On kernel method for sliced average variance estimation

Li-Ping Zhu, Li-Xing Zhu*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

45 Citations (Scopus)


In this paper, we use the kernel method to estimate sliced average variance estimation (SAVE) and prove that this estimator is both asymptotically normal and root n consistent. We use this kernel estimator to provide more insight about the differences between slicing estimation and other sophisticated local smoothing methods. Finally, we suggest a Bayes information criterion (BIC) to estimate the dimensionality of SAVE. Examples and real data are presented for illustrating our method.

Original languageEnglish
Pages (from-to)970-991
Number of pages22
JournalJournal of Multivariate Analysis
Issue number5
Publication statusPublished - May 2007

Scopus Subject Areas

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

User-Defined Keywords

  • Asymptotic normality
  • Bandwidth selection
  • Dimension reduction
  • Kernel estimation
  • Sliced average variance estimation
  • Sliced inverse regression
  • Slicing estimation


Dive into the research topics of 'On kernel method for sliced average variance estimation'. Together they form a unique fingerprint.

Cite this