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Jackknife empirical likelihood of error variance in partially linear varying-coefficient errors-in-variables models

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  • Ai-Ai Liu

    (Tongji University)

  • Han-Ying Liang

    (Tongji University)

Abstract

For the partially linear varying-coefficient model when the parametric covariates are measured with additive errors, the estimator of the error variance is defined based on residuals of the model. At the same time, we construct Jackknife estimator as well as Jackknife empirical likelihood statistic of the error variance. Under both the response variables and their associated covariates form a stationary $$\alpha $$ α -mixing sequence, we prove that the proposed estimators and Jackknife empirical likelihood statistic are asymptotic normality and asymptotic $$\chi ^2$$ χ 2 distribution, respectively. Numerical simulations are carried out to assess the performance of the proposed method.

Suggested Citation

  • Ai-Ai Liu & Han-Ying Liang, 2017. "Jackknife empirical likelihood of error variance in partially linear varying-coefficient errors-in-variables models," Statistical Papers, Springer, vol. 58(1), pages 95-122, March.
    • Handle: RePEc:spr:stpapr:v:58:y:2017:i:1:d:10.1007_s00362-015-0689-8
    DOI: 10.1007/s00362-015-0689-8
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    References listed on IDEAS

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    Cited by:

    1. Yongcheng Qi, 2018. "Jackknife Empirical Likelihood Methods," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 7(2), pages 20-22, June.

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