A Semi-parametric Regression Model with Errors in Variables

Authors: Zhu L.¹; Cui H.²

Source: Scandinavian Journal of Statistics, Volume 30, Number 2, June 2003 , pp. 429-442(14)

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Abstract:

In this paper, we consider a partial linear regression model with measurement errors in possibly all the variables. We use a method of moments and deconvolution to construct a new class of parametric estimators together with a non-parametric kernel estimator. Strong convergence, optimal rate of weak convergence and asymptotic normality of the estimators are investigated.

Keywords: asymptotic normality; convergence rate; deconvolution kernel estimator; errors in variables; semi-parametric regression

Document Type: Research article

DOI: http://dx.doi.org/10.1111/1467-9469.00340

Affiliations: 1: The University of Hong Kong and Chinese Academy of Sciences 2: Beijing Normal University

Publication date: 2003-06-01