On kernel method for sliced average variance estimation

Li Ping Zhu, Lixing ZHU*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

34 Citations (Scopus)

Abstract

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
Volume98
Issue number5
DOIs
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

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