Abstract
A bootstrap method is developed to estimate the average squared error of a kernel based nonparametric regression estimator for a given bandwidth. This estimated average squared error is then minimised over the bandwidth to produce a regression estimate. Locally adaptive smoothing and simultaneous confidence bands may be obtained from this bootstrap method.
Original language | English |
---|---|
Pages (from-to) | 37-44 |
Number of pages | 8 |
Journal | Journal of Statistical Computation and Simulation |
Volume | 37 |
Issue number | 1-2 |
DOIs | |
Publication status | Published - 1 Oct 1990 |
Keywords
- bandwith selection
- bootstrap
- confidence bands
- Nonparametric regression
ASJC Scopus subject areas
- Statistics and Probability
- Modelling and Simulation
- Statistics, Probability and Uncertainty
- Applied Mathematics