143, College Station, TX 77843-3143, ma@stat.tamu.edu.
authors.forename: Yanyuan
authors.initials: Y
authors.lastname: Ma
authors.affiliation: Departments of Epidemiology and Biostatistics, Harvard School of Public Health, 677 Huntington Ave, Boston, MA 02115, stdls@channing.harvard.edu.
authors.forename: Donna
authors.initials: D
authors.lastname: Spiegelman
authors.affiliation: Department of Statistics, Texas A&M University, TAMU 3143, College Station, TX 77843-3143, and School of Mathematical Sciences, University of Technology, Sydney, Broadway NSW 2007, carroll@stat.tamu.edu.
authors.forename: Raymond J
authors.initials: RJ
authors.lastname: Carroll
keywords: External validation study; Functional measurement error modeling; Generalized linear models; Likelihood method; Measurement error; Misclassification; Regression calibration; Semiparametric regression; Simulation extrapolation algorithm; Structural measurement error modeling