Summary of Combinations Of Distributional Regression Algorithms with Application in Uncertainty Estimation Of Corrected Satellite Precipitation Products, by Georgia Papacharalampous et al.
Combinations of distributional regression algorithms with application in uncertainty estimation of corrected satellite precipitation products
by Georgia Papacharalampous, Hristos Tyralis, Nikolaos Doulamis, Anastasios Doulamis
First submitted to arxiv on: 29 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Applications (stat.AP); Methodology (stat.ME); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper introduces the concept of distributional regression for creating precipitation datasets with uncertainty estimates. This approach is shown to be more effective than quantile regression in modeling intermittency and extrapolating beyond training data, which is crucial for predicting extreme precipitation events. The authors formulate new ensemble learning methods that combine conditional zero-adjusted probability distributions estimated using generalized additive models (GAMLSS), spline-based GAMLSS, and distributional regression forests. These methods are evaluated on a large, multi-source precipitation dataset, showing that stacking is superior to individual methods at most quantile levels when measured with the quantile loss function. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to create precipitation datasets that includes uncertainty estimates. This helps with decision-making by giving a range of possible outcomes instead of just one answer. The authors compare different methods for creating these datasets and find that combining multiple approaches works best. They use a large dataset of precipitation information from different sources to test their ideas. |
Keywords
» Artificial intelligence » Loss function » Probability » Regression
