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Asymptotic normality of parametric part in partially linear models with measurement error in the nonparametric part

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  • Liang, Hua

Abstract

We consider the partially linear model relating a response Y to predictors (X,T) with mean function XT Ø + g(T) when the T's are measured with additive error. We derive an estimator of Ø by modification local-likelihood method. The resulting estimator of Ø is shown to be asymptotically normal.We consider the partially linear model relating a response Y to predictors (X,T) with mean function XT Ø + g(T) when the T's are measured with additive error. We derive an estimator of Ø by modification local-likelihood method. The resulting estimator of Ø is shown to be asymptotically normal.

Suggested Citation

  • Liang, Hua, 1997. "Asymptotic normality of parametric part in partially linear models with measurement error in the nonparametric part," SFB 373 Discussion Papers 1997,46, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    • Handle: RePEc:zbw:sfb373:199746
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    References listed on IDEAS

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    1. Liang, Hua & Härdle, Wolfgang & Carroll, Raymond J., 1997. "Large sample theory in a semiparametric partially linear errors-in-variables models," SFB 373 Discussion Papers 1997,27, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    2. Liang, Hua & Härdle, Wolfgang, 1997. "Asymptotic normality of parametric part in partial linear heteroscedastic regression models," SFB 373 Discussion Papers 1997,33, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
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