Variance estimation for semiparametric regression models by local averaging

Jingxin Zhao, Heng Peng, Tao Huang*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

6 Citations (Scopus)


Variance estimation is a fundamental problem in statistical modelling and plays an important role in the inferences after model selection and estimation. In this paper, we focus on several nonparametric and semiparametric models and propose a local averaging method for variance estimation based on the concept of partial consistency. The proposed method has the advantages of avoiding the estimation of the nonparametric function and reducing the computational cost and can be easily extended to more complex settings. Asymptotic normality is established for the proposed local averaging estimators. Numerical simulations and a real data analysis are presented to illustrate the finite sample performance of the proposed method.

Original languageEnglish
Pages (from-to)453-476
Number of pages24
Issue number2
Early online date20 Sept 2017
Publication statusPublished - Jun 2018

Scopus Subject Areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

User-Defined Keywords

  • Local averaging
  • Partial consistency
  • Semiparametric model
  • Variance estimation


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