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 language | English |
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Pages (from-to) | 970-991 |
Number of pages | 22 |
Journal | Journal of Multivariate Analysis |
Volume | 98 |
Issue number | 5 |
DOIs | |
Publication status | Published - 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