|
Activity Number:
|
254
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Tuesday, August 8, 2006 : 8:30 AM to 10:20 AM
|
|
Sponsor:
|
Section on Nonparametric Statistics
|
| Abstract - #305451 |
|
Title:
|
Spline Single-Index Prediction Model
|
|
Author(s):
|
Li Wang*+ and Lijian Yang
|
|
Companies:
|
Michigan State University and Michigan State University
|
|
Address:
|
A400 Wells Hall, East Lansing, MI, 48824,
|
|
Keywords:
|
b spline ; geometric mixing ; knots ; nonparametric regression ; root-n rate ; strong consistency
|
|
Abstract:
|
For the past two decades, single-index model has proven to be an efficient way of coping with the high-dimensional problem in nonparametric regression. In this paper, we investigate the single-index prediction based on weakly dependent sample. The single index is identified by the best approximation to the multivariate prediction function of the response variable, regardless of whether the prediction function is a genuine single-index function. A polynomial spline estimator is proposed for the single-index coefficients and shown to be root-n consistent and asymptotically normal. An iterative program based on Newton-Raphson algorithm is developed. Simulation experiments have provided strong evidence that corroborates with the asymptotic theory. The algorithm is sufficiently fast for the user to analyze large data of high dimension within seconds.
|
