Functional and Structural Methods with Mixed Measurement Error and Misclassification in Covariates. Academic Article

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

• Carroll, Raymond

published proceedings

• J Am Stat Assoc

author list (cited authors)

• Yi, G. Y., Ma, Y., Spiegelman, D., & Carroll, R. J.

citation count

• 32

complete list of authors

• Yi, Grace Y||Ma, Yanyuan||Spiegelman, Donna||Carroll, Raymond J

publication date

• June 2015

publisher

• Taylor & Francis  Publisher

published in

• JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION  Journal

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

PubMed Central ID

• 26190876

Digital Object Identifier (DOI)

• 10.1080/01621459.2014.922777

start page

• 681

end page

• 696

volume

• 110

issue

• 510

URL

• http%3A%2F%2Fdx.doi.org%2F10.1080%2F01621459.2014.922777