Estimation for biased partial linear single index models

Jun Lu, Xuehu Zhu, Lu Lin, Lixing Zhu*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

3 Citations (Scopus)


In this paper, we propose a novel method to consistently estimate, at the root-n rate, the coefficient parameters in a biased partial linear single-index model whose error term does not have zero conditional expectation. To achieve this purpose, we first transfer the model to a pro forma linear model and then introduce an artificial variable into a linear bias correction model. Based on the bias correction model, the parameters can then be consistently estimated by the linear least squares method. Both numerical studies and real data analyses are conducted to show the effectiveness of the proposed estimation procedure.

Original languageEnglish
Pages (from-to)1-13
Number of pages13
JournalComputational Statistics and Data Analysis
Publication statusPublished - Nov 2019

Scopus Subject Areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

User-Defined Keywords

  • Artificial variable construction
  • Bias-corrected model
  • Estimation consistency
  • Partial linear single index model


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