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SIMEX estimation in parametric modal regression with measurement error

Author

Listed:
  • Shi, Jianhong
  • Zhang, Yujing
  • Yu, Ping
  • Song, Weixing

Abstract

A simulation–extrapolation procedure has been developed for estimating the regression coefficients in a class of parametric modal regression models when the covariates are prone to measurement errors. Large sample properties of the proposed estimator, including the consistency and asymptotic normality, have been thoroughly investigated. Simulation studies and real data applications have been conducted to evaluate its robustness to potential outliers and its effectiveness in reducing bias caused by the measurement error.

Suggested Citation

  • Shi, Jianhong & Zhang, Yujing & Yu, Ping & Song, Weixing, 2021. "SIMEX estimation in parametric modal regression with measurement error," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
    • Handle: RePEc:eee:csdana:v:157:y:2021:i:c:s0167947320302498
    DOI: 10.1016/j.csda.2020.107158
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    References listed on IDEAS

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    1. Salah Khardani & Anne Françoise Yao, 2017. "Non linear parametric mode regression," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(6), pages 3006-3024, March.
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    3. Yiping Yang & Tiejun Tong & Gaorong Li, 2019. "SIMEX estimation for single-index model with covariate measurement error," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 103(1), pages 137-161, March.
    4. Kemp, Gordon C.R. & Santos Silva, J.M.C., 2012. "Regression towards the mode," Journal of Econometrics, Elsevier, vol. 170(1), pages 92-101.
    5. Weixin Yao & Longhai Li, 2014. "A New Regression Model: Modal Linear Regression," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(3), pages 656-671, September.
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    Cited by:

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