Linton, Oliver and Jacho-Chávez, David (2010) On internally corrected and symmetrized kernel estimators for nonparametric regression. TEST, 19 (1). pp. 166-186. ISSN 1133-0686
Full text not available from this repository.Abstract
We investigate the properties of a kernel-type multivariate regression estimator first proposed by Mack and Müller (Sankhya 51:59–72, 1989) in the context of univariate derivative estimation. Our proposed procedure, unlike theirs, assumes that bandwidths of the same order are used throughout; this gives more realistic asymptotics for the estimation of the function itself but makes the asymptotic distribution more complicated. We also propose a modification of this estimator that has a symmetric smoother matrix, which makes it admissible, unlike some other common regression estimators. We compare the performance of the estimators in a Monte Carlo experiment. Multivariate regression - Smoothing matrix - Symmetry
| Item Type: | Article |
|---|---|
| Official URL: | http://www.springer.com/statistics/journal/11749 |
| Additional Information: | © 2010 Springer |
| Divisions: | Financial Markets Group STICERD Economics |
| Subjects: | Q Science > QA Mathematics |
| Date Deposited: | 16 Jul 2010 14:29 |
| Last Modified: | 03 Apr 2024 16:45 |
| URI: | http://eprints.lse.ac.uk/id/eprint/28619 |
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