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Semiparametric linear transformation model with differential measurement error and validation sampling

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  • Wang, Xuan
  • Wang, Qihua

Abstract

For the semiparametric linear transformation model with covariate measurement error and validation sampling, we propose an estimation method to estimate the covariate coefficient. The method updates the validation set based estimator to get a more efficient estimator using the data information available on the whole cohort. It can be used to deal with both differential and nondifferential measurement errors. Consistency and asymptotic normality are established for the proposed estimator and a closed form formula is derived for the limiting variance–covariance matrix. Simulation studies and a real data analysis are used to illustrate the performances of the proposed method.

Suggested Citation

  • Wang, Xuan & Wang, Qihua, 2015. "Semiparametric linear transformation model with differential measurement error and validation sampling," Journal of Multivariate Analysis, Elsevier, vol. 141(C), pages 67-80.
    • Handle: RePEc:eee:jmvana:v:141:y:2015:i:c:p:67-80
    DOI: 10.1016/j.jmva.2015.05.017
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    References listed on IDEAS

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    1. Huang, Bin & Wang, Qihua, 2010. "Semiparametric analysis based on weighted estimating equations for transformation models with missing covariates," Journal of Multivariate Analysis, Elsevier, vol. 101(9), pages 2078-2090, October.
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    5. Menggang Yu, 2013. "Adjusted regression estimation for time-to-event data with differential measurement error," Biometrika, Biometrika Trust, vol. 100(3), pages 757-763.
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    7. Samiran Sinha & Yanyuan Ma, 2014. "Semiparametric analysis of linear transformation models with covariate measurement errors," Biometrics, The International Biometric Society, vol. 70(1), pages 21-32, March.
    8. Wang, Qihua, 1999. "Estimation of Partial Linear Error-in-Variables Models with Validation Data," Journal of Multivariate Analysis, Elsevier, vol. 69(1), pages 30-64, April.
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