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On Semiparametric EV Models with Serially Correlated Errors in Both Regression Models and Mismeasured Covariates

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  • JINHONG YOU
  • HAIBO ZHOU

Abstract

. We consider inference for a semiparametric regression model where some covariates are measured with errors, and the errors in both the regression model and the mismeasured covariates are serially correlated. We propose a weighted estimating equations‐based estimator (WEEBE) for the regression coefficients. We show that the WEEBE is asymptotically more efficient than the estimators that neglect the serial correlations. This is an interesting new finding since earlier results in the statistical literature have shown that the weighted estimation is not as efficient as the unweighted estimation when the measurement errors and serially correlated errors of the regression models exist simultaneously (Biometrics, 49, 1993, 1262; Technometrics, 42, 2000, 137). The proposed WEEBE does not require undersmoothing the regressor functions in order to make it attain the root‐n consistency. Simulation studies show that the proposed estimator has nice finite sample properties. A real data set is used to illustrate the proposed method.

Suggested Citation

  • Jinhong You & Haibo Zhou, 2007. "On Semiparametric EV Models with Serially Correlated Errors in Both Regression Models and Mismeasured Covariates," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 34(2), pages 365-383, June.
    • Handle: RePEc:bla:scjsta:v:34:y:2007:i:2:p:365-383
    DOI: 10.1111/j.1467-9469.2006.00538.x
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    Cited by:

    1. Peixin Zhao & Liugen Xue, 2013. "Instrumental variable-based empirical likelihood inferences for varying-coefficient models with error-prone covariates," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(2), pages 380-396, February.
    2. Cui, Hengjian & Hu, Tao, 2011. "On nonlinear regression estimator with denoised variables," Computational Statistics & Data Analysis, Elsevier, vol. 55(2), pages 1137-1149, February.
    3. Amjad D. Al-Nasser, 2014. "Two steps generalized maximum entropy estimation procedure for fitting linear regression when both covariates are subject to error," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(8), pages 1708-1720, August.

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