The Annals of Statistics

Estimation in a semiparametric partially linear errors-in-variables model

Hua Liang, Wolfgang Härdle, and Raymond J. Carroll

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We consider the partially linear model relating a response $Y$ to predictors ($X, T$) with mean function $X^{\top}\beta + g(T)$ when the $X$’s are measured with additive error. The semiparametric likelihood estimate of Severini and Staniswalis leads to biased estimates of both the parameter $\beta$ and the function $g(\cdot)$ when measurement error is ignored. We derive a simple modification of their estimator which is a semiparametric version of the usual parametric correction for attenuation. The resulting estimator of $\beta$ is shown to be consistent and its asymptotic distribution theory is derived. Consistent standard error estimates using sandwich-type ideas are also developed.

Article information

Ann. Statist., Volume 27, Number 5 (1999), 1519-1535.

First available in Project Euclid: 23 September 2004

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Zentralblatt MATH identifier

Primary: 62J99: None of the above, but in this section 62H12: Estimation 62E25 62F10: Point estimation
Secondary: 62H25: Factor analysis and principal components; correspondence analysis 62F10: Point estimation 62F12: Asymptotic properties of estimators 60F05: Central limit and other weak theorems

Errors-in-variables measurement error nonparametric likelihood orthogonal regression partially linear model semiparametric models structural relations


Liang, Hua; Härdle, Wolfgang; Carroll, Raymond J. Estimation in a semiparametric partially linear errors-in-variables model. Ann. Statist. 27 (1999), no. 5, 1519--1535. doi:10.1214/aos/1017939140.

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