Abstract
To explore nonlinear structures hidden in high-dimensional data and to estimate the effective dimension reduction directions in multivariate nonparametric regression, Li and Duan proposed the sliced inverse regression (SIR) method which is simple to use. In this paper, the asymptotic properties of the kernel estimate of sliced inverse regression are investigated. It turns out that regardless of the kernel function, the asymptotic distribution remains the same for a wide range of smoothing parameters.
| Original language | English |
|---|---|
| Pages (from-to) | 1053-1068 |
| Number of pages | 16 |
| Journal | Annals of Statistics |
| Volume | 24 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - Jun 1996 |
Scopus Subject Areas
- Statistics and Probability
- Statistics, Probability and Uncertainty
User-Defined Keywords
- Data structure
- Dimension reduction
- Kernel estimation
- Nonparametric regression
- Sliced inverse regression