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

Jianqing Fan, Elias Masry

Research output: Contribution to journalArticlepeer-review

53 Scopus citations

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 ρ{variant}-mixing processes, when the error distribution function is either ordinarily smooth or super smooth.

Original languageEnglish (US)
Pages (from-to)237-271
Number of pages35
JournalJournal of Multivariate Analysis
Volume43
Issue number2
DOIs
StatePublished - Nov 1992
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Numerical Analysis
  • Statistics, Probability and Uncertainty

Keywords

  • asymptotic normality
  • deconvolution
  • errors-in-variables
  • mixing processes
  • multivariate regression

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