Optimal bandwidth selection for kernel regression using a fast grid search and a GPU

Chris Rohlfs, Mohamed Zahran

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE 31st International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages550-556
Number of pages7
ISBN (Electronic)9781538634080
DOIs
StatePublished - Jun 30 2017
Event31st IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2017 - Orlando, United States
Duration: May 29 2017Jun 2 2017

Publication series

NameProceedings - 2017 IEEE 31st International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2017

Conference

Conference31st IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2017
CountryUnited States
CityOrlando
Period5/29/176/2/17

Keywords

  • cross-validation
  • GPU
  • kernel
  • nonparametric
  • optimal bandwidth
  • regression

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Networks and Communications
  • Information Systems

Fingerprint Dive into the research topics of 'Optimal bandwidth selection for kernel regression using a fast grid search and a GPU'. Together they form a unique fingerprint.

  • Cite this

    Rohlfs, C., & Zahran, M. (2017). Optimal bandwidth selection for kernel regression using a fast grid search and a GPU. In Proceedings - 2017 IEEE 31st International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2017 (pp. 550-556). [7965092] (Proceedings - 2017 IEEE 31st International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IPDPSW.2017.130