Reliable Prediction Intervals for Local Linear Regression
Methodology
2016-07-13 v5 Machine Learning
Abstract
This paper introduces two methods for estimating reliable prediction intervals for local linear least-squares regressions, named Bounded Oscillation Prediction Intervals (BOPI). It also proposes a new measure for comparing interval prediction models named Equivalent Gaussian Standard Deviation (EGSD). The experimental results compare BOPI to other methods using coverage probability, Mean Interval Size and the introduced EGSD measure. The results were generally in favor of the BOPI on considered benchmark regression datasets. It also, reports simulation studies validating the BOPI method's reliability.
Keywords
Cite
@article{arxiv.1603.05587,
title = {Reliable Prediction Intervals for Local Linear Regression},
author = {Mohammad Ghasemi Hamed and Masoud Ebadi Kivaj},
journal= {arXiv preprint arXiv:1603.05587},
year = {2016}
}
Comments
40 pages,11 figures, 10 tables and 1 algorithm. arXiv admin note: text overlap with arXiv:1402.5874