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Empirical simulation extrapolation for measurement error models with replicate measurements

Author

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  • Devanarayan, Viswanath
  • Stefanski, Leonard A.

Abstract

We present a variation of the simex algorithm (J. Amer. statist. Assoc. 89 (1994) 1314) appropriate for the case in which the measurement error variance(s) are unknown but replicate measurements are available. The method used pseudo errors generated from random linear contrasts of the observed replicate measurements. An attractive feature of the new method is its ability to accommodate heteroscedastic measurement error.

Suggested Citation

  • Devanarayan, Viswanath & Stefanski, Leonard A., 2002. "Empirical simulation extrapolation for measurement error models with replicate measurements," Statistics & Probability Letters, Elsevier, vol. 59(3), pages 219-225, October.
    • Handle: RePEc:eee:stapro:v:59:y:2002:i:3:p:219-225
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    Citations

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    Cited by:

    1. Carroll, Raymond J. & Delaigle, Aurore & Hall, Peter, 2009. "Nonparametric Prediction in Measurement Error Models," Journal of the American Statistical Association, American Statistical Association, vol. 104(487), pages 993-1003.
    2. Raymond J. Carroll & Aurore Delaigle & Peter Hall, 2007. "Non‐parametric regression estimation from data contaminated by a mixture of Berkson and classical errors," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(5), pages 859-878, November.
    3. Yuedong Wang & Yanyuan Ma & Raymond J. Carroll, 2009. "Variance estimation in the analysis of microarray data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 425-445, April.
    4. Li, Mengyan & Ma, Yanyuan & Li, Runze, 2019. "Semiparametric regression for measurement error model with heteroscedastic error," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 320-338.
    5. Chun Wang & Gongjun Xu & Xue Zhang, 2019. "Correction for Item Response Theory Latent Trait Measurement Error in Linear Mixed Effects Models," Psychometrika, Springer;The Psychometric Society, vol. 84(3), pages 673-700, September.
    6. Abhra Sarkar & Bani K. Mallick & Raymond J. Carroll, 2014. "Bayesian semiparametric regression in the presence of conditionally heteroscedastic measurement and regression errors," Biometrics, The International Biometric Society, vol. 70(4), pages 823-834, December.
    7. Sukhbir Singh & Kanchan Jain & Suresh Sharma, 2014. "Replicated measurement error model under exact linear restrictions," Statistical Papers, Springer, vol. 55(2), pages 253-274, May.
    8. David M. Zucker & Malka Gorfine & Yi Li & Mahlet G. Tadesse & Donna Spiegelman, 2013. "A Regularization Corrected Score Method for Nonlinear Regression Models with Covariate Error," Biometrics, The International Biometric Society, vol. 69(1), pages 80-90, March.
    9. John P. Buonaccorsi & Giovanni Romeo & Magne Thoresen, 2018. "Model†based bootstrapping when correcting for measurement error with application to logistic regression," Biometrics, The International Biometric Society, vol. 74(1), pages 135-144, March.
    10. J. R. Lockwood & Daniel F. McCaffrey, 2014. "Correcting for Test Score Measurement Error in ANCOVA Models for Estimating Treatment Effects," Journal of Educational and Behavioral Statistics, , vol. 39(1), pages 22-52, February.
    11. J. R. Lockwood & Daniel F. McCaffrey, 2017. "Simulation-Extrapolation with Latent Heteroskedastic Error Variance," Psychometrika, Springer;The Psychometric Society, vol. 82(3), pages 717-736, September.

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