Research Article Open Access

Boosted Regression Estimates of Spatial Data: Pointwise Inference

Marco Di Marzio and Charles C. Taylor

Abstract

In this study simple nonparametric techniques have been adopted to estimate the trend surface of the Swiss rainfall data. In particular we employed the Nadaraya-Watson smoother and in addition, an adapted-by boosting-version of it. Additionally, we have explored the use of the Nadaraya-Watson estimator for the construction of pointwise confidence intervals. Overall, boosting does seem to improve the estimate as much as previous examples and the results indicate that cross-validation can be successfully used for parameter selection on real datasets. In addition, our estimators compare favorably with most of the techniques previously used on this dataset.

Journal of Mathematics and Statistics
Volume 1 No. 4, 2005, 257-266

DOI: https://doi.org/10.3844/jmssp.2005.257.266

Submitted On: 15 January 2005 Published On: 31 December 2005

How to Cite: Di Marzio, M. & Taylor, C. C. (2005). Boosted Regression Estimates of Spatial Data: Pointwise Inference. Journal of Mathematics and Statistics, 1(4), 257-266. https://doi.org/10.3844/jmssp.2005.257.266

  • 3,015 Views
  • 2,443 Downloads
  • 1 Citations

Download

Keywords

  • Boosting
  • coverage rate
  • cross validation
  • machine learning
  • Nadaraya-Watson estimator