New statistical method improves predictions for skewed, unbalanced data
Researchers have developed a faster, more accurate approach to quantile regression—a statistical technique widely used in finance, healthcare, and supply chain management to forecast extreme outcomes. The method handles real-world data patterns better than current industry standards, potentially improving risk assessments and decision-making across sectors reliant on edge-case predictions.
Originaltitel: Quantile regression based on the skewed exponential power distribution
<p>Bayesian quantile regression generally relies on the asymmetric Laplace distribution (ALD) as the error distribution. We consider methods for Lp-quantile regression based on the skewed exponential power distribution (SEPD). Both Bayesian and frequentist estimation procedures are outlined and compared with previous work based on the SEPD. We find that our proposed methods greatly outperform a previous method in terms of quantile estimation. Further, compared with standard quantile regression, we find that our proposed methods generally perform better in terms of root mean square error (RMSE). Empirical evidence of the statistical properties of the proposed models is provided through a simulation study. Further, a real data application illustrates their performance.</p>