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A note on kernel assisted estimators in missing covariate regression

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  • Wang, Suojin
  • Wang, C. Y.
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    Abstract

    We investigate the asymptotic relationships among three kernel assisted semiparametric estimators in regression analysis when some covariates are missing or measured with error. Smoothing techniques are employed in estimating the selection probabilities and the conditionally expected scores, a step which is required to obtain the estimators of interest. The asymptotic distributional properties of these estimators are derived and their asymptotic equivalence is shown. Some important differences are also noted. Furthermore, the asymptotic efficiency of the estimators relative to the usual maximum likelihood estimator is obtained.

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    Bibliographic Info

    Article provided by Elsevier in its journal Statistics & Probability Letters.

    Volume (Year): 55 (2001)
    Issue (Month): 4 (December)
    Pages: 439-449

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    Handle: RePEc:eee:stapro:v:55:y:2001:i:4:p:439-449

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    Keywords: Errors-in-variables Kernel estimation Linear regression Logistic regression Mean-score method Semiparametric methods Weighted estimating equation;

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    1. Newey, Whitney K., 1994. "Kernel Estimation of Partial Means and a General Variance Estimator," Econometric Theory, Cambridge University Press, vol. 10(02), pages 1-21, June.
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    Cited by:
    1. Creemers, An & Aerts, Marc & Hens, Niel & Molenberghs, Geert, 2012. "A nonparametric approach to weighted estimating equations for regression analysis with missing covariates," Computational Statistics & Data Analysis, Elsevier, vol. 56(1), pages 100-113, January.

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