TY - GEN
T1 - Optimal bandwidth selection for kernel regression using a fast grid search and a GPU
AU - Rohlfs, Chris
AU - Zahran, Mohamed
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/30
Y1 - 2017/6/30
N2 - 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.
AB - 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.
KW - GPU
KW - cross-validation
KW - kernel
KW - nonparametric
KW - optimal bandwidth
KW - regression
UR - http://www.scopus.com/inward/record.url?scp=85028040203&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85028040203&partnerID=8YFLogxK
U2 - 10.1109/IPDPSW.2017.130
DO - 10.1109/IPDPSW.2017.130
M3 - Conference contribution
AN - SCOPUS:85028040203
T3 - Proceedings - 2017 IEEE 31st International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2017
SP - 550
EP - 556
BT - Proceedings - 2017 IEEE 31st International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 31st IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2017
Y2 - 29 May 2017 through 2 June 2017
ER -