Reliable Prediction Intervals for Local Linear Regression
This work addresses the need for more reliable uncertainty quantification in regression models, which is incremental but important for applications in statistics and machine learning.
The paper tackled the problem of estimating reliable prediction intervals for local linear regression by introducing Bounded Oscillation Prediction Intervals (BOPI) and a new comparison measure called Equivalent Gaussian Standard Deviation (EGSD). The results showed that BOPI generally outperformed other methods on benchmark datasets in terms of coverage probability and interval size.
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.