Fast Implementations of Nonparametric Curve Estimators
- 1 March 1994
- journal article
- research article
- Published by Taylor & Francis in Journal of Computational and Graphical Statistics
- Vol. 3 (1) , 35-56
- https://doi.org/10.1080/10618600.1994.10474629
Abstract
Recent proposals for implementation of kernel-based nonparametric curve estimators are seen to be faster than naive direct implementations by factors up into the hundreds. The main ideas behind the two different approaches are made clear. Careful speed comparisons in a variety of settings and using a variety of machines and software are done. Various issues on computational accuracy and stability are also discussed. Our speed tests show that the fast methods are as fast or somewhat faster than methods traditionally considered very fast, such as LOWESS and smoothing splines.Keywords
This publication has 11 references indexed in Scilit:
- [Local Regression: Automatic Kernel Carpentry]: CommentStatistical Science, 1993
- Local Regression: Automatic Kernel CarpentryStatistical Science, 1993
- Design-adaptive Nonparametric RegressionJournal of the American Statistical Association, 1992
- Applied Nonparametric RegressionPublished by Cambridge University Press (CUP) ,1990
- VARIABLE KERNEL DENSITY ESTIMATES AND VARIABLE KERNEL DENSITY ESTIMATESAustralian Journal of Statistics, 1990
- Discretized and Interpolated Kernel Density EstimatesJournal of the American Statistical Association, 1989
- Linear Smoothers and Additive ModelsThe Annals of Statistics, 1989
- Canonical kernels for density estimationStatistics & Probability Letters, 1988
- A Variable Span SmootherPublished by Defense Technical Information Center (DTIC) ,1984
- Robust Locally Weighted Regression and Smoothing ScatterplotsJournal of the American Statistical Association, 1979