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

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

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.

Suggested Citation

  • Grace Y. Yi & Yanyuan Ma & Donna Spiegelman & Raymond J. Carroll, 2015. "Functional and Structural Methods With Mixed Measurement Error and Misclassification in Covariates," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(510), pages 681-696, June.
    • Handle: RePEc:taf:jnlasa:v:110:y:2015:i:510:p:681-696
    DOI: 10.1080/01621459.2014.922777
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    References listed on IDEAS

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    1. White, Halbert, 1982. "Maximum Likelihood Estimation of Misspecified Models," Econometrica, Econometric Society, vol. 50(1), pages 1-25, January.
    2. Ma, Yanyuan & Ronchetti, Elvezio, 2011. "Saddlepoint Test in Measurement Error Models," Journal of the American Statistical Association, American Statistical Association, vol. 106(493), pages 147-156.
    3. Grace Y. Yi & Yanyuan Ma & Raymond J. Carroll, 2012. "A functional generalized method of moments approach for longitudinal studies with missing responses and covariate measurement error," Biometrika, Biometrika Trust, vol. 99(1), pages 151-165.
    4. Peter Hall & Yanyuan Ma, 2007. "Semiparametric estimators of functional measurement error models with unknown error," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(3), pages 429-446, June.
    5. Anastasios A. Tsiatis & Yanyuan Ma, 2004. "Locally efficient semiparametric estimators for functional measurement error models," Biometrika, Biometrika Trust, vol. 91(4), pages 835-848, December.
    6. David M. Zucker & Donna Spiegelman, 2004. "Inference for the Proportional Hazards Model with Misclassified Discrete-Valued Covariates," Biometrics, The International Biometric Society, vol. 60(2), pages 324-334, June.
    7. Carriquiry, Alicia L. & Fuller, Wayne A., 1996. "A Semiparametric Approach to Estimating Usual Intake Distributions," Staff General Research Papers Archive 1036, Iowa State University, Department of Economics.
    8. John P. Buonaccorsi & Petter Laake & Marit B. Veierød, 2005. "On the Effect of Misclassification on Bias of Perfectly Measured Covariates in Regression," Biometrics, The International Biometric Society, vol. 61(3), pages 831-836, September.
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    Cited by:

    1. Pei Geng & Hira L. Koul, 2019. "Minimum distance model checking in Berkson measurement error models with validation data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(3), pages 879-899, September.
    2. Dlugosz, Stephan & Mammen, Enno & Wilke, Ralf A., 2017. "Generalized partially linear regression with misclassified data and an application to labour market transitions," Computational Statistics & Data Analysis, Elsevier, vol. 110(C), pages 145-159.
    3. Qihuang Zhang & Grace Y. Yi, 2023. "Zero‐inflated Poisson models with measurement error in the response," Biometrics, The International Biometric Society, vol. 79(2), pages 1089-1102, June.
    4. Li‐Pang Chen & Grace Y. Yi, 2021. "Analysis of noisy survival data with graphical proportional hazards measurement error models," Biometrics, The International Biometric Society, vol. 77(3), pages 956-969, September.
    5. Qianqian Wang & Yanyuan Ma & Guangren Yang, 2020. "Locally efficient estimation in generalized partially linear model with measurement error in nonlinear function," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(2), pages 553-572, June.
    6. Liqun Diao & Grace Y. Yi, 2023. "Classification Trees with Mismeasured Responses," Journal of Classification, Springer;The Classification Society, vol. 40(1), pages 168-191, April.
    7. Li-Pang Chen & Grace Y. Yi, 2021. "Semiparametric methods for left-truncated and right-censored survival data with covariate measurement error," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 73(3), pages 481-517, June.

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