Nagging Predictors

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ID: 115041
2020
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Abstract
We define the nagging predictor, which, instead of using bootstrapping to produce a series of i.i.d. predictors, exploits the randomness of neural network calibrations to provide a more stable and accurate predictor than is available from a single neural network run. Convergence results for the family of Tweedie’s compound Poisson models, which are usually used for general insurance pricing, are provided. In the context of a French motor third-party liability insurance example, the nagging predictor achieves stability at portfolio level after about 20 runs. At an insurance policy level, we show that for some policies up to 400 neural network runs are required to achieve stability. Since working with 400 neural networks is impractical, we calibrate two meta models to the nagging predictor, one unweighted, and one using the coefficient of variation of the nagging predictor as a weight, finding that these latter meta networks can approximate the nagging predictor well, only with a small loss of accuracy.
Reference Key
wüthrich2020risksnagging Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Ronald Richman,Mario V. Wüthrich;Ronald Richman;Mario V. Wüthrich;
Journal risks
Year 2020
DOI
10.3390/risks8030083
URL
https://sciprofiles.com/publication/view/c14be8b72c8333649f42e307cf47a921
Keywords
regression modeling neural networks bagging bootstrap aggregation network aggregation insurance pricing

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