Multivariate regression estimation with errors-in-variables: Asymptotic normality for mixing processes

Jianqing Fan and Elias Masry

Journal of Multivariate Analysis, 1992, vol. 43, issue 2, 237-271

Abstract: Errors-in-variables regression is the study of the association between covariates and responses where covariates are observed with errors. In this paper, we consider the estimation of multivariate regression functions for dependent data with errors in covariates. Nonparametric deconvolution technique is used to account for errors-in-variables. The asymptotic behavior of regression estimators depends on the smoothness of the error distributions, which are characterized as either ordinarily smooth or super smooth. Asymptotic normality is established for both strongly mixing and [varrho]-mixing processes, when the error distribution function is either ordinarily smooth or super smooth.

Keywords: asymptotic; normality; deconvolution; errors-in-variables; multivariate; regression; mixing; processes (search for similar items in EconPapers)
Date: 1992
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Citations: View citations in EconPapers (33)

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