Summary of Distributional Refinement Network: Distributional Forecasting Via Deep Learning, by Benjamin Avanzi et al.
Distributional Refinement Network: Distributional Forecasting via Deep Learning
by Benjamin Avanzi, Eric Dong, Patrick J. Laub, Bernard Wong
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Risk Management (q-fin.RM); Methodology (stat.ME)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Distributional Refinement Network (DRN) addresses challenges in classic (distributional) regression approaches like Generalized Linear Models (GLMs) by combining an inherently interpretable baseline model with a flexible neural network. The DRN refines the entire baseline distribution, capturing varying effects of features across all quantiles and improving predictive performance while maintaining adequate interpretability. The approach is inspired by the Combined Actuarial Neural Network (CANN) and demonstrates superior distributional forecasting capacity using both synthetic and real-world data. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new model that combines two different approaches to improve actuarial modelling. It uses a baseline model like GLMs, but also adds a neural network to make the predictions more accurate. This combined approach is called the Distributional Refinement Network (DRN). The DRN does a better job of predicting how often certain events will happen than other models do. This could be useful for making decisions about insurance and risk management. |
Keywords
» Artificial intelligence » Neural network » Regression