Functional and Structural Methods With Mixed Measurement Error and Misclassification in Covariates

Grace Y. Yi, Yanyuan Ma, Donna Spiegelman, Raymond J. Carroll

Research output: Contribution to journalArticle

17 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)681-696
Number of pages16
JournalJournal of the American Statistical Association
Volume110
Issue number510
DOIs
StatePublished - Apr 3 2015

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Misclassification
Measurement Error
Covariates
Imprecision
Generalized Linear Model
Scenarios
Measurement error
Inference

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

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Functional and Structural Methods With Mixed Measurement Error and Misclassification in Covariates. / Yi, Grace Y.; Ma, Yanyuan; Spiegelman, Donna; Carroll, Raymond J.

In: Journal of the American Statistical Association, Vol. 110, No. 510, 03.04.2015, p. 681-696.

Research output: Contribution to journalArticle

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