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A General Method for Dealing with Misclassification in Regression: The Misclassification SIMEX

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  • Helmut Küchenhoff
  • Samuel M. Mwalili
  • Emmanuel Lesaffre

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  • Helmut Küchenhoff & Samuel M. Mwalili & Emmanuel Lesaffre, 2006. "A General Method for Dealing with Misclassification in Regression: The Misclassification SIMEX," Biometrics, The International Biometric Society, vol. 62(1), pages 85-96, March.
    • Handle: RePEc:bla:biomet:v:62:y:2006:i:1:p:85-96
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    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2005.00396.x
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    References listed on IDEAS

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    1. John M. Neuhaus, 2002. "Analysis of Clustered and Longitudinal Binary Data Subject to Response Misclassification," Biometrics, The International Biometric Society, vol. 58(3), pages 675-683, September.
    2. Robert B. Israel & Jeffrey S. Rosenthal & Jason Z. Wei, 2001. "Finding Generators for Markov Chains via Empirical Transition Matrices, with Applications to Credit Ratings," Mathematical Finance, Wiley Blackwell, vol. 11(2), pages 245-265, April.
    3. Mary J. Morrissey & Donna Spiegelman, 1999. "Matrix Methods for Estimating Odds Ratios with Misclassified Exposure Data: Extensions and Comparisons," Biometrics, The International Biometric Society, vol. 55(2), pages 338-344, June.
    4. Samuel M. Mwalili & Emmanuel Lesaffre & Dominique Declerck, 2005. "A Bayesian ordinal logistic regression model to correct for interobserver measurement error in a geographical oral health study," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(1), pages 77-93, January.
    5. White, Halbert, 1982. "Maximum Likelihood Estimation of Misspecified Models," Econometrica, Econometric Society, vol. 50(1), pages 1-25, January.
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    Cited by:

    1. Simon Cheng & Yingmei Xi & Ming-Hui Chen, 2008. "A New Mixture Model for Misclassification With Applications for Survey Data," Sociological Methods & Research, , vol. 37(1), pages 75-104, August.
    2. Gordon Burtch & Edward McFowland III & Mochen Yang & Gediminas Adomavicius, 2023. "EnsembleIV: Creating Instrumental Variables from Ensemble Learners for Robust Statistical Inference," Papers 2303.02820, arXiv.org.
    3. repec:iab:iabfme:201011(en is not listed on IDEAS
    4. Mochen Yang & Edward McFowland & Gordon Burtch & Gediminas Adomavicius, 2022. "Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem," INFORMS Joural on Data Science, INFORMS, vol. 1(2), pages 138-155, October.
    5. Paul S. Albert & Aiyi Liu & Tonja Nansel, 2014. "Efficient logistic regression designs under an imperfect population identifier," Biometrics, The International Biometric Society, vol. 70(1), pages 175-184, March.
    6. Engzell, Per, 2017. "What Do Books in the Home Proxy For? A Cautionary Tale," Working Paper Series 1/2016, Stockholm University, Swedish Institute for Social Research.
    7. Mengke Qiao & Ke-Wei Huang, 2021. "Correcting Misclassification Bias in Regression Models with Variables Generated via Data Mining," Information Systems Research, INFORMS, vol. 32(2), pages 462-480, June.
    8. Wenqi Wu & James Stamey & David Kahle, 2015. "A Bayesian Approach to Account for Misclassification and Overdispersion in Count Data," IJERPH, MDPI, vol. 12(9), pages 1-14, August.
    9. Roberto Mari & Antonello Maruotti, 2022. "A two-step estimator for generalized linear models for longitudinal data with time-varying measurement error," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(2), pages 273-300, June.
    10. Mochen Yang & Gediminas Adomavicius & Gordon Burtch & Yuqing Rena, 2018. "Mind the Gap: Accounting for Measurement Error and Misclassification in Variables Generated via Data Mining," Information Systems Research, INFORMS, vol. 29(1), pages 4-24, March.
    11. Muff, Stefanie & Ott, Manuela & Braun, Julia & Held, Leonhard, 2017. "Bayesian two-component measurement error modelling for survival analysis using INLA—A case study on cardiovascular disease mortality in Switzerland," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 177-193.
    12. Hong Li & Qifan Song & Jianxi Su, 2021. "Robust estimates of insurance misrepresentation through kernel quantile regression mixtures," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 88(3), pages 625-663, September.
    13. 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.
    14. repec:ebl:ecbull:v:3:y:2007:i:36:p:1-19 is not listed on IDEAS

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