Estimation of nonparametric regression models with a mixture of Berkson and classical errors

Zanhua Yin*, Wei Gao, Man Lai TANG, Guo Liang Tian

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

We consider the estimation of nonparametric regression models with the explanatory variable being measured with Berkson errors or with a mixture of Berkson and classical errors. By constructing a compact operator, the regression function is the solution of an ill-posed inverse problem, and we propose an estimation procedure based on Tikhonov regularization. Under mild conditions, the convergence rate of proposed estimator is derived. The finite-sample properties of the estimator are investigated through simulation studies.

Original languageEnglish
Pages (from-to)1151-1162
Number of pages12
JournalStatistics and Probability Letters
Volume83
Issue number4
DOIs
Publication statusPublished - Apr 2013

Scopus Subject Areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

User-Defined Keywords

  • Berkson error
  • Classical error
  • Ill-posed problem
  • Non-parametric regression

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