Functional and Structural Methods with Mixed Measurement Error and Misclassification in Covariates
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Date
2015
Author(s)
Yi, Grace
Ma, Yanyuan
Spiegelman, Donna
Carroll, Raymond
Publisher
Taylor & Francis
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Citation
Yi, Grace Y., Yanyuan Ma, Donna Spiegelman, and Raymond J. Carroll. 2015. “Functional and Structural Methods With Mixed Measurement Error and Misclassification in Covariates.” Journal of the American Statistical Association 110 (510): 681–96. https://doi.org/10.1080/01621459.2014.922777.
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.
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