@inproceedings{b6865346a1144cdf9f86e5804c967ef6,
title = "Optimal bandwidth selection for kernel regression using a fast grid search and a GPU",
abstract = "This study presents a new algorithm and corresponding statistical package for estimating optimal bandwidth for a nonparametric kernel regression. Kernel regression is widely used in Economics, Statistics, and other fields. The formula for the optimal 'bandwidth,' or smoothing parameter, is well-known. In practice, however, the computational demands of estimating the optimal bandwidth have historically been prohibitively high. Consequently, researchers typically select bandwidths for kernel regressions using ad hoc rules of thumb. This paper exploits the Single Program Multiple Data (SPMD) parallelism inherent in optimal bandwidth calculation to develop a method for computing optimal bandwidth on a GPU. Using randomly generated datasets of different sizes, this approach is shown to reduce the run time by as much as a factor of seven.",
keywords = "cross-validation, GPU, kernel, nonparametric, optimal bandwidth, regression",
author = "Chris Rohlfs and Mohamed Zahran",
year = "2017",
month = jun,
day = "30",
doi = "10.1109/IPDPSW.2017.130",
language = "English (US)",
series = "Proceedings - 2017 IEEE 31st International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "550--556",
booktitle = "Proceedings - 2017 IEEE 31st International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2017",
note = "31st IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2017 ; Conference date: 29-05-2017 Through 02-06-2017",
}