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Nonparametric regression estimate with Berkson Laplace measurement error

Author

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  • Shi, Jianhong
  • Bai, Xiuqin
  • Song, Weixing

Abstract

In this paper, a nonparametric estimator for the regression function is constructed when the covariates are contaminated with the multivariate Laplace measurement error. The proposed estimator is based upon a simple relationship between the regression function and the conditional expectation of the regression function given the proxy data, as well as the second derivative of this expectation. Large sample properties of the proposed estimator, including the consistency and asymptotic normality, are established. The theoretical optimal bandwidth based on asymptotic integrated mean squared error is derived, and a data-driven bandwidth selector is recommended. Finite sample performance of the proposed estimator is evaluated by a simulation study.

Suggested Citation

  • Shi, Jianhong & Bai, Xiuqin & Song, Weixing, 2020. "Nonparametric regression estimate with Berkson Laplace measurement error," Statistics & Probability Letters, Elsevier, vol. 166(C).
    • Handle: RePEc:eee:stapro:v:166:y:2020:i:c:s016771522030167x
    DOI: 10.1016/j.spl.2020.108864
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    References listed on IDEAS

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    1. Meister, Alexander, 2010. "Nonparametric Berkson regression under normal measurement error and bounded design," Journal of Multivariate Analysis, Elsevier, vol. 101(5), pages 1179-1189, May.
    2. Lu, Zhan-Qian, 1996. "Multivariate Locally Weighted Polynomial Fitting and Partial Derivative Estimation," Journal of Multivariate Analysis, Elsevier, vol. 59(2), pages 187-205, November.
    3. Magnus, J.R. & Neudecker, H., 1980. "The elimination matrix : Some lemmas and applications," Other publications TiSEM 0e3315d3-846c-4bc5-928e-f, Tilburg University, School of Economics and Management.
    4. Jingping Gu & Qi Li & Jui-Chung Yang, 2015. "Multivariate Local Polynomial Kernel Estimators: Leading Bias and Asymptotic Distribution," Econometric Reviews, Taylor & Francis Journals, vol. 34(6-10), pages 979-1010, December.
    5. Aurore Delaigle & Peter Hall & Peihua Qiu, 2006. "Nonparametric methods for solving the Berkson errors‐in‐variables problem," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(2), pages 201-220, April.
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