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Checking the adequacy for a distortion errors-in-variables parametric regression model

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  • Zhang, Jun
  • Li, Gaorong
  • Feng, Zhenghui

Abstract

This paper studies tools for checking the validity of a parametric regression model, when both response and predictors are unobserved and distorted in a multiplicative fashion by an observed confounding variable. A residual based empirical process test statistic marked by proper functions of the regressors is proposed. We derive asymptotic distribution of the proposed empirical process test statistic: a centered Gaussian process under the null hypothesis and a non-centered one under local alternatives converging to the null hypothesis at parametric rates. We also suggest a bootstrap procedure to calculate critical values. Simulation studies are conducted to demonstrate the performance of the proposed test statistic and real examples are analyzed for illustrations.

Suggested Citation

  • Zhang, Jun & Li, Gaorong & Feng, Zhenghui, 2015. "Checking the adequacy for a distortion errors-in-variables parametric regression model," Computational Statistics & Data Analysis, Elsevier, vol. 83(C), pages 52-64.
    • Handle: RePEc:eee:csdana:v:83:y:2015:i:c:p:52-64
    DOI: 10.1016/j.csda.2014.09.018
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    References listed on IDEAS

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    1. Zhang, Jun & Zhu, Li-Xing & Liang, Hua, 2012. "Nonlinear models with measurement errors subject to single-indexed distortion," Journal of Multivariate Analysis, Elsevier, vol. 112(C), pages 1-23.
    2. Zhang, Jun & Feng, Zhenghui & Zhou, Bu, 2014. "A revisit to correlation analysis for distortion measurement error data," Journal of Multivariate Analysis, Elsevier, vol. 124(C), pages 116-129.
    3. Zhang, Jun & Gai, Yujie & Wu, Ping, 2013. "Estimation in linear regression models with measurement errors subject to single-indexed distortion," Computational Statistics & Data Analysis, Elsevier, vol. 59(C), pages 103-120.
    4. W. Stute & W. L. Xu & L. X. Zhu, 2008. "Model diagnosis for parametric regression in high-dimensional spaces," Biometrika, Biometrika Trust, vol. 95(2), pages 451-467.
    5. Sentürk, Damla & Nguyen, Danh V., 2009. "Asymptotic properties of covariate-adjusted regression with correlated errors," Statistics & Probability Letters, Elsevier, vol. 79(9), pages 1175-1180, May.
    6. Fan J. & Huang L-S., 2001. "Goodness-of-Fit Tests for Parametric Regression Models," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 640-652, June.
    7. Zhang, Jun & Zhu, Li-Ping & Zhu, Li-Xing, 2012. "On a dimension reduction regression with covariate adjustment," Journal of Multivariate Analysis, Elsevier, vol. 104(1), pages 39-55, February.
    8. D. Y. Lin & L. J. Wei & Z. Ying, 2002. "Model-Checking Techniques Based on Cumulative Residuals," Biometrics, The International Biometric Society, vol. 58(1), pages 1-12, March.
    9. Jun Zhang & Yao Yu & Li-Xing Zhu & Hua Liang, 2013. "Partial linear single index models with distortion measurement errors," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 65(2), pages 237-267, April.
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    Citations

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    Cited by:

    1. Zhihua Sun & Dongshan Luo & Xiaohua Zhou & Qingzhao Zhang, 2021. "Comparative studies on the adequacy check of parametric measurement error models with auxiliary variable," Statistical Papers, Springer, vol. 62(4), pages 1723-1751, August.
    2. Jun Zhang & Yiping Yang & Gaorong Li, 2020. "Logarithmic calibration for multiplicative distortion measurement errors regression models," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 74(4), pages 462-488, November.
    3. Xie, Chuanlong & Zhu, Lixing, 2019. "A goodness-of-fit test for variable-adjusted models," Computational Statistics & Data Analysis, Elsevier, vol. 138(C), pages 27-48.
    4. Dai, Shuang & Huang, Zhensheng, 2020. "Nonparametric inference for covariate-adjusted model," Statistics & Probability Letters, Elsevier, vol. 162(C).
    5. Zhao, Jingxin & Xie, Chuanlong, 2018. "A nonparametric test for covariate-adjusted models," Statistics & Probability Letters, Elsevier, vol. 133(C), pages 65-70.
    6. Feng Li & Lu Lin & Yiqiang Lu & Sanying Feng, 2021. "An adaptive estimation for covariate-adjusted nonparametric regression model," Statistical Papers, Springer, vol. 62(1), pages 93-115, February.
    7. Jun Zhang & Nanguang Zhou & Zipeng Sun & Gaorong Li & Zhenghong Wei, 2016. "Statistical inference on restricted partial linear regression models with partial distortion measurement errors," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 70(4), pages 304-331, November.

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