Inference on the primary parameter of interest with the aid of dimension reduction estimation

Lexin Li*, Liping Zhu, Lixing ZHU

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

13 Citations (Scopus)


As high dimensional data become routinely available in applied sciences, sufficient dimension reduction has been widely employed and its research has received considerable attention. However, with the majority of sufficient dimension reduction methodology focusing on the dimension reduction step, complete analysis and inference after dimension reduction have yet to receive much attention. We couple the strategy of sufficient dimension reduction with a flexible semiparametric model. We concentrate on inference with respect to the primary variables of interest, and we employ sufficient dimension reduction to bring down the dimension of the regression effectively. Extensive simulations demonstrate the efficacy of the method proposed, and a real data analysis is presented for illustration.

Original languageEnglish
Pages (from-to)59-80
Number of pages22
JournalJournal of the Royal Statistical Society. Series B: Statistical Methodology
Issue number1
Publication statusPublished - Jan 2011

Scopus Subject Areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

User-Defined Keywords

  • Central partial mean subspace
  • Dimension reduction
  • Partial ordinary least squares
  • Partially linear single-index model


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